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This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the "big picture" considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query "artificial intelligence and teaching" over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field. [ABSTRACT FROM AUTHOR]

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI's significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions. [ABSTRACT FROM AUTHOR]

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them. [ABSTRACT FROM AUTHOR]

The increasing integration of artificial intelligence (AI) in healthcare, particularly in vascular and interventional radiology (VIR), has opened avenues for enhanced efficiency and precision. This narrative review delves into the potential applications of large language models (LLMs) in VIR, with a focus on Chat Generative Pre-Trained Transformer (ChatGPT) and similar models. LLMs, designed for natural language processing, exhibit promising capabilities in clinical decision-making, workflow optimization, education, and patient-centered care. The discussion highlights LLMs' ability to analyze extensive medical literature, aiding radiologists in making informed decisions. Moreover, their role in improving clinical workflow, automating report generation, and intelligent patient scheduling is explored. This article also examines LLMs' impact on VIR education, presenting them as valuable tools for trainees. Additionally, the integration of LLMs into patient education processes is examined, highlighting their potential to enhance patient-centered care through simplified and accurate medical information dissemination. Despite these potentials, this paper discusses challenges and ethical considerations, including AI over-reliance, potential misinformation, and biases. The scarcity of comprehensive VIR datasets and the need for ongoing monitoring and interdisciplinary collaboration are also emphasized. Advocating for a balanced approach, the combination of LLMs with computer vision AI models addresses the inherently visual nature of VIR. Overall, while the widespread implementation of LLMs in VIR may be premature, their potential to improve various aspects of the discipline is undeniable. Recognizing challenges and ethical considerations, fostering collaboration, and adhering to ethical standards are essential for unlocking the full potential of LLMs in VIR, ushering in a new era of healthcare delivery and innovation. [ABSTRACT FROM AUTHOR]

ChatGPT (GPT-3.5), an intelligent Web-based tool capable of conducting text-based conversations akin to human interaction across various subjects, has recently gained significant popularity. This surge in interest has led researchers to examine its impact on numerous fields, including education. The aim of this paper is to investigate the perceptions of undergraduate students regarding ChatGPT's utility in academic environments, focusing on its strengths, weaknesses, opportunities, and threats. It responds to emerging challenges in educational technology, such as the integration of artificial intelligence in teaching and learning processes. The study involved 257 students from two university departments in Greece—namely primary and early childhood education pre-service teachers. Data were collected using a structured questionnaire. Various methods were employed for data analysis, including descriptive statistics, inferential analysis, K-means clustering, and decision trees. Additional insights were obtained from a subset of students who undertook a project in an elective course, detailing the types of inquiries made to ChatGPT and their reasons for recommending (or not recommending) it to their peers. The findings offer valuable insights for tutors, researchers, educational policymakers, and ChatGPT developers. To the best of the authors' knowledge, these issues have not been dealt with by other researchers. [ABSTRACT FROM AUTHOR]

The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world's first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs. [ABSTRACT FROM AUTHOR]

In this editorial essay, we explore the potential of large language models (LLMs) for conceptual work and for developing theory papers within the field of organization and management studies. We offer a technically informed, but at the same time accessible, analysis of the generative AI technology behind tools such as Bing Chat, ChatGPT, Claude and Gemini, to name the most prominent LLMs currently in use. Our aim in this essay is to go beyond prior work and to provide a more nuanced reflection on the possible application of such technology for the different activities and reasoning processes that constitute theorizing within our domain of scholarly inquiry. Specifically, we highlight ways in which LLMs might augment our theorizing, but we also point out the fundamental constraints in how contemporary LLMs 'reason', setting considerable limits to what such tools might produce as 'conceptual' or 'theoretical' outputs. Given worrisome trade-offs in their use, we urge authors to be careful and reflexive when they use LLMs to assist (parts of) their theorizing, and to transparently disclose this use in their manuscripts. We conclude the essay with a statement of Organization Theory 's editorial policy on the use of LLMs. [ABSTRACT FROM AUTHOR]

