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Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to describe artificial intelligence, machine learning, and "Dentronics" applications and perspectives in dentistry. Materials and Methods: A literature review was carried out to identify the applications of artificial intelligence in the field of dentistry. A specialized search for information was carried out in three databases such as Scopus, PubMed, and Web of Science. Manuscripts published from January 1988 to November 2021 were analyzed. Articles were included without any restriction by language or country. Results: Scopus, PubMed, and Web of Science were found to have 215, 1023, and 98 registered manuscripts, respectively. Duplicates (191 manuscripts) were eliminated. Finally, 4 letters, 12 editorials, 5 books, 1 erratum, 54 conference papers, 3 conference reviews, and 222 reviews were excluded. Conclusions: Artificial intelligence has revolutionized prediction, diagnosis, and therapeutic management in modern dentistry. Finally, artificial intelligence is a potential complement to managing future data in this area. [ABSTRACT FROM AUTHOR]

The article focuses on a critical analysis of systematic review methodologies concerning vertical root fractures (VRFs), highlighting concerns about potential biases when including studies on broader categories of dental cracks in VRF-specific reviews. Topics include the definition and characteristics of VRFs, methodological variations in VRF detection studies, and the implications of these variations on the reliability of systematic reviews in dental research.

This article examines the advantages and disadvantages of large language models(LLMs) and artificial intelligence (AI) in research and education and proposes the urgent need for an international statement to guide their responsible use. LLMs and AI demonstrate remarkable natural language processing, data analysis, and decision‑making capabilities, offering potential benefits such as improved efficiency and transformative solutions. However, concerns regarding ethical considerations, bias, fake publications, and malicious use also arise. The objectives of this paper are to critically evaluate the utility of LLMs and AI in research and education, call for discussions between stakeholders, and discuss the need for an international statement. We identify advantages such as data processing, task automation, and personalized experiences, alongside disadvantages such as bias reinforcement, interpretability challenges, inaccurate reporting, and plagiarism. Stakeholders from academia, industry, government, and civil society must engage in open discussions to address the ethical, legal, and societal implications. The proposed international statement should emphasize transparency, accountability, ongoing research, and risk mitigation. Monitoring, evaluation, user education, and awareness are essential components. By fostering discussions and establishing guidelines, we can ensure the responsible and ethical development and use of LLMs and AI, maximizing benefits while minimizing risks. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images. [ABSTRACT FROM AUTHOR]

This review article explores the use of ChatGPT in academic writing and provides insights on how to utilize it judiciously. With the increasing popularity of AI-powered language models, ChatGPT has emerged as a potential tool for assisting writers in the research and writing process. We have provided a list of potential uses of ChatGPT by a novice researcher for getting help during research proposal preparation and manuscript writing. However, there are concerns regarding its reliability and potential risks associated with its use. The review highlights the importance of maintaining human judgment in the writing process and using ChatGPT as a complementary tool rather than a replacement for human effort. The article concludes with recommendations for researchers and writers to ensure responsible and effective use of ChatGPT in academic writing. [ABSTRACT FROM AUTHOR]

Radiosurgery and stereotactic radiotherapy have established themselves as precise and accurate areas of radiation oncology for the treatment of brain and extracranial lesions. Along with the evolution of other methods of radiotherapy, this type of treatment has been associated with significant advances in terms of a variety of modalities and techniques to improve the accuracy and efficacy of treatment. This paper provides a comprehensive overview of the progress in stereotactic radiosurgery (SRS) over several decades, and includes a review of various articles and research papers, commencing with the emergence of stereotactic techniques in radiotherapy. Key clinical aspects of SRS, such as fixation methods, radiobiology considerations, quality assurance practices, and treatment planning strategies, are presented. In addition, the review highlights the technological advancements in treatment modalities, encompassing the transition from cobalt-based systems to linear accelerator-based modalities. By addressing these topics, this study aims to offer insights into the advancements that have shaped the field of SRS, that have ultimately enhanced the accuracy and effectiveness of treatment. [ABSTRACT FROM AUTHOR]

