The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)–driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

https://mededu.jmir.org/2023/1/e48291/PDF

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society. 

https://doi.org/10.1016/j.jksuci.2023.101675

Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions

There are many patient groups who may refuse blood products; the most well known amongst them is the Jehovah's Witness faith. Treatment of anaemia and bleeding in such patients presents a challenge to medical, anaesthetic, and surgical teams. This review examines the perioperative issues and management of Jehovah's Witnesses. The history and beliefs of Jehovah's Witnesses are outlined together with their impact on ethics and the law, and different management options throughout the perioperative period are discussed.

https://ct1.medstarhealth.org/content/uploads/sites/10/2015/10/perioperative-jehovahs-witnesses-a-review.pdf

Perioperative Jehovah's Witnesses: a review

INTRODUCTION
Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was to compare the performance of orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on orthopaedic assessment examinations. A secondary objective was to perform a subgroup analysis comparing the performance of each group on questions that included image interpretation versus text-only questions.


METHODS
The ResStudy orthopaedic examination question bank was used as the primary source of questions. One hundred eighty questions and answer choices from nine different orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. ChatGPT did not have consistently available image interpretation, so no images were directly provided to either AI format. Answers were recorded as correct versus incorrect by the chatbot, and resident performance was recorded based on user data provided by ResStudy.


RESULTS
Overall, ChatGPT-3.5, GPT-4, and ortho residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference among the three groups in testing success, with ortho residents scoring higher than ChatGPT-3.5 and GPT-4 (P < 0.001 and P < 0.001). GPT-4 scored higher than ChatGPT-3.5 (P = 0.002). A subgroup analysis was performed by dividing questions into question stems without images and question stems with images. ChatGPT-3.5 was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when comparing text-only questions versus questions with images. Residents were 72.6% versus 75.5% correct with text-only questions versus questions with images, with no significant difference (P = 0.302).


CONCLUSION
Orthopaedic residents were able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 and GPT-4 performed better on text-only questions than questions with images. It is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of Orthopaedic Surgery written examination.

Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations.

In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI’s evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Letter to the EditorEvaluating the performance of ChatGPT in medical physiology university examination of phase I MBBSMahila Subramani, Ilham Jaleel, and Surapaneni Krishna MohanMahila SubramaniDepartment of Physiology, Panimalar Medical College Hospital and Research Institute, Varadharajapuram, Chennai, Tamil Nadu, India, Ilham JaleelDepartment of Physiology, Panimalar Medical College Hospital and Research Institute, Varadharajapuram, Chennai, Tamil Nadu, India, and Surapaneni Krishna MohanDepartments of Biochemistry, Medical Education, Molecular Virology, Research, Clinical Skills and Simulation, Panimalar Medical College Hospital and Research Institute, Varadharajapuram, Chennai, Tamil Nadu, IndiaPublished Online:27 Mar 2023https://doi.org/10.1152/advan.00036.2023MoreSectionsPDF (161 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat San Francisco-based OpenAI Inc/LP developed and launched the revolutionary and enormous language model powered by an artificial intelligence (AI) chatbot system named ChatGPT on November 30, 2022, which generates humanoid responses to varied and diverse natural queries (1). Within a week of its release, ChatGPT has crossed the over 1 million user mark (2). This ChatGPT (Chat Generative Pre-trained Transformer) is a freely accessible conversational AI tool that was developed on the concept of reinforcement learning from human feedback (3). Ever since the integration of ChatGPT, the Microsoft Bing search engine has reached an impressive milestone of more than 100 million daily active users and is soon to be made instantly available for millions of users online along with healthcare professionals and students (4). In recognition of its high impact, the efficiency of ChatGPT has also been tested in the United States Medical Licensing Examination (USMLE), and it has successfully cleared the test (5). Although ChatGPT cannot replace human thinking and reasoning, its usability in several fields including medical education and clinical decision making is worthy of more research that can assess its accuracy and utility in the medical field (6).We evaluated the performance of ChatGPT on February 14, 2023 in the Medical Physiology Examination of Phase I MBBS. We used theory questions and multiple choice questions (MCQs) for this evaluation. The question paper is evaluated for 100 marks, with 2 essays worth 15 marks each, 10 short notes worth 5 marks each, and 20 MCQs. We typed the questions in the search bar of ChatGPT and allowed it to generate the response. ChatGPT generated the responses for the theory questions with all essays and short notes. The MCQs were also entered in the search bar, and ChatGPT provided the right answer along with an explanation. All the responses were saved and documented. The answers were later evaluated by the faculty of the department of physiology.The first essay was a case-based question on myocardial infarction with four subquestions. ChatGPT gave the right diagnosis, and the answers to the subsequent subquestions were also appreciable. ChatGPT also fared well in other short notes. It gave right answers to all questions in pointwise fashion, encompassing all appropriate examples and important information that examiners look for. However, ChatGPT could not generate relevant diagrams for the answers simultaneously. In regard to MCQs, ChatGPT gave the right answer for 17 MCQ out of 20 MCQs, along with a good explanation for every question. The answers were evaluated and cross-checked by expert faculty in medical physiology. Overall, ChatGPT cleared the Physiology University Examination with a distinction (>75% of marks).In the fast-evolving digital era, ChatGPT is emerging as one of the most rapid and easily usable AI technologies that can be utilized to assist physicians and other healthcare professionals in diagnosis, critical decision making, and planning of appropriate treatment strategies. These digital assistants have been shown to pass medical exams with commendable scores and can thus serve as a useful tool to reframe medical education and assessment that is soon to be advantaged by AI. Although AI has the potential to revamp medical systems, it could still lead to automation bias without clinician validity. Thus, to depend on AI as an innovative tool for education or medical practices, the accuracy, source, and reliability of the information should be authenticated by expert faculties and clinicians before these are put into practice.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the authors.AUTHOR CONTRIBUTIONSS.K.M. conceived and designed research; S.K.M. performed experiments; M.S., I.J., and S.K.M. analyzed data; M.S., I.J., and S.K.M. interpreted results of experiments; S.K.M. prepared figures; S.K.M. drafted manuscript; M.S., I.J., and S.K.M. edited and revised manuscript; M.S., I.J., and S.K.M. approved final version of manuscript.REFERENCES1. What is ChatGPT and how can you use it? https://www.searchenginejournal.com/what-is-chatgpt/473664/#close [2023 Mar 9].Google Scholar2. OpenAI’s ChatGPT, launched last week, used by over 1 million in 6 days: CEO. https://www.hindustantimes.com/technology/openais-chatgpt-launched-last-week-used-by-over-1-million-in-6-days-ceo-101670234260469.html [2023 Mar 9].Google Scholar3. Thorp HH. ChatGPT is fun, but not an author. Science 379: 313, 2023. doi:10.1126/science.adg7879. Crossref | PubMed | Google Scholar4. The ChatGPT effect: Microsoft claims Bing has now 100 million daily users. https://timesofindia.indiatimes.com/gadgets-news/the-chatgpt-effect-microsoft-claims-bing-has-now-100-million-daily-users/articleshow/98522286.cms?from=mdr [2023 Mar 9].Google Scholar5. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, Madriaga M, Aggabao R, Diaz-Candido G, Maningo J, Tseng V. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health 2: e0000198, 2023. doi:10.1371/journal.pdig.0000198. Crossref | PubMed | Google Scholar6. Mbakwe AB, Lourentzou I, Celi LA, Mechanic OJ, Dagan A. ChatGPT passing USMLE shines a spotlight on the flaws of medical education. PLOS Digit Health 2: e0000205, 2023. doi:10.1371/journal.pdig.0000205. Crossref | PubMed | Google ScholarAUTHOR NOTESCorrespondence: S. Krishna Mohan (krishnamohan.[email protected]com). Download PDF Previous Back to Top FiguresReferencesRelatedInformation CollectionsAdvances in Physiology Education CollectionsAI in Physiology Education More from this issue > Volume 47Issue 2June 2023Pages 270-271 Crossmark Copyright & PermissionsCopyright © 2023 the American Physiological Society.https://doi.org/10.1152/advan.00036.2023PubMed36971685History Received 10 March 2023 Accepted 10 March 2023 Published online 27 March 2023 Published in print 1 June 2023 Keywordsartificial intelligencechatbotChatGPTmedical educationmedical physiology Metrics 

