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Background: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports. The target domain for this study was case narratives from the United Kingdom's Yellow Card scheme, which collects and monitors information on suspected side effects to medicines and vaccines. Methods: We finetuned BERT – a transformer-based neural network – for recognising names in case narratives. Training data consisted of newly annotated records from the Yellow Card data and of the i2b2 2014 deidentification challenge. Because the Yellow Card data contained few names, we used predictive models to select narratives for training. Performance was evaluated on a separate set of annotated narratives from the Yellow Card scheme. In-depth review determined whether (parts of) person names missed by the de-identification method could enable re-identification of the individual, and whether de-identification reduced the clinical utility of narratives by collaterally masking relevant information. Results: Recall on held-out Yellow Card data was 87% (155/179) at a precision of 55% (155/282) and a false-positive rate of 0.05% (127/ 263,451). Considering tokens longer than three characters separately, recall was 94% (102/108) and precision 58% (102/175). For 13 of the 5,042 narratives in Yellow Card test data (71 with person names), the method failed to flag at least one name token. According to in-depth review, the leaked information could enable direct identification for one narrative and indirect identification for two narratives. Clinically relevant information was removed in less than 1% of the 5,042 processed narratives; 97% of the narratives were completely untouched. Conclusions: Automated redaction of names in free-text narratives of adverse event reports can achieve sufficient recall including shorter tokens like patient initials. In-depth review shows that the rare leaks that occur tend not to compromise patient confidentiality. Precision and false positive rates are acceptable with almost all clinically relevant information retained. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Methods Results Conclusion The application of large language models (LLMs) in medicine has received increasing attention, showing significant potential in teaching, research, and clinical practice, especially in knowledge extraction, management, and understanding. However, the use of LLMs in Traditional Chinese Medicine (TCM) has not been thoroughly studied. This study aims to provide a comprehensive overview of the status and challenges of LLM applications in TCM.A systematic search of five electronic databases and Google Scholar was conducted between November 2022 and April 2024, using the Arksey and O'Malley five‐stage framework to identify relevant studies. Data from eligible studies were comprehensively extracted and organized to describe LLM applications in TCM and assess their performance accuracy.A total of 29 studies were identified: 24 peer‐reviewed articles, 1 review, and 4 preprints. Two core application areas were found: the extraction, management, and understanding of TCM knowledge, and assisted diagnosis and treatment. LLMs developed specifically for TCM achieved 70% accuracy in the TCM Practitioner Exam, while general‐purpose Chinese LLMs achieved 60% accuracy. Common international LLMs did not pass the exam. Models like EpidemicCHAT and MedChatZH, trained on customized TCM corpora, outperformed general LLMs in TCM consultation.Despite their potential, LLMs in TCM face challenges such as data quality and security issues, the specificity and complexity of TCM data, and the nonquantitative nature of TCM diagnosis and treatment. Future efforts should focus on interdisciplinary talent cultivation, enhanced data standardization and protection, and exploring LLM potential in multimodal interaction and intelligent diagnosis and treatment. [ABSTRACT FROM AUTHOR]

ABSTRACT Linguistic landscape (LL), functioning as a form of health communication, offers a lens through which to examine whether the visual representation of languages in medical spaces ensures accessibility for all and communicates spatial justice. Recent studies on LL have primarily concentrated on elucidating LL as a cognitive and affective indicator of public space, with less attention to how sign‐readers affectively perceive and engage with public signage in medical and migrant contexts. Exploring sign‐readers’ affective responses to the LL can help us understand whether the spatial resources indicated by that LL meet their practical and psychological needs. This empirical study contributes to LL research by examining affective responses to the LL among immigrant patients in a Shanghai international hospital, illuminating the interactions between LL and spatial justice in a medical context. Based on 260 signs collected through photography and interviews with eight international patients, we aimed to explore their feelings and thoughts about the LL at the moment of their experience of it. After identifying various affective responses triggered by the LL, we found that the use of English in the LL projects the hospital's espoused values of “care” and “quality”, while the absence of languages such as Korean and Japanese constrains linguistic equality and spatial justice, triggering anxiety among those who speak neither English nor Mandarin. Future planning of the LL in Shanghai's international communities might be informed by a multilingual awareness to promote the subjective well‐being of transnational immigrants and to improve the epistemic diversity and inclusivity of urban spaces. [ABSTRACT FROM AUTHOR]

Background/Objectives: Large language models (LLMs) show promise in healthcare but face challenges with hallucinations, particularly in rapidly evolving fields like diabetes management. Traditional LLM updating methods are resource-intensive, necessitating new approaches for delivering reliable, current medical information. This study aimed to develop and evaluate a novel retrieval system to enhance LLM reliability in diabetes management across different languages and guidelines. Methods: We developed a dual retrieval-augmented generation (RAG) system integrating both Korean Diabetes Association and American Diabetes Association 2023 guidelines. The system employed dense retrieval with 11 embedding models (including OpenAI, Upstage, and multilingual models) and sparse retrieval using BM25 algorithm with language-specific tokenizers. Performance was evaluated across different top-k values, leading to optimized ensemble retrievers for each guideline. Results: For dense retrievers, Upstage's Solar Embedding-1-large and OpenAI's text-embedding-3-large showed superior performance for Korean and English guidelines, respectively. Multilingual models outperformed language-specific models in both cases. For sparse retrievers, the ko_kiwi tokenizer demonstrated superior performance for Korean text, while both ko_kiwi and porter_stemmer showed comparable effectiveness for English text. The ensemble retrievers, combining optimal dense and sparse configurations, demonstrated enhanced coverage while maintaining precision. Conclusions: This study presents an effective dual RAG system that enhances LLM reliability in diabetes management across different languages. The successful implementation with both Korean and American guidelines demonstrates the system's cross-regional capability, laying a foundation for more trustworthy AI-assisted healthcare applications. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to enhance the efficiency and effectiveness of Chinese Clinical Named Entity Recognition by improving the Bert-BiLSTM-CRF model through the adoption of the RoBERTa pre-training model. Design/methodology/approach: A deep learning approach is employed, combining the RoBERTa pre-training model, Bi-directional Long Short-Term Memory (BiLSTM) network, and Conditional Random Field (CRF) model to form a Named Entity Recognition (NER) model. The model takes the pre-training model trained by the deep network model as input, mitigates the scarcity of annotated datasets, leverages the strong advantage of BiLSTM in learning the context information of words, and combines the CRF model to infer the ability of labels through global information. Findings: The RoBERTa-BiLSTM-CRF model has shown satisfactory results in the experiment. It enhances the reasoning ability between characters, allows the model to fully learn the feature information of the text, and improves the model performance to a certain extent. Originality/value: This paper proposes a RoBERTa medical named entity recognition model for the scarcity of annotated data in medical named entity recognition tasks and BERT's inability to obtain word-level information. The model is not limited to medical entity recognition tasks and shows potential for other medical natural language processing tasks, considering data enhancement, data optimization, and domain transfer on the model to improve model performance and generalization capabilities. [ABSTRACT FROM AUTHOR]