This paper investigates the validity of ChatGPT as a tool to generate meaningful input for the serious game design process. Input is collected from game designers, students and teachers via surveys, individual interviews and group discussions inspired by a description of a simple educational drilling game and its context of use. In these mixed methods experiments ChatGPT 3.5 and 4.0 is prompted with the same description to validate findings with expert participants. In addition, the impact on the models' suggestions from integrating the expert's role (e.g., "Answer as if you were a teacher.", "game designer", or a "student") into the prompt is investigated. The findings show that ChatGPT can produce statistically similar input, depending on the group of experts. ChatGPT 3.5 outperforms 4.0 with the student input. The integration of expert's role in prompt is found to be unreliable, and necessary only with game designer input with the version 3.5 of ChatGPT. The practical implications are that multiple ChatGPT versions should be used when collecting input. In addition, it is shown that experts can provide unique insight to the development process. This research opens the discussion on the trustworthiness of ChatGPT generated input for serious game development. [ABSTRACT FROM AUTHOR]

This article explores the potential use of dual-process theories of thought as architectures for developing neuro-symbolic AI models. It discusses the limitations of large language models (LLMs) in handling structured tasks that rely on symbolic reasoning and proposes integrating LLMs with symbolic systems to address these limitations. The article explores different models for the interaction between rapid, associative thinking (System 1) and deliberate, rule-based thinking (System 2), and suggests that these architectures could be useful in developing and testing symbolic/subsymbolic systems integration in AI. It emphasizes the need for further exploration of the interaction between the two systems in neuro-symbolic architectures, while also prioritizing minimizing errors and achieving optimal performance. The document contains a list of references and citations from various sources related to bias in artificial intelligence and decision-making processes, providing additional sources for further research. [Extracted from the article]

This research intends to demonstrate the potential integration of large language models (LLMs) in accounting practices in Jordan. It is a mixed methods study that employs semi-structured interviews and content analysis of published financial reports. A total of 13 semi-structured interviews are conducted with various accounting professionals, such as accountant, financial analyst, financial controller, auditor, tax consultant, and finance manager. In addition, the study includes a thorough content analysis of financial reports, which reveals a compelling pattern highlighting the distinct narrative content richness prevalent across financial, industrial, and service sectors. The results emphasize the need for sector-specific adaptation, suggesting a paradigm shift in financial reporting practices. This study represents the initial empirical exploration in Jordan. It serves as a bridge between theory and application, offering both theoretical insights and practical guidance for accounting professionals. Ultimately, the study envisions a future where LLMs significantly enrich financial reporting practices across diverse sectors. [ABSTRACT FROM AUTHOR]

Background: ChatGPT is a large-scale language model that utilizes artificial intelligence (AI) to answer a broad range of scientific inquiries, create clinical scenarios, and evaluate educational programs. While its use offers numerous advantages, it also presents several challenges. Objectives: The aim of this study is to conduct a comprehensive review of ChatGPT's functionality and explore the benefits, challenges, existing solutions, and future prospects of using AI in medical education. Method: A comprehensive literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. The search phrases used were ChatGPT, Artificial Intelligence, Chatbot, Medical Education, and large language models (LLMs). Results: The application of ChatGPT in medical education offers several advantages, such as enhanced quality of interaction between medical students and patients, improved education quality, enhanced research opportunities, personalized learning, virtual patient simulations, and cost-effectiveness. However, there are also critical challenges, such as ethical and transparency concerns, limited access to reliable databases, restricted information availability after 2021, limited development of students' critical thinking ability, and the risk of generating AI hallucinations. Conclusions: Artificial intelligence models have become a popular tool for researchers to access scientific resources, comprehend articles and textbooks, and create scientific texts. ChatGPT has been used extensively in medicine and medical education in a short period. It is essential to weigh the benefits and challenges, use expert supervision, conduct frequent assessments, and provide feedback reviews to guarantee its efficacy. Although this technology should not replace human labor, it is essential to prepare for the changes brought by AI and create appropriate guidelines and curricula by reviewing existing solutions and conducting extensive studies. [ABSTRACT FROM AUTHOR]

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli's (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell's (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field. [ABSTRACT FROM AUTHOR]

Data clumps, groups of variables that repeatedly appear together across different parts of a software system, are indicative of poor code structure and can lead to potential issues such as maintenance challenges, testing complexity, and scalability concerns, among others. Addressing this, our study introduces an innovative AI-driven pipeline specifically designed for the refactoring of data clumps in software repositories. This pipeline leverages the capabilities of Large Language Models (LLM), such as ChatGPT, to automate the detection and resolution of data clumps, thereby enhancing code quality and maintainability. In developing this pipeline, we have taken into consideration the new European Union (EU)-Artificial Intelligence (AI) Act, ensuring that our pipeline complies with the latest regulatory requirements and ethical standards for use of AI in software development by outsourcing decisions to a human in the loop. Preliminary experiments utilizing ChatGPT were conducted to validate the effectiveness and efficiency of our approach. These tests demonstrate promising results in identifying and refactoring data clumps, but also the challenges using LLMs. [ABSTRACT FROM AUTHOR]

