Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review

JMIR Medical Education, Год журнала: 2023, Номер 9, С. e48785 - e48785

Опубликована: Сен. 28, 2023

Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT Bard, can generate new content have a wide range of possible applications.

Язык: Английский

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A scoping review of artificial intelligence in medical education: BEME Guide No. 84

Medical Teacher, Год журнала: 2024, Номер 46(4), С. 446 - 470

Опубликована: Фев. 29, 2024

Background Artificial Intelligence (AI) is rapidly transforming healthcare, and there a critical need for nuanced understanding of how AI reshaping teaching, learning, educational practice in medical education. This review aimed to map the literature regarding applications education, core areas findings, potential candidates formal systematic gaps future research.

Язык: Английский

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The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review

Smart Learning Environments, Год журнала: 2024, Номер 11(1)

Опубликована: Июнь 18, 2024

Abstract The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance learning aids. Despite examination ethical concerns associated with these technologies, there is a noticeable gap in investigations on how issues AI contribute to students’ over-reliance systems, such affects cognitive abilities. Overreliance occurs when users accept AI-generated recommendations without question, leading errors task performance context decision-making. This typically arises individuals struggle assess reliability or much trust place its suggestions. systematic review investigates particularly those embedded generative models for academic learning, their critical capabilities including decision-making, thinking, analytical reasoning. By using Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, our evaluated body literature addressing contributing factors effects contexts. comprehensive spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, Web Science. Our findings indicate that stemming impacts abilities, as increasingly favor fast optimal solutions over slow ones constrained by practicality. tendency explains why prefer efficient shortcuts, heuristics, even amidst presented technologies.

Язык: Английский

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Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers

International Endodontic Journal, Год журнала: 2023, Номер 57(1), С. 108 - 113

Опубликована: Окт. 9, 2023

Chatbot Generative Pre-trained Transformer (ChatGPT) is a generative artificial intelligence (AI) software based on large language models (LLMs), designed to simulate human conversations and generate novel content the training data it has been exposed to. The aim of this study was evaluate consistency accuracy ChatGPT-generated answers clinical questions in endodontics, compared provided by experts.Ninety-one dichotomous (yes/no) were categorized into three levels difficulty. Twenty randomly selected from each difficulty level. Sixty generated ChatGPT for question. Two endodontic experts independently answered 60 questions. Statistical analysis performed using SPSS program calculate experts. Confidence intervals (95%) standard deviations used estimate variability.The showed high (85.44%). No significant differences found question In terms answer accuracy, achieved an average 57.33%. However, observed difficulty, with lower easier questions.Currently, not capable replacing dentists decision-making. As ChatGPT's performance improves through deep learning, expected become more useful effective field endodontics. careful attention ongoing evaluation are needed ensure its reliability safety

Язык: Английский

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Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses

BMC Medical Education, Год журнала: 2024, Номер 24(1)

Опубликована: Фев. 14, 2024

Abstract Background Large language models like ChatGPT have revolutionized the field of natural processing with their capability to comprehend and generate textual content, showing great potential play a role in medical education. This study aimed quantitatively evaluate comprehensively analysis performance on three types national examinations China, including National Medical Licensing Examination (NMLE), Pharmacist (NPLE), Nurse (NNLE). Methods We collected questions from Chinese NMLE, NPLE NNLE year 2017 2021. In NMLE NPLE, each exam consists 4 units, while NNLE, 2 units. The figures, tables or chemical structure were manually identified excluded by clinician. applied direct instruction strategy via multiple prompts force clear answer distinguish between single-choice multiple-choice questions. Results failed pass accuracy threshold 0.6 any over five years. Specifically, highest recorded was 0.5467, which attained both 2018 0.5599 2017. most impressive result shown 2017, an 0.5897, is also our entire evaluation. ChatGPT’s showed no significant difference different but question types. performed well range subject areas, clinical epidemiology, human parasitology, dermatology, as various topics such molecules, health management prevention, diagnosis screening. Conclusions These results indicate spanning show large future high-quality data will be required improve performance.

Язык: Английский

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ChatGPT: The End of Online Exam Integrity?

Education Sciences, Год журнала: 2024, Номер 14(6), С. 656 - 656

Опубликована: Июнь 17, 2024

This study addresses the significant challenge posed by use of Large Language Models (LLMs) such as ChatGPT on integrity online examinations, focusing how these models can undermine academic honesty demonstrating their latent and advanced reasoning capabilities. An iterative self-reflective strategy was developed for invoking critical thinking higher-order in LLMs when responding to complex multimodal exam questions involving both visual textual data. The proposed demonstrated evaluated real subject experts performance (GPT-4) with vision estimated an additional dataset 600 text descriptions questions. results indicate that invoke multi-hop capabilities within LLMs, effectively steering them towards correct answers integrating from each modality into final response. Meanwhile, considerable proficiency being able answer across 12 subjects. These findings prior assertions about limitations emphasise need robust security measures proctoring systems more sophisticated mitigate potential misconduct enabled AI technologies.