Background: Owing to the rapid growth in the popularity of Large Language Models (LLMs), various performance evaluation studies have been conducted to confirm their applicability in the medical field. However, there is still no clear framework for evaluating LLMs. Objective: This study reviews studies on LLM evaluations in the medical field and analyzes the research methods used in these studies. It aims to provide a reference for future researchers designing LLM studies. Methods & materials: We conducted a scoping review of three databases (PubMed, Embase, and MEDLINE) to identify LLM-related articles published between January 1, 2023, and September 30, 2023. We analyzed the types of methods, number of questions (queries), evaluators, repeat measurements, additional analysis methods, use of prompt engineering, and metrics other than accuracy. Results: A total of 142 articles met the inclusion criteria. LLM evaluation was primarily categorized as either providing test examinations (n = 53, 37.3%) or being evaluated by a medical professional (n = 80, 56.3%), with some hybrid cases (n = 5, 3.5%) or a combination of the two (n = 4, 2.8%). Most studies had 100 or fewer questions (n = 18, 29.0%), 15 (24.2%) performed repeated measurements, 18 (29.0%) performed additional analyses, and 8 (12.9%) used prompt engineering. For medical assessment, most studies used 50 or fewer queries (n = 54, 64.3%), had two evaluators (n = 43, 48.3%), and 14 (14.7%) used prompt engineering. Conclusions: More research is required regarding the application of LLMs in healthcare. Although previous studies have evaluated performance, future studies will likely focus on improving performance. A well-structured methodology is required for these studies to be conducted systematically. [ABSTRACT FROM AUTHOR]

The advent of the Transformer has significantly altered the course of research in Natural Language Processing (NLP) within the domain of deep learning, making Transformer-based studies the mainstream in subsequent NLP research. There has also been considerable advancement in domain-specific NLP research, including the development of specialized language models for medical. These medical-specific language models were trained on medical data and demonstrated high performance. While these studies have treated the medical field as a single domain, in reality, medical is divided into multiple departments, each requiring a high level of expertise and treated as a unique domain. Recognizing this, our research focuses on constructing a model specialized for cardiology within the medical sector. Our study encompasses the creation of open-source datasets, training, and model evaluation in this nuanced domain. [ABSTRACT FROM AUTHOR]

Medical large language models are being introduced to the public in collaboration with governments, medical institutions, and artificial intelligence (AI) researchers. However, a crucial question remains: Will patients follow the medical advice provided by AI doctors? The lack of user research makes it difficult to provide definitive answers. Based on the clinician-patient communication pathway model, this study conducted a factorial experiment with a 2 (medical provider, AI vs. human) × 2 (information support, low vs. high) × 2 (response latency, slow vs. fast) between-subjects design (n = 535). The results showed that participants exhibited significantly lower adherence to AI doctors’ advice than to human doctors. In addition, the interaction effect suggested that, under the slow-response latency condition, subjects perceived greater health benefits and patient-centeredness from human doctors, while the opposite was observed for AI doctors. [ABSTRACT FROM AUTHOR]

The present study analyzes metaphorical expressions related to hysterectomy in a diverse corpus comprising scientific texts, Reddit forums, and patient-authored articles and blogs. The research aims to understand how hysterectomy is discursively constructed and experienced by women. The corpus reveals a range of metaphors from clinical descriptions to personal and creative expressions. The analysis shows that metaphors not only reflect but also influence women’s perceptions of their bodies and the procedure. The study identifies metaphors including the uterus as a ‘tenant’ or ‘kidnapper,’ and the procedure as an ‘eviction’ or a ‘robbery’, highlighting the emotional and psychological aspects of hysterectomy. Limitations include the corpus size, lack of metadata to establish more correlations, and its Western-centric perspective, suggesting the need for more inclusive research. The study contributes to the discourse on women’s health, emphasizing the role of language in shaping medical experiences. It underscores the need for understanding metaphorical language to improve communication and support for women undergoing hysterectomy. [ABSTRACT FROM AUTHOR]

Medical language is characterized by its veracity, precision, and clarity (Navarro, 2009). However, due to the different communicative situations and contexts in which it is used, it is one of the special languages with more terminological variation (Bowker and Hawkins, 2006). From the point of view of terminology work, in any of its applications: language planning, standardization or translation, the first steps consist of structuring the subject area and accurately define the conceptual field (Cabré, 2005; ISO, 2022; Wright, 1997), and variation is usually an obstacle during this stage. This paper presents the findings of a study for the elaboration of terminological resources on Women’s Health from a corpus of specialized academic articles in English. Preliminary results reveal a lack of uniformity in the identification of the most representative lexical units regarding issues that specifically affect Women’s Health. This analysis offers a typology of denominative variation in the subject field of Women’s Health in academic journals in English prior to initiate the delimitation of the conceptual field in Spanish and standardize terminology equivalence in order to ensure efficient communication. [ABSTRACT FROM AUTHOR]