This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is one of the leading causes of death worldwide affecting mainly low- and middle-income countries. Therefore, the objective is to analyze the bibliometric characteristics of the application of artificial intelligence (AI) in tuberculosis in Scopus. Methods: A bibliometric study, the Scopus database was used using a search strategy composed of controlled and free terms regarding tuberculosis and AI. The search fields "TITLE," "ABSTRACT," and "AUTHKEY" were used to find the terms. The collected data were analyzed with Scival software. Bibliometric data were described through the figures and tables summarized by absolute values and percentages. Results: Thousand and forty-one documents were collected and analyzed. Yudong Zhang was the author with the highest scientific production; however, K. C. Santosh had the greatest impact. Anna University (India) was the institution with the highest number of published papers. Most papers were published in the first quartile. The United States led the scientific production. Articles with international collaboration had the highest impact. Conclusion: Articles related to tuberculosis and AI are mostly published in first quartile journals, which would reflect the need and interest worldwide. Although countries with a high incidence of new cases of tuberculosis are among the most productive, those with the highest reported drug resistance need greater support and collaboration. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is making giant strides in the medical domain, and the field of anaesthesia is not untouched. Enhancement in technology, especially AI, in many fields, including medicine, has proven to be far superior, safer and less erratic than human decision-making. The intersection of anaesthesia and AI holds the potential for augmenting constructive advances in anaesthesia care. AI can improve anaesthesiologists' efficiency, reduce costs and improve patient outcomes. Anaesthesiologists are well placed to harness the advantages of AI in various areas like perioperative monitoring, anaesthesia care, drug delivery, post-anaesthesia care unit, pain management and intensive care unit. Perioperative monitoring of the depth of anaesthesia, clinical decision support systems and closed-loop anaesthesia delivery aid in efficient and safer anaesthesia delivery. The effect of various AI interventions in clinical practice will need further research and validation, as well as the ethical implications of privacy and data handling. This paper aims to provide an overview of AI in perioperative monitoring in anaesthesia. [ABSTRACT FROM AUTHOR]

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges. [ABSTRACT FROM AUTHOR]

Managing the massive losses associated with large-scale disasters requires significant resources. The unexpected violence of these events generally remains a matter of casualties that urgently need to be identified in a reliable and cost-effective manner. To overcome these difficulties, many researchers have attempted to develop automated methods; moreover, a few recent research have investigated the applicability of artificial intelligence (AI)-based methods using skull, dental, and maxillofacial forensic imaging. In this review, we speculate on the advancement and potential of AI in Dental and Maxillofacial imaging that can help simplify person or victim identification and speed up the process with good accuracy. Using a few prefix search phrases, an online literature search was conducted (AI, Forensic, Skull, Dental, Imaging, Radiology) to identify papers about the advancement of AI in forensic dentistry in all kinds of radiographs, including two-dimensional (2D) and three-dimensional (3D) radiographs, cone beam computed tomography (CT) and CT. Most of the studies reported that automated methods of human identification based on 2D and 3D Dental and Skull radiographs using a convolutional neural network showed to assist in the fast and accurate identification by expertise evaluating a lot of images and quickly coming up with potential matches for identification. We advocate the application of AI techniques in the identification of individuals. However, there is a need to continue research with emphasis to validate models in skull identification. [ABSTRACT FROM AUTHOR]

Chat generative pretrained transformer (ChatGPT) is a conversational language model powered by artificial intelligence (AI). It is a sophisticated language model that employs deep learning methods to generate human-like text outputs to inputs in the natural language. This narrative review aims to shed light on ethical concerns about using AI models like ChatGPT in writing assistance in the health care and medical domains. Currently, all the AI models like ChatGPT are in the infancy stage; there is a risk of inaccuracy of the generated content, lack of contextual understanding, dynamic knowledge gaps, limited discernment, lack of responsibility and accountability, issues of privacy, data security, transparency, and bias, lack of nuance, and originality. Other issues such as authorship, unintentional plagiarism, falsified and fabricated content, and the threat of being red-flagged as AI-generated content highlight the need for regulatory compliance, transparency, and disclosure. If the legitimate issues are proactively considered and addressed, the potential applications of AI models as writing assistance could be rewarding. [ABSTRACT FROM AUTHOR]

Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products. [ABSTRACT FROM AUTHOR]

Referral is a dynamic process, in which a health worker at one level of the health system, having insufficient resources (drugs, equipment, skills) to manage a clinical condition, seeks the help of a better or differently resourced facility at the same or higher level to assist in. Health care systems of every country are designed in such a way to encourage patients to first attempt to get care at the primary level and then to approach a higher level of care according to the need. This protocol minimizes the costs for the caretaker/patients. However, in most of the countries, patients often bypass primary care facilities and directly go to the higher center thereby, increasing the burden on higher level facilities, the picture is not very different in India also. Health care system in India is plugged by: overpopulation, lack of expert clinicians, skewed distribution of physicians, lack of motivation among existing health care personnel and an ineffective referral mechanism. Due to failure of conventional paper-based referral systems in our country, we can introduce an e-referral system in the era of internet. It is evident from our experiences, that this artificial intelligence enabled e-referral system has many advantages over the traditional paper-based referral system. It will aid health workers for timely management of cases. Most importantly, it will streamline the existing unorganized referral process. Although, for effective e-Referral system, there should be a collaborative platform where easy search and discovery for health care providers is possible and help in decision making. e- Referral should be incorporated in our health system to strengthen it by bridging the access gap may be through Public Private Partnership model. [ABSTRACT FROM AUTHOR]