Язык: Английский

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Performance of Large Language Models (ChatGPT, Bing Search, and Google Bard) in Solving Case Vignettes in Physiology

Cureus, Год журнала: 2023, Номер unknown

Опубликована: Авг. 4, 2023

Background Large language models (LLMs) have emerged as powerful tools capable of processing and generating human-like text. These LLMs, such ChatGPT (OpenAI Incorporated, Mission District, San Francisco, United States), Google Bard (Alphabet Inc., CA, US), Microsoft Bing (Microsoft Corporation, WA, been applied across various domains, demonstrating their potential to assist in solving complex tasks improving information accessibility. However, application case vignettes physiology has not explored. This study aimed assess the performance three namely, (3.5; free research version), (Experiment), (precise), answering cases Physiology. Methods cross-sectional was conducted July 2023. A total 77 were prepared by two physiologists validated other content experts. presented each LLM, responses collected. Two independently rated answers provided LLMs based on accuracy. The ratings measured a scale from 0 4 according structure observed learning outcome (pre-structural = 0, uni-structural 1, multi-structural 2, relational 3, extended-abstract). scores among compared Friedman's test inter-observer agreement checked intraclass correlation coefficient (ICC). Results overall for ChatGPT, Bing, study, with cases, found be 3.19±0.3, 2.15±0.6, 2.91±0.5, respectively, p<0.0001. Hence, 3.5 (free version) obtained highest score, (Precise) had lowest (Experiment) fell between terms performance. average ICC values 0.858 (95% CI: 0.777 0.91, p<0.0001), 0.975 0.961 0.984, 0.964 0.944 0.977, respectively. Conclusion outperformed physiology. students teachers may think about choosing educational purposes accordingly case-based Further exploration capabilities is needed adopting those medical education support clinical decision-making.

Язык: Английский

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A systematic review of large language models and their implications in medical education

Medical Education, Год журнала: 2024, Номер unknown

Опубликована: Апрель 19, 2024

Abstract Introduction In the past year, use of large language models (LLMs) has generated significant interest and excitement because their potential to revolutionise various fields, including medical education for aspiring physicians. Although students undergo a demanding educational process become competent health care professionals, emergence LLMs presents promising solution challenges like information overload, time constraints pressure on clinical educators. However, integrating into raises critical concerns educators, professionals students. This systematic review aims explore LLM applications in education, specifically impact students' learning experiences. Methods A search was performed PubMed, Web Science Embase articles discussing using selected keywords related from ChatGPT's debut until February 2024. Only available full text or English were reviewed. The credibility each study critically appraised by two independent reviewers. Results identified 166 studies, which 40 found be relevant study. Among key themes included capabilities, benefits such as personalised regarding content accuracy. Importantly, 42.5% these studies evaluated novel way, ChatGPT, contexts exams clinical/biomedical information, highlighting replicating human‐level performance knowledge. remaining broadly discussed prospective role reflecting keen future despite current constraints. Conclusions responsible implementation offers opportunity enhance ensuring accuracy, emphasising skill‐building maintaining ethical safeguards are crucial. Continuous evaluation interdisciplinary collaboration essential appropriate integration education.

Язык: Английский

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Interactive computer-aided diagnosis on medical image using large language models

Communications Engineering, Год журнала: 2024, Номер 3(1)

Опубликована: Сен. 17, 2024

Computer-aided diagnosis (CAD) has advanced medical image analysis, while large language models (LLMs) have shown potential in clinical applications. However, LLMs struggle to interpret images, which are critical for decision-making. Here we show a strategy integrating with CAD networks. The framework uses LLMs' knowledge and reasoning enhance network outputs, such as diagnosis, lesion segmentation, report generation, by summarizing information natural language. generated reports of higher quality can improve the performance vision-based models. In chest X-rays, an LLM using ChatGPT improved 16.42 percentage points compared state-of-the-art models, GPT-3 provided 15.00 point F1-score improvement. Our allows accurate generation creates patient-friendly interactive system, unlike conventional systems only understood professionals. This approach revolutionize decision-making patient communication.

Язык: Английский

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A scoping review of ChatGPT's role in healthcare education and research

Nurse Education Today, Год журнала: 2024, Номер 135, С. 106121 - 106121

Опубликована: Фев. 6, 2024

To examine and consolidate literature regarding the advantages disadvantages of utilizing ChatGPT in healthcare education research. We searched seven electronic databases (PubMed/Medline, CINAHL, Embase, PsycINFO, Scopus, ProQuest Dissertations Theses Global, Web Science) from November 2022 until September 2023. This scoping review adhered to Arksey O'Malley's framework followed reporting guidelines outlined PRISMA-ScR checklist. For analysis, we employed Thomas Harden's thematic synthesis framework. A total 100 studies were included. An overarching theme, "Forging Future: Bridging Theory Integration ChatGPT" emerged, accompanied by two main themes (1) Enhancing Healthcare Education, Research, Writing with ChatGPT, (2) Controversies Concerns about Education Research Writing, subthemes. Our underscores importance acknowledging legitimate concerns related potential misuse such as 'ChatGPT hallucinations', its limited understanding specialized knowledge, impact on teaching methods assessments, confidentiality security risks, controversial practice crediting it a co-author scientific papers, among other considerations. Furthermore, our also recognizes urgency establishing timely regulations, along active engagement relevant stakeholders, ensure responsible safe implementation ChatGPT's capabilities. advocate for use cross-verification techniques enhance precision reliability generated content, adaptation higher curricula incorporate potential, educators' need familiarize themselves technology improve their literacy approaches, development innovative detect usage. data protection measures should be prioritized when employing transparent becomes crucial integrating into academic writing.

Язык: Английский

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