The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements. [ABSTRACT FROM AUTHOR]

Study Design: Comparative Analysis and Narrative Review. Objective: To assess and compare ChatGPT's responses to the clinical questions and recommendations proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We explore the advantages and disadvantages of ChatGPT's responses through an updated literature review on spinal stenosis. Methods: We prompted ChatGPT with questions from the NASS Evidence-based Clinical Guidelines for LSS and compared its generated responses with the recommendations provided by the guidelines. A review of the literature was performed via PubMed, OVID, and Cochrane on the diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 2023. Results: 14 questions proposed by the NASS guidelines for LSS were uploaded into ChatGPT and directly compared to the responses offered by NASS. Three questions were on the definition and history of LSS, one on diagnostic tests, seven on non-surgical interventions and three on surgical interventions. The review process found 40 articles that were selected for inclusion that helped corroborate or contradict the responses that were generated by ChatGPT. Conclusions: ChatGPT's responses were similar to findings in the current literature on LSS. These results demonstrate the potential for implementing ChatGPT into the spine surgeon's workplace as a means of supporting the decision-making process for LSS diagnosis and treatment. However, our narrative summary only provides a limited literature review and additional research is needed to standardize our findings as means of validating ChatGPT's use in the clinical space. [ABSTRACT FROM AUTHOR]

PURPOSE Worldwide clinical knowledge is expanding rapidly, but physicians have sparse time to review scientific literature. Large language models (eg, Chat Generative Pretrained Transformer [ChatGPT]), might help summarize and prioritize research articles to review. However, large language models sometimes “hallucinate” incorrect information. METHODS We evaluated ChatGPT’s ability to summarize 140 peer-reviewed abstracts from 14 journals. Physicians rated the quality, accuracy, and bias of the ChatGPT summaries. We also compared human ratings of relevance to various areas of medicine to ChatGPT relevance ratings. RESULTS ChatGPT produced summaries that were 70% shorter (mean abstract length of 2,438 characters decreased to 739 characters). Summaries were nevertheless rated as high quality (median score 90, interquartile range [IQR] 87.0-92.5; scale 0-100), high accuracy (median 92.5, IQR 89.0-95.0), and low bias (median 0, IQR 0-7.5). Serious inaccuracies and hallucinations were uncommon. Classification of the relevance of entire journals to various fields of medicine closely mirrored physician classifications (nonlinear standard error of the regression [SER] 8.6 on a scale of 0-100). However, relevance classification for individual articles was much more modest (SER 22.3). CONCLUSIONS Summaries generated by ChatGPT were 70% shorter than mean abstract length and were characterized by high quality, high accuracy, and low bias. Conversely, ChatGPT had modest ability to classify the relevance of articles to medical specialties. We suggest that ChatGPT can help family physicians accelerate review of the scientific literature and have developed software (pyJournalWatch) to support this application. Life-critical medical decisions should remain based on full, critical, and thoughtful evaluation of the full text of research articles in context with clinical guidelines. [ABSTRACT FROM AUTHOR]

The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students' performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process. [ABSTRACT FROM AUTHOR]

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions. [ABSTRACT FROM AUTHOR]

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology. Plain language summary: Large language models in gastroenterology: a simplified overview for clinicians This text discusses the recent advancements in large language models (LLMs), like ChatGPT, which have significantly advanced artificial intelligence. These models can create specific, high-quality text responses without needing extensive training data or complex programming. They show great promise in transforming various aspects of clinical healthcare, particularly in improving patient care, medical education, and research. This article focuses on how LLMs can be applied in the field of gastroenterology. It explains the technical aspects of LLMs, their strengths and weaknesses, and how to use them effectively and safely. The text also explores how LLMs could be used in clinical practice, education, and research in gastroenterology. Finally, it discusses the challenges in implementing these models and the ongoing efforts to overcome them, aiming to provide gastroenterologists with the basic knowledge needed to engage more actively in the development and use of this emerging technology. [ABSTRACT FROM AUTHOR]

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and timeconsuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships. [ABSTRACT FROM AUTHOR]

In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice. [ABSTRACT FROM AUTHOR]