A paradigm shift in human evolution, from our predecessors, the 'hunter-gatherers' to the 'era of digital revolution', has made certain human skills more and more machine driven. This digital revolution has made possible a constant connectivity, wearable technologies, customized platforms, enormous data storage and cloud computing at high speeds, smart phones and apps, internet of things, artificial intelligence, telemedicine, etc. These have made real-time monitoring and interventions possible in healthcare. Most advanced countries have made electronic health records (EHR) mandatory. The Government of India has an agenda of Digital India and digital healthcare and might insist on EHRs. EHR is a real-time, patient-centered digital version of a patient's paper record/chart, available instantly and securely to authorized users. EHR contains a patient's medical history, diagnosis, medications, treatment plans, immunization dates, allergies, radiological images, and laboratory results. It can access evidence-based tools that help to make safer decisions about a patient's care with enhanced decision support, clinical alerts, reminders and medical information. The procedure is also more reliable for dispensing medications and introduces the convenience of e-prescriptions. While the advanced technology and digital devices are well received by the healthcare providers, universal acceptance of the EHRs is far from achieving its full potential. The author, in this paper, discusses the current scenario and issues concerned with EHRs in the digital healthcare. [ABSTRACT FROM AUTHOR]

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution. [ABSTRACT FROM AUTHOR]

Nanotechnology with artificial intelligence (AI) can metamorphose medicine to an extent that has never been achieved before. AI could be used in anesthesia to develop advanced clinical decision support tools based on machine learning, increasing efficiency, and accuracy. It is also potentially highly troublesome by creating insecurity among clinicians and allowing the transfer of expert domain knowledge to machines. Anesthesia is a complex medical specialty, and assuming AI can easily replace the expert as a clinically sound anesthetist is a very unrealistic expectation. This paper focuses on the association and opportunities for AI developments and deep learning with anesthesia. It reviews the current advances in AI tools and hardware technologies and outlines how these can be used in the field of anesthesia. [ABSTRACT FROM AUTHOR]

Purpose: Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual detection of glaucoma is a challenging task that needs expertise and years of experience.Methods: In this paper, we suggest a powerful and accurate algorithm using a convolutional neural network (CNN) for the automatic diagnosis of glaucoma. In this work, 1113 fundus images consisting of 660 normal and 453 glaucomatous images from four databases have been used for the diagnosis of glaucoma. A 13-layer CNN is potently trained from this dataset to mine vital features, and these features are classified into either glaucomatous or normal class during testing. The proposed algorithm is implemented in Google Colab, which made the task straightforward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of our algorithm, the dataset is divided into 70% for training, 20% for validation, and the remaining 10% utilized for testing. The training images are augmented to 12012 fundus images.Results: Our model with SoftMax classifier achieved an accuracy of 93.86%, sensitivity of 85.42%, specificity of 100%, and precision of 100%. In contrast, the model with the SVM classifier achieved accuracy, sensitivity, specificity, and precision of 95.61, 89.58, 100, and 100%, respectively.Conclusion: These results demonstrate the ability of the deep learning model to identify glaucoma from fundus images and suggest that the proposed system can help ophthalmologists in a fast, accurate, and reliable diagnosis of glaucoma. [ABSTRACT FROM AUTHOR]

Background: Patient education is an essential component of improving public health as it empowers individuals with the knowledge and skills necessary for making informed decisions about their health and well-being. Primary care physicians play a crucial role in patients' education as they are the first contact between the patients and the healthcare system. However, they may not get adequate time to prepare educational material for their patients. An artificial intelligence-based writer like ChatGPT can help write the material for physicians. Aim: This study aimed to ascertain the capability of ChatGPT for generating patients' educational materials for common public health issues in India. Materials and Methods: This observational study was conducted on the internet using the free research version of ChatGPT, a conversational artificial intelligence that can generate human-like text output. We conversed with the program with the question - "prepare a patients' education material for X in India." In the X, we used the following words or phrases -- "air pollution," "malnutrition," "maternal and child health," "mental health," "noncommunicable diseases," "road traffic accidents," "tuberculosis," and "water-borne diseases." The textual response in the conversation was collected and stored for further analysis. The text was analyzed for readability, grammatical errors, and text similarity. Result: We generated a total of eight educational documents with a median of 26 (Q1-Q3: 21.5-34) sentences with a median of 349 (Q1-Q3: 329-450.5) words. The median Flesch Reading Ease Score was 48.2 (Q1-Q3: 39-50.65). It indicates that the text can be understood by a college student. The text was grammatically correct with very few (seven errors in 3415 words) errors. The text was very clear in the majority (8 out of 9) of documents with a median score of 85 (Q1-Q3: 82.5-85) in 100. The overall text similarity index was 18% (Q1-Q3: 7.5-26). Conclusion: The research version of the ChatGPT (January 30, 2023 version) is capable of generating patients' educational materials for common public health issues in India with a difficulty level ideal for college students with high grammatical accuracy. However, the text similarity should be checked before using it. Primary care physicians can take the help of ChatGPT for generating text for materials used for patients' education. [ABSTRACT FROM AUTHOR]