Several multimedia tools can be used to improve patients' understanding of urological conditions and procedures, such as simulation and models. Use of these aids for preoperative discussion enhances knowledge and patient satisfaction, which can lead to more realistic patient expectations and better informed consent. Discussions surrounding urological diagnoses and planned procedures can be challenging, and patients might experience difficulty in understanding the medical language, even when shown radiological imaging or drawings. With the introduction of virtual reality and simulation, informed consent could be enhanced by audiovisual content and interactive platforms. Our aim was to assess the role of enhanced consent in the field of urology. A systematic review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, using informed consent, simulation, and virtual reality in urology as the search terms. All original articles were screened. Thirteen original studies were included in the review. The overall quality of these studies was deemed good according to the Newcastle-Ottawa Scale. The studies analysed the application of different modalities for enhanced consent: 3D printed or digital models, audio visual multimedia contents, virtual simulation of procedures and interactive navigable apps. Published studies agreed upon a significantly improved effect on patient understanding of the diagnosis, including basic anatomical details, and surgery-related issues such as the aim, steps and the risks connected to the planned intervention. Patient satisfaction was unanimously reported as improved as a result of enhanced consent. Simulation and multimedia tools are extremely valuable for improving patients' understanding of and satisfaction with urological procedures. Widespread application of enhanced consent would represent a milestone for patient-urologist communication. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians' expertise. Patients and Methods: We developed a conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making. This framework focuses on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting, to help clinicians better interpret complex medical data and improve diagnosis and treatment planning. Results: Several AI methodologies were adapted to enhance clinical decision-making. First, backpropagation allows clinicians to refine initial assessments by revisiting them as new data emerges, improving diagnostic accuracy over time. Second, quantization helps break down complex medical problems into manageable components, enabling clinicians to prioritize critical elements of care. Finally, avoiding overfitting encourages clinicians to balance rare diagnoses with more common explanations, reducing the risk of diagnostic errors and unnecessary complexity. Conclusion: The integration of AI-driven hybrid intelligence has the potential to enhance clinical decision-making. By adapting AI methodologies, clinicians can enhance their ability to analyze data, prioritize treatments, and make more accurate diagnoses while preserving the essential human aspect of health care. This framework highlights the importance of combining AI's strengths with clinicians' expertise for more effective and balanced decision-making in clinical practice. This perspective highlights the value of hybrid intelligence in achieving more balanced, effective, and patient-centered decision-making in health care. [ABSTRACT FROM AUTHOR]

Acknowledgment sections are a rich but underused resource for understanding how language is used for social purposes (such as expressing gratitude and communicating social relations networks), and how conventions and patterns emerge in this process. This paper presents a usage-based case study combining qualitative and quantitative methods for analyzing a dataset of >300 acknowledgment sections from medical dissertations written in German. In our quantitative analysis, we gauge keywords and key n-grams and assess the relative position of recurrent words in each text. Our analysis shows that this text type has developed clear conventions, with acknowledgments in the professional domain being followed by a usually smaller set of expressions of gratitude associated with the private domain. In addition, our quantitative analysis suggests recurrent patterns that can be linked to specific socio-pragmatic functions. For instance, an analysis of n-grams attested in text segments associated with the professional vs. the private domain shows some differences with regard to the typical patterns chosen in those segments. Our analysis also raises a number of future research questions, thus showing that acknowledgment sections are a highly interesting object of study that deserve to be investigated in more detail. [ABSTRACT FROM AUTHOR]

AbstractPhenomenon. The language of medicine (i.e., biomedical discourse) represents queerness as pathological, yet it is this same discourse medical education researchers use to resist that narrative. To be truly inclusive, we must examine and disrupt the biomedical discourse we use. The purpose of this study is to disrupt oppressive biomedical discourses by examining the language and structures medical educators use in their publications about queerness in relation to physicians and physician trainees. Approach. We searched PubMed, Web of Science, CINAHL, PsycINFO, and ERIC in October 2021 and again in June 2023 using a combination of controlled vocabulary (select terms designated by a database to enhance and reduce ambiguity in search) and keywords to identify articles related to sexuality, gender, identity, diversity and medical professionals. Searches were limited to articles published from 2013 to the present to align with the passage of The Respect for Marriage Act. Articles were included if they focused on the experiences and paths of physicians and physician trainees identifying with or embodying queerness, were authored by individuals based in the United States, and presented empirical studies. We excluded articles only discussing attitudes of cisgender heterosexual individuals about queerness. Two authors independently screened all articles for inclusion. We then used narrative techniques to “re-story” included articles into summaries, which we analyzed with four guiding questions, using queer theory as a sensitizing concept. Finally, we sought recurrent patterns in these summaries. Findings. We identified 2206 articles of which 23 were included. We found that biomedical discourse often: characterized individuals associated with queerness as a single homogenous group rather than as individuals with a breadth of identities and experiences; implied queer vulnerability without naming–and making responsible–the causes or agents of this vulnerability; and relied minimally on actual intervention, instead speculating on potential changes without attempting to enact them. Reflections. Authors each reflect on these findings from their positionalities, discussing: disrupting essentializing categories like “LGBT”; addressing harm through allyship around queerness; editorial responsibility to disrupt structures supporting oppressive biomedical discourse; the importance of program evaluation and interventions; and shifting the focus of medical education research toward queerness using QuantCrit theory. [ABSTRACT FROM AUTHOR]

Purpose: Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. Methods: We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs' performance. Results: The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. Conclusion: The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Within the last 15 years, more than 2.5 million "migrants" attended special language courses [so-called integration courses] in Germany. In the last three years "vocational language courses" increased, especially courses in the field of health care. The aim of the article is to show that language analysis in accordance with Edda Weigand's MGM can help to get a better insight into language usage, needed especially in the field of health care. By means of analysing one specific medical dialogue, the need for seeing language use as intercultural use will be discussed. [ABSTRACT FROM AUTHOR]