Evaluating the performance of ChatGPT in medical physiology university examination of phase I MBBS.

Introduction: The cognitive functions in adults can be altered by a number of diseases, nutritional deficiencies, hormonal changes, ageing and drugs. Anaemia, through various possible mechanisms, has been suggested to result in a cognitive deterioration.

Aim and Objective: To study the effect of Hb% on cognitive skills of 100 UG medical students of Sree Balaji Medical College and Hospital, Chromepet, Chennai, India.

Methodology: One hundred UG medical students who were in the age group of 17 to 19 years were selected and their Hb% estimations were done by using Drabkin’s method. Their cognitive skills were assessed by using the Mini Mental State Examination (MMSE). Correlation between Hb% and their cognitive skills was done.

Results: This study showed that there was a significant correlation between the Hb concentration and the cognitive skills in the students.

Effect of Hb% on Cognitive Skills in UG Medical Students.

Background: The relationship between the parameters of body mass index (BMI) and lung function has been established by numerous studies done earlier. Those studies have been done mainly on obese individuals, but only a very few studies have been done in people with normal BMI or overweight group. Our aim is to analyze the various spirometric variables, namely forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), forced expiratory flow (FEF 25%–75%), and peak expiratory flow rate (PEFR), with respect to increase in the BMI in otherwise normal healthy subjects. Methods: The present study design is a randomized experimental parallel-group study. Sixty individuals who were otherwise healthy without any respiratory illness were selected for this study. Their anthropometric measurements were taken. Based on WHO classification, the subjects were grouped as follows: Group I with a BMI of 18.5–24.9 as a normal weight group and Group II with a BMI of 25–29.9 as the overweight group. Using Medispiror, the spirometric variables were determined. Analysis of spirometric variables, namely FEV1, FVC, FEF 25%–75%, and PEFR, were all done using the appropriate statistical method. Results: The results showed that there was a significant decrease in all spirometric variables except PEFR with an increase in BMI. However, the decrease in FVC was relatively more than the decrease in FEV1. Conclusion: This study can be concluded that there is a significant decrease in spirometric variables, namely FEV1, FVC, and FEF 25%–75%, as the BMI increases even in normal individuals who are not obese.

Analysis of spirometric variables with increasing body mass index in normal and overweight healthy individuals

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ilham Jaleel

Author Tools

Chat about Author

Papers

Evaluating the performance of ChatGPT in medical physiology university examination of phase I MBBS.

Effect of Hb% on Cognitive Skills in UG Medical Students.

Analysis of spirometric variables with increasing body mass index in normal and overweight healthy individuals