Dermatology, as a highly intuitive clinical discipline, requires dermatologists to apply their own knowledge and clinical experience to make a reasonable diagnosis for various skin diseases. However, the diagnosis results are largely affected by the subjective consciousness of dermatologists, and there is a certain misdiagnosis rate, especially in areas with low medical levels. Therefore, there is an urgent need for more accurate, objective, and efficient auxiliary diagnostic tools to improve the diagnostic level of skin diseases. Facing an increasing number of patients and higher requirements for accurate diagnosis of diseases, Artificial Intelligence (AI) technology has attracted considerable attention in the field of dermatology. As a nonorganism, the computer is not affected by subjective consciousness, emotion, fatigue, and other factors in the recognition process. It helps to improve the efficiency of diagnosis and assists human doctors to provide objective and accurate diagnosis results. This paper systematically summarizes the research progress of AI in the diagnosis and application of skin cancers, acne, rosacea, onychomycosis, psoriasis, vitiligo, atopic dermatitis, and eczema diseases at home and abroad in recent years, hoping to help dermatologists have a deeper understanding of AI. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) broadly refers to machines that simulate intelligent human behavior, and research into this field is exponential and worldwide, with global players such as Microsoft battling with Google for supremacy and market share. This paper reviews the “good” aspects of AI in medicine for individuals who embrace the 4P model of medicine (Predictive, Preventive, Personalized, and Participatory) to medical assistants in diagnostics, surgery, and research. The “bad” aspects relate to the potential for errors, culpability, ethics, data loss and data breaches, and so on. The “ugly” aspects are deliberate personal malfeasances and outright scientific misconduct including the ease of plagiarism and fabrication, with particular reference to the novel ChatGPT as well as AI software that can also fabricate graphs and images. The issues pertaining to the potential dangers of creating rogue, super‑intelligent AI systems that lead to a technological singularity and the ensuing perceived existential threat to mankind by leading AI researchers are also briefly discussed. [ABSTRACT FROM AUTHOR]

Automated clinical decision support systems (CDSS) are knowledge-based systems that provide patient-specific information and data to clinicians at the proper time for enhancing the clinical workflow of hospital organizations. Nowadays, it is adopted by most of the health care professionals for clinical decision-making that helps to reduce the adverse clinical care events occurring during the treatment. In this article, we present a recent literature review on the adoption of computer-based CDSSs in the area of health care based on qualitative and quantitative techniques, published between 2007 and 2022. For this purpose, we searched Google Scholar and identified different adoption factors by using textual analysis from the included publications. We then ranked the different factors based on the total number of occurrences and represented them as a conceptual framework. A total of 14 different adoption factors were found from 13 studies, among them the usefulness of the system is the most prominent factor that influences the adoption of CDSS to a great extent. This literature review and the framework could be helpful to researchers and healthcare professionals working in the field of technology adoption, providing an overall idea of factors and techniques in this field of research. We have also mentioned the limitations and future research gaps of different studies, which will help the researchers to take an initiation towards these types of research. We also conducted a case study on adoption of fully automatic digital blood pressure monitor and identified that "usefulness" and "ease of use" could influence the adoption of fully automatic digital blood pressure monitor system. [ABSTRACT FROM AUTHOR]

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine. [ABSTRACT FROM AUTHOR]

Background: There is a constant search across the globe for optimal healthcare solutions with affordability, accessibility, availability, and quality of healthcare services being the burning issue for mankind. The pandemic has further necessitated the need of use of the Telemedicine platform to address healthcare issues which are also non Covid related. Though Telemedicine has been in use for over two decades in India, there have been various challenges and adoption issues which have not yet made the technology an effective solution to address the current healthcare issues. There has been phenomenal growth in the Information and Communication Technology (ICT) over the last decade and its utilization in the healthcare field. Methods: Internet research on the various adoption strategies by healthcare providers coupled with our own experience for using this technology along with guidelines provided by the information and communication technology providers. The Telemedicine Guidelines of 2020 released by the Ministry of Health and Family welfare, India, provides a framework for the implementation of healthcare delivery through this technology. Results: This paper mentions our telemedicine experience in governmental and private institutes and highlights the implementation challenges of this technology and some solutions that made a difference in the execution. However, we discuss to a larger extent the possible challenges and barriers in the implementation of this technology in India. Conclusion: Despite successful work in the field of telemedicine, it is yet to become an integral part of healthcare system because challenges related to adaptability of healthcare users and lack of proper training to fast growing technologies. The future is going to compel the usage of this kind of technology and it is essential for setting up infrastructure and having trained personnel to man these departments to encash the full potential of the telemedicine technology. [ABSTRACT FROM AUTHOR]