Background: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical Text version 2 (ExECTv2) NLP pipeline. Methods: We created 200 synthetic clinic letters based on hospital outpatient consultations with epilepsy specialists. The letters were double annotated by trained clinicians and researchers according to agreed guidelines. We used the annotation tool, Markup, with an epilepsy concept list based on the Unified Medical Language System ontology. All annotations were reviewed, and a gold standard set of annotations was agreed and used to validate the performance of ExECTv2. Results: The overall inter-annotator agreement (IAA) between the two sets of annotations produced a per item F1 score of 0.73. Validating ExECTv2 using the gold standard gave an overall F1 score of 0.87 per item, and 0.90 per letter. Conclusion: The synthetic letters, annotations, and annotation guidelines have been made freely available. To our knowledge, this is the first publicly available set of annotated epilepsy clinic letters and guidelines that can be used for NLP researchers with minimum epilepsy knowledge. The IAA results show that clinical text annotation tasks are difficult and require a gold standard to be arranged by researcher consensus. The results for ExECTv2, our automated epilepsy NLP pipeline, extracted detailed epilepsy information from unstructured epilepsy letters with more accuracy than human annotators, further confirming the utility of NLP for clinical and research applications. [ABSTRACT FROM AUTHOR]

The purpose of the article is to expose the results of the investigation whose objective was focused on developing activities that promote the development of oral expression in the english language in medical science students. The deficiencies in the English subject related to the development of oral expresion are shown to achieve a correct communication through the use of English language. Taking into account that the training of students in medical universities is based on having a good communicative approach, in addition, that they are prepared from a technical scientific point of view and that they have linguistic skills for exchange and collaboration which constitutes a current challenge for Cuban Universities. In this medical importance is given to knowledge with emphasis in the development of the oral expression, which constitutes a need which establishes as a graduation requirement user level equivalent to B1. [ABSTRACT FROM AUTHOR]

One of the largest family literacy providers in the Midwest shares a new initiative to bring the family literacy model to health care facilities as a means for providing families with deeper health literacy and language. The article details the early stages of program development, from codesign and deep-listening principles to the findings from the full program implementation. From these findings, we explore ways family literacy can bring important community conversations to the clinical space and inform future medical health literacy development. [ABSTRACT FROM AUTHOR]

When the sources are examined, it is known for centuries that cinnamon is one of the oldest spices and medicinal plants in the world. Medicinal plants have been used by humans for various purposes by drying, crushing, powdering or mixing with other drugs. Cinnamon has been included in religious ceremonies, folk medicine and culinary culture since ancient times in various parts of the world. Today, it is widely used in the treatment of various diseases, cosmetics industry, and folk medicine as a drug, spice, and fragrance, and is known as a source of healing. Plants have been an obligatory source of medicine for the treatment of disease and preventive health in the Ottoman period, as well as in the world. The understanding of medicine of the period was based on the theory of Humoral Pathology and Four Elements (Four Hilts-Ahlât-ı Erbaa), which was used to explain the causes of disease in Ancient Greece until the 19th century. Fire, water, earth and air in nature are the four elements that correspond to these Four Elements, such as blood, phlegm, yellow bile and black bile in humans. These also have the characteristics of warmth, dryness, coldness and humidity in the body. Every nutrient taken into the body is converted into these four substances. According to this theory, health depends on the balance of these hilts (fluids) in the body, and disease depends on the disruption of this balance. The proportions of these four fluids (humor, humor) in the body determine one's temperament. Every food in nature has a temperament. These are called "hot (har, germ), cold (barid, serd), dry (yabis, husk), moist (ratb, ter)". According to the theory of Four Hilts (humoral pathology), cinnamon has hot and dry properties. Due to its nature, it heals diseases with its softening and heating properties. In Turkish medical manuscripts, cinnamon is referred to as "darçîn/darçînî". In Turkish medical works, cinnamon is included in the recipes of various pastes and drugs, as well as in Ottoman cuisine. The medicinal plants and their uses, found in drug formulas in historical medical journals, are subjects of research in fields such as botany, medicine and pharmacy, as well as in philology due to their relevance to the Turkish language's vocabulary. During the Seljuk period, Turkish was used as a spoken language. Since the language of science was Arabic and Persian, medical works were also written in these languages. During the Anatolian Beyliks period, Turkish was given importance, and Turkish, which had been a spoken language until then, became the language of the state, written literature and science. Although the first Turkish medical work in Anatolia dates back to the early XIII century, the Turkishization of the medical language coincides with the second half of the XIV century. When we look at this century, Anatolia has developed quite a lot in terms of science, culture, art and literature. XIII-XV century Turkish medical works have made a great contribution to the Turkishization of the medical language. In later periods, there are also Turkish medical works, written in Ottoman Turkish, that shed light on their periods. In the majority of these works, the language used is quite simple, fluent, and understandable, as it aims to be useful to the reader. In general, the fact that many terms in the works appear in Turkish, Arabic, Persian, Greek or Frankish is proof that Turkish competes with other languages and is the language of science. In this study, the place of cinnamon as a medicinal plant, which is mentioned in Turkish medical manuscripts such as Müntehâb-ı Şifa (XIV century), Tabiatnâme (XIV century), Müfîd (Nazmü't-Teshîl) (XV century), Kitâb-ı Tıbb-ı Latîf' (XVI. century), Gâyetü'l Beyân fi Tedbiri Bedeni'l- İnsan (XVII century), Neşati Efendi's Dühn Terkipleri Risalesi (XVIII century), will be investigated. Thus, the diseases in which this plant is used in the treatment will be determined together with its techniques and transferred to today's Turkish. In line with the purpose of this study, a field literature review was conducted on medical works written in Turkish. Researching medical works that describe human health and methods of staying healthy is important in terms of revealing the power, vocabulary and terminological richness of the Turkish language. [ABSTRACT FROM AUTHOR]

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model's performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model's varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages. [ABSTRACT FROM AUTHOR]