Currently, many generative Artificial Intelligence (AI) tools are being integrated into the educational technology landscape for instructors. Our paper examines the potential and challenges of using Large Language Models (LLMs) to code student-generated content in online discussions based on intended learning outcomes and how instructors could use this to assess the intended and enacted learning design. If instructors were to rely on LLMs as a means of assessment, the reliability of these models to code the data accurately is crucial. Employing a diverse set of LLMs from the GPT family and prompting techniques on an asynchronous online discussion dataset from a blended-learning bachelor-level course, our research examines the reliability of AI-supported coding in educational research. Findings reveal that while AI-supported coding demonstrates efficiency, achieving substantial, moderate agreement with human coding for specific nuanced and context-dependent codes is challenging. Moreover, the high cost, token limits, and the advanced necessary skills needed to write API scripts might limit the usability of AI-driven coding. Finally, implementation would require specific parameterization techniques based on the class and may not be feasible for widespread implementation. Our study underscores the importance of transparency in AI coding methodologies and the need for a hybrid approach that integrates human judgement to ensure data accuracy and interpretability. In addition, it contributes to the knowledge base about the reliability of LLMs to code real, small datasets using complex codes that are common in the instructor's practice and explores the potential and challenges of using these models for assessment purposes.

BackgroundNatural language processing tools are becoming increasingly adopted in multiple industries worldwide. They have shown promising results however their use in the field of surgery is under-recognised. Many trials have assessed these benefits in small settings with promising results before large scale adoption can be considered in surgery. This study aims to review the current research and insights into the potential for implementation of natural language processing tools into surgery.MethodsA narrative review was conducted following a computer-assisted literature search on Medline, EMBASE and Google Scholar databases. Papers related to natural language processing tools and consideration into their use for surgery were considered.ResultsCurrent applications of natural language processing tools within surgery are limited. From the literature, there is evidence of potential improvement in surgical capability and service delivery, such as through the use of these technologies to streamline processes including surgical triaging, data collection and auditing, surgical communication and documentation. Additionally, there is potential to extend these capabilities to surgical academia to improve processes in surgical research and allow innovation in the development of educational resources. Despite these outcomes, the evidence to support these findings are challenged by small sample sizes with limited applicability to broader settings.ConclusionWith the increasing adoption of natural language processing technology, such as in popular forms like ChatGPT, there has been increasing research in the use of these tools within surgery to improve surgical workflow and efficiency. This review highlights multifaceted applications of natural language processing within surgery, albeit with clear limitations due to the infancy of the infrastructure available to leverage these technologies. There remains room for more rigorous research into broader capability of natural language processing technology within the field of surgery and the need for cross-sectoral collaboration to understand the ways in which these algorithms can best be integrated.

Large Language Models (LLMs) have grown in popularity in recent years and are now employed in a variety of software engineering domains thanks to their Natural Language Processing (NLP) capabilities, which include source code generation, understanding, and documentation. Selecting the appropriate model for source code generation presents a problem to developers as more and more powerful LLMs become available. While some studies have evaluated Copilot or ChatGPT, there is a lack of research on how developers can choose from available LLMs, which is a key factor in the growing set of available models and services. It is crucial to know if a model is capable of generating useful source code that meets the quality requirements and if the developers will be able to use the generated code. Regarding these factors, one has to decide which model to utilize during everyday tasks. This paper shows a methodology to compare such models by demonstrating an actual comparison of two models. Subsequently, we investigated the functional and non-functional qualities of the code synthesized by the models on a program synthesis benchmark containing 25 tasks. On average, the functional testing shows that ChatGPT generated 17 perfect solutions, while Copilot could only solve 13. The non-functional analysis reflected that both models generated good quality code, however, both have characteristic code smells. Our evaluation shows that ChatGPT performs better using this methodology, which is supported by human reviewers who evaluated the generated code by hand.

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence , strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to what a human might produce (meaning-semblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis : SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding. • Statistics-of-Occurrence Models (SOMs) are defined. • SOMs include Vector Space Models and generative models such as ChatGPT. • Systems with semantic understanding must function to respect semantic content. • SOMs are unlikely to meet this Functioning Criterion. • SOMs thus fail to possess semantic understanding. • SOMs' behavior can be better explained without attributing semantic understanding. • Causal analyses of SOMs may help support semantic understanding in the future. [ABSTRACT FROM AUTHOR]

Medicine is entering a new era in which artificial intelligence (AI) and deep learning have a measurable impact on patient care. This impact is especially evident in cardiovascular medicine. While the purpose of this short opinion paper is not to provide an in-depth review of the many applications of AI in cardiovascular medicine, we summarize some of the important advances that have taken place in this domain.

The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.