As a complementary and alternative medicine (CAM), traditional Chinese medicine (TCM) represents an established medical system with a rich history and abundant clinical experience. TCM is an empirical medicine, the process of which is analogous to ChatGPT's learning and development model. In TCM, inquiry is a relatively objective way of traditional syndrome differentiation. Although various artificial intelligence systems related to TCM consultation exist, their interactive abilities remain limited. The study standardized the primary complaint and instructed ChatGPT to simulate a TCM practitioner, conducting three comprehensive inquiry tests. The results yielded unexpected conclusions, revealing that ChatGPT could simulate a TCM practitioner's inquiry with patients, confirming its potential in the field of TCM inquiry. However, current applications still pose certain limitations and risks. Hence, to integrate ChatGPT-like language models with traditional TCM AI to establish an associative mode that can facilitate TCM diagnosis and treatment with more convenience and standardization is crucial, yet at the same time, it should be treated very carefully. [ABSTRACT FROM AUTHOR]

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine. [ABSTRACT FROM AUTHOR]

Even after making allowance for an unprecedented hype, it is an undeniable fact that, in the coming decade, deployment of Artificial Intelligence (AI) will cause a paradigm shift in the delivery of healthcare. This paper will review the practical utility of AI in neurosciences from a clinician's perspective. Steering clear of the complex, technical, computational jargon, the authors will critically review the exponential development in this area from a clinical standpoint. The reader will be exposed to the fundamentals of AI in healthcare and its applications in different areas of neurosciences. Powerful AI techniques can unlock clinically relevant information, hidden in massive amounts of data. Translating technical computational success to meaningful clinical impact is, however, a challenge. AI requires a thorough and systematic evaluation, prior to integration in the clinical care. Like other disruptive technologies in the past, its potential for causing a great impact should not be underestimated. A scenario in which medical information, gathered at the point of care, is analyzed using sophisticated machine algorithms to provide real-time actionable analytics seems to be within touching distance. [ABSTRACT FROM AUTHOR]

Background: AI in healthcare services is advancing every day, with a focus on uprising cognitive capabilities. Higher cognitive functions in AI entail performing intricate processes like decision-making, problem-solving, perception, and reasoning. This advanced cognition surpasses basic data handling, encompassing skills to grasp ideas, understand and apply information contextually, and derive novel insights from previous experiences and acquired knowledge. ChatGPT, a natural language processing model, exemplifies this evolution by engaging in conversations with humans, furnishing responses to inquiries. Objective: We aimed to understand the capability of ChatGPT in solving doubts pertaining to symptoms and histological features related to subject of oral pathology. The study's objective is to evaluate ChatGPT's effectiveness in answering questions pertaining to diagnoses. Methods: This cross-sectional study was done using an AI-based ChatGPT application that provides free service for research and learning purposes. The current version of ChatGPT3.5 was used to obtain responses for a total of 25 queries. These randomly asked questions were based on basic queries from patient aspect and early oral histopathologists. These responses were obtained and stored for further processing. The responses were evaluated by five experienced pathologists on a four point liekart scale. The score were further subjected for deducing kappa values for reliability. Result & Statistical Analysis: A total of 25 queries were solved by the program in the shortest possible time for an answer. The sensitivity and specificity of the methods and the responses were represented using frequency and percentages. Both the responses were analysed and were statistically significant based on the measurement of kappa values. Conclusion: The proficiency of ChatGPT in handling intricate reasoning queries within pathology demonstrated a noteworthy level of relational accuracy. Consequently, its text output created coherent links between elements, producing meaningful responses. This suggests that scholars or students can rely on this program to address reasoning-based inquiries. Nevertheless, considering the continual advancements in the program's development, further research is essential to determine its accuracy levels in future versions. [ABSTRACT FROM AUTHOR]

Halitosis, commonly known as oral malodor, is a multifactorial health concern that significantly impacts the psychological and social well-being of individuals. It is the third most frequent reason for individuals to seek dental treatment, after dental caries and periodontal diseases. For an in-depth exploration of the topic of halitosis, an extensive literature review was conducted. The review focused on articles published in peer-reviewed journals and only those written in the English language were considered. The search for relevant literature began by employing subject headings such as 'halitosis, oral malodor, volatile sulfur compounds, artificial intelligence, and olfaction' in databases such as PubMed/Medline, Scopus, Google Scholar, Web of Science, and EMBASE. Additionally, a thorough hand search of references was conducted to ensure the comprehensiveness of the review. After amalgamating the search outcomes, a comprehensive analysis revealed the existence of precisely 134 full-text articles that bore relevance to the study. Abstracts and editorial letters were excluded from this study, and almost 50% of the full-text articles were deemed immaterial to dental practice. Out of the remaining articles, precisely 54 full-text articles were employed in this review. As primary healthcare providers, dentists are responsible for diagnosing and treating oral issues that may contribute to the development of halitosis. To effectively manage this condition, dentists must educate their patients about the underlying causes of halitosis, as well as proper oral hygiene practices such as tongue cleaning, flossing, and selecting appropriate mouthwash and toothpaste. This narrative review summarises all possible AI olfaction in halitosis. [ABSTRACT FROM AUTHOR]