Robot-assisted minimally invasive surgery is the gold standard for the surgical treatment of many pathological conditions since it guarantees to the patient shorter hospital stay and quicker recovery. Several manuals and academic papers describe how to perform these interventions and thus contain important domain-specific knowledge. This information, if automatically extracted and processed, can be used to extract or summarize surgical practices or develop decision making systems that can help the surgeon or nurses to optimize the patient's management before, during, and after the surgery by providing theoretical-based suggestions. However, general English natural language understanding algorithms have lower efficacy and coverage issues when applied to domain others than those they are typically trained on, and a domain specific textual annotated corpus is missing. To overcome this problem, we annotated the first robotic-surgery procedural corpus, with PropBank-style semantic labels. Starting from the original PropBank framebank, we enriched it by adding new lemmas, frames and semantic arguments required to cover missing information in general English but needed in procedural surgical language, releasing the Robotic-Surgery Procedural Framebank (RSPF). We then collected from robotic-surgery textbooks as-is sentences for a total of 32,448 tokens, and we annotated them with RSPF labels. We so obtained and publicly released the first annotated corpus of the robotic-surgical domain that can be used to foster further research on language understanding and procedural entities and relations extraction from clinical and surgical scientific literature. [ABSTRACT FROM AUTHOR]

To address the issue of weak model generalization caused by processing long texts, hierarchical coding structures, and long-tailed distributions in international classification of diseases (ICD) automatic coding methods, this paper proposed the PromptHP method for few-shot ICD automatic coding, leveraging medical pre-trained language models. Firstly, the PromptHP method combined coding descriptions and clinical texts into the prompt template to improve the model's comprehension of clinical texts. Then, it utilized the pre-trained language model's prior knowledge for initial prediction. Next, it introduced the hypersphere prototypical onto the output representation of the pre-trained language model for category modeling and metric classification, while fine-tuning the network on the medical dataset to incorporate the data knowledge and improve the model's performance on few-shot ICD coding classification tasks. Finally, it obtained the coding prediction results by integrating and weighting the two parts of the prediction results. Experimental results on the publicly available medical dataset MIMIC-III demonstrate that PromptHP outperforms state-of-the-art baseline methods, increasing the macro-AUC, micro-AUC, macro-F1, and micro-F1 of few-shot coding by 1.77%, 1.54%, 14.22%, and 15.01%, respectively. The experimental results validate the effectiveness of the PromptHP method in few-shot coding classification tasks. [ABSTRACT FROM AUTHOR]

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The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers' productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain. [ABSTRACT FROM AUTHOR]

Background: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance. Results: In this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, the Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate's accuracy and 90.0% on top 10 candidate's accuracy. Conclusion: This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials. [ABSTRACT FROM AUTHOR]

AbstractPurpose: Education on medical Spanish, defined as the use of Spanish by clinicians for communication with patients, has proliferated rapidly since the first guidelines were published in 2008. This study aims to characterize the scope of the field, identify gaps, and propose emerging questions for future study. Method: The authors conducted a narrative review of the medical Spanish education literature published from 2000 to 2023. First, a comprehensive search algorithm was developed across three databases (Medline, Scopus, and Web of Science Core Collection) and conducted on August 2, 2023. Two reviewers then independently assessed articles for inclusion/exclusion and subsequent categorization of included articles. Results: The search identified 1,303 articles, and authors added ten articles from other sources. A total of 138 individual articles were included in the final categorization and sub-analysis. There has been an upward trend in the number of articles published yearly since 2000. Most publications were educational interventions (67/138, 49%), followed by commentaries/perspectives (27/138, 20%), proficiency testing (17/138, 13%), needs assessments (16/138, 12%), reviews (6/138, 4%), and vocabulary analyses (5/138, 4%). Slightly over half of publications (72/138, 52%) were centered on physicians or physicians-in-training, with 23 (17%) articles applicable across health professions, and a few focused on pharmacists, nurses, physical therapists, psychologists, physician assistants, and genetic counselors. The vast majority (119/138, 86%) were published in medical/scientific journals and 19 (14%) in language/humanities journals. All but two first authors were affiliated with United States institutions, representing 30 states and Puerto Rico. Conclusions: Over the past two decades, many medical Spanish educational interventions have been published, and several assessment tools have been developed and validated. Gaps remain in evaluation data to demonstrate course effectiveness, the use of pedagogical frameworks to guide curricula, faculty development opportunities, and the role of heritage Spanish learners. Future work should address medical Spanish gaps in health professions and medical specialties, explore patient-engaged approaches to research, and evaluate longitudinal outcomes. [ABSTRACT FROM AUTHOR]

This document discusses the controversy surrounding the use of the word "renal" in medical literature. Some journals have started to demand that the word be replaced with "kidney" in an effort to make articles more understandable to patients. However, many physicians argue that this policy is misguided and undermines academic freedom. They believe that the medical literature should facilitate communication among physicians worldwide and that it is not the purview of English-speaking journals to dictate changes in the global medical lexicon. The authors encourage researchers to choose journals that respect academic freedom and not submit their work to journals that enforce this language control. [Extracted from the article]

The paper outlines the research on the usage of English borrowings, specifically acronyms, in the Polish medical language. The raw data is presented alongside a discussion about the findings and their implications. The research was conducted manually, with the assistance of basic electronic tools, on a personally prepared corpus consisting of an annual run of Kurier Medyczny, a journal that covers general medical topics. The analysis included the techniques of introducing the borrowed acronyms to the readers, their general meanings, as well as data related to their frequency within the corpus.

Medical language is characterized by its veracity, precision and clarity (Navarro, 2003). However, due to the different communicative situations and contexts in which it is used, it is one of the special languages with more terminological variation (Bowker and Hawkins, 2006). From the point of view of terminology work, in any of its applications: language planning, standardization or translation, the first steps consist of structuring the subject area and accurately define the conceptual field (Cabré, 2005; ISO, 2022; Wright, 1997), and variation is usually an obstacle during this stage. This paper presents the findings of a study for the elaboration of terminological resources on Women’s Health from a corpus of specialized academic articles in English. Preliminary results reveal a lack of uniformity in the identification of the most representative lexical units regarding issues that specifically affect Women’s Health. This analysis offers a typology of denominative variation in the subject field of Women’s Health in academic journals in English prior to initiate the delimitation of the conceptual field in Spanish and standardize terminology equivalence in order to ensure efficient communication.

The linguistics of special languages, which deserves a great deal of attention, makes it possible to break down the various special languages almost to the ground. The position of special languages is undoubtedly relevant at the present time. Transferring specialized knowledge, special languages make it possible to define, among other things, various phenomena typical of a particular field. With the permanent development of sciences, special languages are also evolving. One of the special languages that is developing with tremendous speed is the language of medicine which is analyzed here. One particularly significant aspect of the study of special languages is word formation. The purpose of this article is to present adjectival compounding in German medical language. The theoretical part, first and foremost, presents the informative sketch of special languages in general, of medical language itself and of adjective composition as a type of word formation. In the empirical part, based on the corpus incorporating issues of the professional journal “Deutsches Ärzteblatt” it was investigated (using a qualitative and quantitative method), what a-constituents have the adjective compounds, how many components the adjective compounds retrieved there consist of, how they are written and what linking morphemes participate in their formation. The corpus analysis carried out in the article illuminates the direction of orientation in the development of the German language of medicine in the area of word formation – here with regard to adjective composition – and provides important information on the structure, length and spelling of the extracted adjective compounds.

Summary: Background: Interest in large language models (LLMs), such as OpenAI's ChatGPT, across multiple specialties has grown as a source of patient‐facing medical advice and provider‐facing clinical decision support. The accuracy of LLM responses for gastroenterology and hepatology‐related questions is unknown. Aims: To evaluate the accuracy and potential safety implications for LLMs for the diagnosis, management and treatment of questions related to gastroenterology and hepatology. Methods: We conducted a systematic literature search including Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus and the Web of Science Core Collection to identify relevant articles published from inception until January 28, 2024, using a combination of keywords and controlled vocabulary for LLMs and gastroenterology or hepatology. Accuracy was defined as the percentage of entirely correct answers. Results: Among the 1671 reports screened, we identified 33 full‐text articles on using LLMs in gastroenterology and hepatology and included 18 in the final analysis. The accuracy of question‐responding varied across different model versions. For example, accuracy ranged from 6.4% to 45.5% with ChatGPT‐3.5 and was between 40% and 91.4% with ChatGPT‐4. In addition, the absence of standardised methodology and reporting metrics for studies involving LLMs places all the studies at a high risk of bias and does not allow for the generalisation of single‐study results. Conclusions: Current general‐purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks, which may lead to adverse patient safety events through incorrect information or triage recommendations, which might overburden healthcare systems or delay necessary care. [ABSTRACT FROM AUTHOR]

Researchers have indicated that integrating augmented reality into robot-assisted language learning modules could visually represent the students' learning needs and extend their embodied cognition of comprehension. To examine the potential of robots through extended embodied cognition, this quasi-experimental study was conducted in the English for Medical Purposes classrooms at the Isfahan University of Medical Sciences. In the 2022 academic year, 526 male and female participants, whose first language is not English, were selected. Students were sorted into three English proficiency levels: pre-intermediate (n = 176), intermediate (n = 175), and upper-intermediate (n = 175), then semi-randomly divided into three-member teams, subsequently, randomly assigned to control (n = 370) or experimental (n = 156) groups. Students in the control group learned English for Medical Purposes listening and reading through online classrooms, while those in the experimental group learned the skills in robot (augmented reality)-assisted classrooms. In the control and experimental groups, flipped classrooms were conducted under the supervision of the teachers. The primary data sources included formative assessments of the participants' English for Medical Purposes listening and reading, as well as interviews. The findings showed that the participants in the robot's augmented reality group achieved significantly higher results, when compared with robot-only and control groups, in English for Medical Purposes listening and reading in academia and the healthcare fields. The positive perception participants had of robot's augmented reality was clear, based on interview results. The outcomes are discussed in detail. • Robot-aided modules facilitated English for Medical Purposes listening and reading. • The glossed robot fostered connection with the world. • Robot's AR enhanced extended embodied cognition for comprehension. • Robot's AR fostered variegated channels for comprehension. • Robot's AR facilitated listening and reading in the fields. [ABSTRACT FROM AUTHOR]

Disorder named entity recognition (DNER) is a fundamental task of biomedical natural language processing, which has attracted plenty of attention. This task consists in extracting named entities of disorders such as diseases, symptoms, and pathological functions from unstructured text. The European Clinical Case Corpus (E3C) is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) of semantically annotated clinical case texts. The entities of type disorder in the clinical cases are annotated at both mention and concept level. At mention -level, the annotation identifies the entity text spans, for example, abdominal pain. At concept level, the entity text spans are associated with their concept identifiers in Unified Medical Language System, for example , C0000737. This corpus can be exploited as a benchmark for training and assessing information extraction systems. Within the context of the present work, multiple experiments have been conducted in order to test the appropriateness of the mention-level annotation of the E3C corpus for training DNER models. In these experiments, traditional machine learning models like conditional random fields and more recent multilingual pre-trained models based on deep learning were compared with standard baselines. With regard to the multilingual pre-trained models, they were fine-tuned (i) on each language of the corpus to test per-language performance, (ii) on all languages to test multilingual learning, and (iii) on all languages except the target language to test cross-lingual transfer learning. Results show the appropriateness of the E3C corpus for training a system capable of mining disorder entities from clinical case texts. Researchers can use these results as the baselines for this corpus to compare their own models. The implemented models have been made available through the European Language Grid platform for quick and easy access. [ABSTRACT FROM AUTHOR]