BACKGROUND: This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS: A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS: The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION: By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS: Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) is becoming an important part of our lives owing to increased data availability and improved power of computing. One of the recently launched modalities of AI, ChatGPT, is being enormously used worldwide for different types of tasks. In medical context, its use is being explored for clinical queries, academia, research help, etc. Further, literature suggests that parents seek information about health of their children using different Internet resources and would surely turn toward ChatGPT for the same, as this chatbot model is easy to use, generates “one” response, and is available without any subscription. ChatGPT generates a response using text cues and applying different algorithms on prepublished literature but is still in its naïve state; hence, it is imperative to validate the generated responses. Accordingly, we planned this study to determine the clarity, correctness, and completeness of some Frequently asked questions (FAQs) about child’s oral health, from a mother’s perspective. Methods: The study design was a vignette‑based survey and included a set of 23 questions, for which ChatGPT was interviewed from the perspective of an imaginary parent. The answers responded by ChatGPT were copied “verbatim,” and a Google survey form was designed. The survey form was validated and then sent to 15 pediatric dentists, and the responses were mainly collected on the Likert’s scale with a provision of one open‑ended question aiming to determine “what they would have added” to this generated response as an expert in the field. Results: The responses on Likert’s scale were condensed and values ≥4 were considered ‘adequate and acceptable’ while scores ≤3, were considered ‘inadequate’. The generated responses and comments mentioned by different respondents in the open‑ended question were critiqued in reference to the existing literature. Conclusion: Overall, the responses were found to be complete and logical and in clear language, with only some inadequacies being reported in few of the answers. [ABSTRACT FROM AUTHOR]

Background: Medical progress has often been hindered by the inherent limitations of human ability to process large volumes of data. The application of Artificial Intelligence (AI) can help overcome this constraint, particularly in the field of trauma. Purpose and Objectives: This study aims to analyze the application of artificial intelligence in the field of trauma through visualization tools, predict future research hotspots, and explore the potential applications of related technologies in the field of trauma, especially traumatic brain injury (TBI). Materials and Methods: Based on the Web of Science database, this study utilized visualization tools such as CiteSpace, VOSviewer, and SciMAT to create a knowledge map of AI applications in trauma from 1979 to 2022. Results: The analysis indicates that traumatic brain injury (TBI) will be a focal point for future research on the use of AI in trauma. Additionally, terms related to machine learning, including Artificial Neural Network and Convolutional Neural Network, are expected to be extensively employed in trauma detection and prediction. These targeted algorithms hold significant potential for groundbreaking applications in TBI. Conclusion: Artificial intelligence, especially machine learning techniques, will play a crucial role in the research and application of trauma, particularly TBI. In the future, these technologies are expected to provide new methods and perspectives for TBI detection, prediction, and treatment. [ABSTRACT FROM AUTHOR]

Digital pathology is gaining prominence among the researchers with developments in advanced imaging modalities and new technologies. Generative adversarial networks (GANs) are a recent development in the field of artificial intelligence and since their inception, have boosted considerable interest in digital pathology. GANs and their extensions have opened several ways to tackle many challenging histopathological image processing problems such as color normalization, virtual staining, ink removal, image enhancement, automatic feature extraction, segmentation of nuclei, domain adaptation and data augmentation. This paper reviews recent advances in histopathological image processing using GANs with special emphasis on the future perspectives related to the use of such a technique. The papers included in this review were retrieved by conducting a keyword search on Google Scholar and manually selecting the papers on the subject of H&E stained digital pathology images for histopathological image processing. In the first part, we describe recent literature that use GANs in various image preprocessing tasks such as stain normalization, virtual staining, image enhancement, ink removal, and data augmentation. In the second part, we describe literature that use GANs for image analysis, such as nuclei detection, segmentation, and feature extraction. This review illustrates the role of GANs in digital pathology with the objective to trigger new research on the application of generative models in future research in digital pathology informatics. [ABSTRACT FROM AUTHOR]

The article discusses the integration of artificial intelligence (AI) and simulation technologies in medical education. It highlights the role of the Conversational Generative Pre-trained Transformer (ChatGPT), an AI-powered conversational agent developed by OpenAI, in revolutionizing medical training. ChatGPT enhances prebriefing, scenario creation, and debriefing in simulation zones, providing guidance, realistic interactions, and tailored feedback to students. It also offers insights into student progress and competence development over time. The article emphasizes that ChatGPT enriches the educational experience, empowering students to become skilled, empathetic, and resilient healthcare practitioners. [Extracted from the article]

When using artificial intelligence systems, edge computing can allow end users to access data output in real-time without waiting for lengthy data-intensive analyses to be carried out externally.[[5]] Using edge computing also offers an additional opportunity for raw, sensitive data to be processed locally and rendered secure before sending it to the cloud. Examples of open-source systems include Apache Edgent,[[6]] OpenStack,[[7]] and EdgeX Foundry.[[8]] Edge computing business systems include Azure IoT Edge[[9]] and Amazon AWS Greengrass.[[10]] There are four essential technologies that enable edge computing.[[11]],[[12]] The first are virtual machines (VMs) and containers. Novelty of the LiveMicro telepathology System In their paper, Sacco et al. developed a new edge computing-based telepathology system called LiveMicro.[[1]] Their application was made accessible via a web browser. [Extracted from the article]