Purpose: All colleges of medicine in the Gulf Cooperation Council (GCC) adopt English as a language of instructions. This study aimed to examine medical students' views on introducing medical terminology in Arabic within an English-based curriculum. Design/methodology/approach: This descriptive study targeted preclinical second- and fourth-year students in the College of Medicine and Medical Sciences at the Arabian Gulf University, during the academic year 2022–2023 (n = 407). Within the pharmacology teaching material in unit I (second year) and unit VIII (fourth year), which are taught in English, students were provided with medical terms in Arabic. At the end of these two units, students' views were sought by using a self-administered questionnaire. Findings: The number of respondents was 263 (response rate 64.1%: 22.2% males, 77.8% females). Most participants received their school education mainly in Arabic (78.8%). A significant percentage of students believed that providing Arabic terms helped their learning (79.8%). If pharmacology is taught exclusively in English, majority of the students anticipated to face difficulties when explaining drug treatment to their patients in the future (71.3%). Most respondents expected this intervention to help them communicate with patients (86.7%), and preferred to include it in the clinical skills training (82.2%). The second-year students and those whose school education was mainly in Arabic were more likely to agree to the intervention (p < 0.05 for both). Originality/value: The introduction of medical terms in Arabic is an acceptable alternative to complete Arabization, and is believed to help students in their learning and communication with their patients. [ABSTRACT FROM AUTHOR]

This study explores the exactness of machine translation and finding out the comparison between Generative Pretrained Transformer (chatGpt) that has denoted remarkable development in various projects which are related to language and translation which has done by human being. Intercultural connections have become easy to understand with the emergence of AI tools like chatGpt which is working as a translator for various languages like English, Urdu, Arabic and many more. This study finds the drawbacks and differences of chatGpt translation with human translation. AS a result it designates that chatGpt is less reliable for translation of complexities in languages, it can be used as a translator of simple language but it has less reliability in translation of medical language, language of law and literary works. So, one must be careful as a user of chatGpt while translating such texts and the interference of human being is mandatory to make sure the authenticity of translation. Furthermore, it cannot translate the idiomatic expressions in a refined form. This article is also going to discuss the functions of machine translation and its effect on translation which has already done by human. It selects English and Urdu language for this purpose. English is the target language and source language is Urdu and the researcher uses the qualitative method for this research. [ABSTRACT FROM AUTHOR]

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning. [ABSTRACT FROM AUTHOR]

Objectives: Children with medical complexity experienced health disparities during the coronavirus disease 2019 (COVID-19) pandemic. Language may compound these disparities since people speaking languages other than English (LOE) also experienced worse COVID-19 outcomes. Our objective was to investigate associations between household language for children with medical complexity and caregiver COVID-19 vaccine intentions, testing knowledge, and trusted sources of information. Methods: This cross-sectional survey of caregivers of children with medical complexity ages 5 to 17 years was conducted from April-June 2022. Children with medical complexity had at least 1 Complex Chronic Condition. Households were considered LOE if they reported speaking any language other than English. Multivariable logistic regression examined associations between LOE and COVID-19 vaccine intentions, interpretation of COVID-19 test results, and trusted sources of information. Results: We included 1,338 caregivers of children with medical complexity (49% response rate), of which 133 (10%) had household LOE (31 total languages, 58% being Spanish). There was no association between household LOE and caregiver COVID-19 vaccine intentions. Caregivers in households with LOE had similar interpretations of positive COVID-19 test results, but significantly different interpretations of negative results. Odds of interpreting a negative test as expected (meaning the child does not have COVID-19 now or can still get the virus from others) were lower in LOE households (aOR [95% CI]: 0.56 [0.34–0.95]). Households with LOE were more likely to report trusting the US government to provide COVID-19 information (aOR [95% CI]: 1.86 [1.24–2.81]). Conclusion: Differences in COVID-19 test interpretations based on household language for children with medical complexity were observed and could contribute to disparities in outcomes. Opportunities for more inclusive public health messaging likely exist. [ABSTRACT FROM AUTHOR]

Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications. [ABSTRACT FROM AUTHOR]

AbstractPhenomenon: In China, medical English courses are critical to medical education, equipping Chinese students with the linguistic tools necessary for international medical practice and collaboration. However, a disconnect persists between the pedagogical approaches of medical practitioners and language educators, leading to a curriculum that emphasizes grammatical accuracy over practical communication skills. This misalignment results in student disengagement and falls short of addressing the real-world demands of the medical profession. With the growing importance of English proficiency in the global health sector, the need for significant improvements in medical English education is evident. This study delves into the underlying causes of student demotivation and aims to reconcile educational delivery with the evolving expectations of the medical field. Insights gained from this research will inform targeted interventions, promising to enhance medical English courses and support improved educational experiences for Chinese medical undergraduates. Approach: This cross-sectional quantitative study surveyed 3,046 second-year medical students from four medical universities in Guangdong Province, China, leveraging means-analysis and Expectancy-Disconfirmation Theory (EDT) as its foundation. The research was conducted at the end of the 2022–2023 academic year, utilizing a questionnaire to assess students’ perceptions of their medical English courses. Importance-Performance Analysis (IPA) was the primary analytical tool to discern discrepancies between students’ expectations and experiences. Findings: The IPA revealed that course content, classroom environment, and instructor effectiveness were pivotal factors influencing the perceived quality of the medical English courses. Students expressed a need for practical and relevant course material, with current content and textbooks falling short of preparing them for future medical communication demands. Additionally, while learning technologies were acknowledged, there was a discernible preference against their excessive application, suggesting a misalignment between student satisfaction and learning outcomes. Insights: This study highlights the need for innovative staffing models, refined qualifications for part-time instructors, development of collaborative and practical teaching materials, and focused training for medical English instructors. It also emphasizes the judicious integration of e-learning to enhance the learning experience. These insights aim to improve instruction quality by informing potential pedagogical adjustments and resource allocations in medical English education. [ABSTRACT FROM AUTHOR]