Background: Reported cases of uncontrolled use of pesticides and its produced effects by direct or indirect exposition, represent a high risk for human health. Therefore, in this paper, it is shown the results of the development and execution of an algorithm that predicts the possible effects in endocrine system in Fisher 344 (F344) rats, occasioned by ingestion of malathion. Methods: It was referred to ToxRefDB database in which different case studies in F344 rats exposed to malathion were collected. The experimental data were processed using Naïve Bayes (NB) machine learning classifier, which was subsequently optimized using genetic algorithms (GAs). The model was executed in an application with a graphical user interface programmed in C#. Results: There was a tendency to suffer bigger alterations, increasing levels in the parathyroid gland in dosages between 4 and 5 mg/kg/day, in contrast to the thyroid gland for doses between 739 and 868 mg/kg/day. It was showed a greater resistance for females to contract effects on the endocrine system by the ingestion of malathion. Females were more susceptible to suffer alterations in the pituitary gland with exposure times between 3 and 6 months. Conclusions: The prediction model based on NB classifiers allowed to analyze all the possible combinations of the studied variables and improving its accuracy using GAs. Excepting the pituitary gland, females demonstrated better resistance to contract effects by increasing levels on the rest of endocrine system glands. [ABSTRACT FROM AUTHOR]

Diffusion of Innovation (DOI) theory explains how, over a period of time, an idea or behavior diffuses (or spreads) through a targeted population or society. The major thing about adoption is that the people must perceive the idea, behavior, or product as new or innovative and it should be useful to them. The more the perceived benefit, the quicker is the diffusion. Same thing can be expected for adoption of new technology in healthcare also. In this paper, we have described two novel breast cancer screening technologies and have concluded that implementation of a new technology should consider the cultural aspect and mindset of local people so that quick diffusion of healthcare technology takes place among all segments in our society to make it a better world. [ABSTRACT FROM AUTHOR]

Dear Editor, Systematic reviews and meta-analyses play an invaluable role in the practice of evidence-based medicine.[[1]] Unfortunately, the process is time-consuming, on average requiring 67 weeks to sift through all available literature, collate relevant data, and analyze results to form conclusions.[[2]] However, recent advances in natural language processing (NLP) and machine learning have enabled "artificial intelligence" (AI) to "learn" through algorithms and assist with text classification and data extraction.[[3]] Semi-automation, with "human-in-the-loop" systems, can potentially assist with several labor-intensive steps of the systematic review process and make it faster.[[1]],[[3]] Nevertheless, skepticism as to the accuracy of automated tools exists which presents a barrier to their widespread acceptance.[[1]],[[3]] Two independent investigators conducted a systematic database search of PubMed and ClinicalTrials.gov. The time taken both for the article selection and data extraction was lower for the search conducted with AI assistance [Figure 1]. [Extracted from the article]

With the progress in refractive cataract surgery, more intraocular lens (IOL) power formulas have been introduced with the aim of reducing the postoperative refractive error. The postoperative IOL position is critical to IOL power calculations. Therefore, the improvements in postoperative IOL position prediction will enable better selection of IOL power and postoperative refraction. In the past, the postoperative IOL position was mainly predicted by preoperative anterior segment parameters such as preoperative axial length (AL), anterior chamber depth (ACD), and corneal curvature. In recent years, some novel methods including the intraoperative ACD, crystalline lens geometry, and artificial intelligence (AI) of prediction of postoperative IOL position have been reported. This article attempts to give a review about the research progress on prediction of the postoperative IOL position. [ABSTRACT FROM AUTHOR]

Resistance among educators to using artificial intelligence (AI) may yield significant disadvantages. Without AI, educators risk a higher workload, inefficient use of time, limited personalization, lack of feedback, inability to adapt, reduced student engagement, fewer job opportunities, and limited professional development. However, AI is designed to empower educators, enhance instructional practices, and foster professional growth. In the field of medicine, reluctance to adopt AI could hinder students from being equipped with vital skills in tackling the modern health-care situation. By leveraging AI tools, educators can create dynamic learning environments, producing better-trained health-care professionals ready to tackle evolving challenges. AI integration enhances education quality and prepares students for technological advancements in their medical careers, ensuring they are equipped to navigate the complexities of modern health-care practice. Thus, AI adoption in education is essential for maintaining the competitiveness and relevance of curricula in the AI age. [ABSTRACT FROM AUTHOR]

The article focuses on the Indo-United Kingdom (UK) collaboration in mental health, highlighting the growth of the initiative from a pilot involving 2 National Health Service (NHS) organizations to over 20 organizations across England and Wales. Topics include developing a long-term sustainable partnership; recent advances in clinical research in India and the UK; and strengths and challenges of the UK-India partnership in mental health.