This article meticulously examines the transformation of educator roles in medical education against the backdrop of emerging large language models (LLMs). Traditionally, educators have played a crucial role in transmitting knowledge, training skills, and evaluating educational outcomes. However, the advent of LLMs such as Chat Generative Pre-trained Transformer-4 has expanded and enriched these traditional roles by leveraging opportunities to enhance teaching efficiency, foster personalised learning, and optimise resource allocation. This has imbued traditional medical educator roles with new connotations. Concurrently, LLMs present challenges to medical education, such as ensuring the accuracy of information, reducing bias, minimizing student over-reliance, preventing patient privacy exposure and safeguarding data security, enhancing the cultivation of empathy, and maintaining academic integrity. In response, educators are called to adopt new roles including experts of information management, navigators of learning, guardians of academic integrity, and defenders of clinical practice. The article emphasises the enriched connotations and attributes of the medical teacher's role, underscoring their irreplaceable value in the AI-driven evolution of medical education. Educators are portrayed not just as users of advanced technology, but also as custodians of the essence of medical education. [ABSTRACT FROM AUTHOR]

Empathy, which involves supporting the client, is a mandatory component of doctor-client communication. Sticking to the tactics of consolation in communication allows achieving greater effectiveness of the clinical activity of a medical worker. Since medical discourse as a sample of institutional discourse is characterized by a certain constant pattern, it has been proven that in the context of learning a foreign language it is appropriate to use a genre approach. The involvement of the genre approach in linguistic didactics is also explained by the dependence of the number of speech genres mastered by a foreigner and his level of formation of the secondary language personality. It is proved that consolation is a speech genre. The choice of film discourse for the study is justified by the specificity of the genre and the fact that it is a sample of authentic texts, the effectiveness of the auditory and visual channels of information perception. The "signals" of the use of the consolation genre, vocabulary and grammatical features are described. The verbal means of the main tactics within the secondary genre of consolation are listed. Methods of organizing the teaching of the Ukrainian language according to the genre approach are proposed. [ABSTRACT FROM AUTHOR]

Effective communication in medical writing is essential for conveying accurate and understandable information to healthcare professionals and patients. English language errors in medical literature can lead to misunderstandings, misinterpretations, and potentially harmful consequences. This systematic literature review aims to identify, analyze, and categorize common English language errors encountered in medical writing. This study identifies gaps in existing literature on language errors in medical writing and reveals areas that need further investigation or where more comprehensive guidelines are required. This study addresses six questions: (1) What types of language errors are discussed in the current literature? (2) What types of medical writing are involved in the current literature? (3) Which countries or areas are involved in the current literature? (4) What are the research methods used in the studies? (5) What tools are used in the studies? (6) What are the limitations of these studies? To answer these questions, this review searched nine databases and one platform, ProQuest Dissertations and Theses Fulltext, ProQuest Ebook Central (e-Books), Scopus, Web of Science, Wiley Online Library (e-Journals & e-Books) (PPV), Science Direct, Cambridge Core e-Books, Oxford Press Scholarship Online (e-Books), Google Scholar, and Carian Bestari@UKM / Discovery Service@UKM. Finally, ten studies were selected, including articles and books. The findings provide insights into types of language errors, types of medical writing, countries or areas of medical writing, research methods, tools, and limitations of these ten studies, highlighting the importance of linguistic accuracy and proficiency in medical writing. [ABSTRACT FROM AUTHOR]

Innovative communication theories propose that we understand messages not by decoding their meaning but by inferring what speakers intend to express. However scientifically accurate the messages climate scientists have put forward, the appropriate inferences may not have been drawn by most of their audiences. One of the main reasons may be that scientific metaphors allow for multiple interpretations, yet, because of their expressive power, they impact discourses disproportionately. Climate communication took a path of euphemistic scientific expressions partially due to the noble scientific norms of self-restraint and modesty, but the hidden implications of climate jargon distort the way non-experts think about the heating climate. Consequently, the current climate jargon hinders informed decisions about Earth's life support systems. Changing the softened expressions of climate language, from the cool of basic research to the heat and compassion of medical contexts, may allow for more productive public and political debates -- which may lead to more powerful policy solutions. Speaking and thinking in medical terms could turn the perception of worst case scenarios from hypotheticals or doomism to life-saving interventions. We typically start reducing fever before it gets out of control, let alone crosses a threshold of potential death. Instead of putting on a positivist mascara, a calm and serious discussion of safety measures in medical terms, for example, talking about climatic tipping cascades as metastases, could foster a more honest evaluation of the required legal and regulatory steps to keep our home planet habitable. [ABSTRACT FROM AUTHOR]

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in the French language. The methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. This research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with electronic health records (EHRs) and its utility in medical education. This study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions. [ABSTRACT FROM AUTHOR]

This article examines dialogue interpreting in unscripted role-plays in the community interpreting classroom. In 2019, faculty members from several departments at Viterbo University (La Crosse, Wisconsin) coordinated an interprofessional education collaboration via role-playing in the institution's Clinical Simulation Learning Center. Nursing, social work and pre-medical students were given the health-professional roles of caring for community members with limited English proficiency (who acted as 'patients'). Interpreting students, both heritage speakers of Spanish and second language learners (L2) of both English and Spanish, facilitated language access for all parties involved. Recordings of these dialogues were then transcribed, annotated, and analyzed via mixed methods. This study examines overall and comparative findings of how heritage speakers and second language learners interpret dialogue, focusing on the textual aspects of their exchanges. While no language profile seemed to perform particularly better overall, certain indicators were more problematic for L2 Spanish speakers and/or heritage speakers. The presentation of these results and conclusions intend to foster improved teaching interventions for classrooms with students of varying English <> Spanish language backgrounds. [ABSTRACT FROM AUTHOR]

Background: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing. Results: Word embeddings, which represent a word's context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug's repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE. Conclusions: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique's suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach. [ABSTRACT FROM AUTHOR]

The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. [ABSTRACT FROM AUTHOR]

In the review by Omiye and colleagues ([1]), the authors identified several numerical input errors in Figure 2. The following corrections have been made to address these: Vicuna was changed from 7B to 13B. ChatGPT was renamed as GPT-3. Med-PALM was changed from 540B+ to 540B. BioGPT was changed from 357B to 347M. PMC-LLaMA was changed from 75B to 13B. BioGPT-Large was added.These changes do not affect the conclusions of the article. [Extracted from the article]