This article discusses the potential applications of artificial intelligence (AI) in the field of obstetric anesthesia. AI can be used to optimize pre-procedure assessments, improve success rates in epidural and spinal anesthesia, predict and prevent hemodynamic crises, ensure optimal drug administration, and refine pain management. AI algorithms can be used to predict the need for cesarean sections and postoperative complications, assess airway difficulties in pregnant patients, guide needle placement for regional anesthesia, estimate optimal drug doses, and predict adverse events during surgery. However, there are still challenges to overcome, such as data collection, ethical and legal issues, and the need for further research and development. Overall, AI has the potential to enhance clinical decision-making and treatment in obstetric anesthesia, but further advancements are needed. [Extracted from the article]

Purpose: To quantifiably assess the diagnostic accuracy of Adven‑I, a proprietary artificial intelligence (AI)‑driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven‑i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non‑DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven‑i is the only AI system classifying retinal abnormalities into DR and non‑DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. Methods: The double‑blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground‑truth was generated on the basis of majority agreement among the readers. An arbitrator’s decision was regarded final if all three readers disagreed. Results: 2261 fundus images were analyzed by Adven‑i. The sensitivity and specificity of Adven‑i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. Conclusions and Relevance: Adven‑i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven‑i can be adopted to scale for mass screening in resource‑limited settings. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) in the field of ophthalmology has garnered significant attention for its potential to enhance the accuracy of eye disease diagnosis. This systematic review and meta-analysis aimed to comprehensively assess the impact of AI on diagnosing eye diseases through the synthesis of existing research. A systematic search of electronic databases was conducted to identify relevant studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Eligible studies were those that reported the diagnostic accuracy of AI in ophthalmic image diagnosis. The standardized mean difference (SMD) and mean difference (MD) were utilised as the effect size measures to evaluate AI performance. A total of 18 studies meeting the inclusion criteria were selected for the quantitative synthesis. Further, the meta-analysis revealed that AI exhibited a substantial positive impact on the accuracy of diagnosing eye diseases. The overall SMD across various diagnostic parameters indicated a statistically significant improvement (SMD = 0.88, 95% confidence interval [CI]: 0.71–1.05). Moreover, the MD of diagnostic values demonstrated significant enhancements, with an overall MD of −10.2 (95% CI: −12.1 to −8.3). The selected studies consistently demonstrated that AI achieved high accuracy levels, reinforcing its potential as a valuable tool in ophthalmic diagnosis. This study provides significant evidence supporting the significant positive impact of AI on diagnosing eye diseases. The synthesis of the selected studies underscores the high accuracy achieved by AI in ophthalmic image diagnosis, as indicated by the substantial SMD and MD improvements. These findings highlight the promising role of AI in ophthalmology, offering the potential to revolutionise the field and improve patient care through enhanced diagnostic precision. [ABSTRACT FROM AUTHOR]

The potential of artificial intelligence (AI) in diagnosing and managing sexually transmitted infections (STIs), nonvenereal genital diseases, and overall sexual health is immense. AI shows promise in STI screening and diagnosis through image recognition and patient data analysis, potentially increasing diagnostic accuracy while ensuring inclusivity. AI can fuel the transformation of e-health and direct-to-consumer services, enhancing targeted screening and personalized interventions while improving the user-friendliness of services. There is a significant role for AI in sexual education, particularly its use in interactive, empathetic chatbots. AI's integration into health care as a decision support tool for primary health-care providers can boost real-time diagnostic accuracy. Furthermore, AI's use in big data can enhance real-time epidemiology, predictive analysis, and directed interventions at population levels. However, challenges such as real-world diagnostic accuracy, liability, privacy concerns, and ethical dilemmas persist. Future directions include an emphasis on inclusivity, language accommodation, and swift research-to-practice transitions. Collaboration among policymakers, researchers, and health-care providers is needed to leverage AI's transformative potential in sexual health. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a transformative force in the field of diagnostic dermatology, offering unprecedented capabilities in image recognition and data analysis. Despite its promise, the integration of AI into clinical practice faces multifaceted challenges that span technical, ethical, and regulatory domains. This article provides a narrative overview of the current state of AI in dermatology, tracing its historical evolution from early diagnostic tools to contemporary hybrid supervised models. We identify and categorize six critical challenges: data quality and quantity, algorithmic development and explainability, ethical considerations, clinical workflow integration, regulatory frameworks, and stakeholder collaboration. Each challenge is dissected from the perspectives of academia, industry, and healthcare providers, offering actionable recommendations for future research and implementation. We also highlight the paradigm shift in AI research, emphasizing the potential of transformer architectures in revolutionizing diagnostic methodologies. By addressing the challenges and harnessing the latest advancements, AI has the potential to significantly impact diagnostic accuracy and patient outcomes in dermatology. [ABSTRACT FROM AUTHOR]