ChatGPT: roles and boundaries of the new artificial intelligence tool in medical education and health research – correspondence

Periaysamy, Aravind Gandhi; Satapathy, Prakasini; Neyazi, Ahmad; Padhi, Bijaya Kumar

doi:10.1097/ms9.0000000000000371

Cited by 18 publications

(17 citation statements)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, other papers provided instances of AI-generated content to demonstrate the potential for creating novel learning materials with this technology. However, the range of examples provided in the current literature is relatively limited [ 1 , 12 , 15 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…Numerous articles underscored the potential of generative AI to assist with literature reviews and summarizations [ 1 , 12 , 14 , 20 , 22 , 25 ]. However, they cautioned against the possibility of generative AI fabricating references and information, a pitfall commonly referred to as “hallucination.” This issue was brought to the fore in a piece by the editor of Medical Teacher, which recounted the journal’s first encounter with a “hallucinated” citation in a manuscript submitted for publication [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Preiksaitis,

Rose

2023

JMIR Med Educ

View full text Add to dashboard Cite

Background 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 and Bard, can generate new content and have a wide range of possible applications. Objective This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. Methods We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. Results Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners’ skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. Conclusions The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Preiksaitis,

Rose

2023

JMIR Med Educ

View full text Add to dashboard Cite

show abstract

“…Almost all disciplines worldwide harness AI’s rapidly emerging principles and applications. Medical education can also leverage the potential of AI in providing engaged and self-directed learning for students [ 16 , 21 ]. This efficacy might be due to the extensive training database of ChatGPT, which comprised 300 billion words spanning multiple disciplines and the “transformer model.” Earlier learning models were restricted to a specific task (ie, narrow AI).…”

Section: Discussionmentioning

confidence: 99%

“…ChatGPT’s inherent property of the “transformer model” has been reported to assist in writing review articles on health [ 15 ]. The interactive digital interface and prompt-based responses of GPT models with access to a multidomain database [ 16 ] offer feasibility for students to use these tools during the “interested learner” stage of self-directed learning (SDL) [ 15 ], which is an essential principle of adult learning.…”

Section: Introductionmentioning

confidence: 99%

Performance of ChatGPT on the India Undergraduate Community Medicine Examination: Cross-Sectional Study

Gandhi,

Joesph,

Rajagopal

et al. 2024

JMIR Form Res

Self Cite

View full text Add to dashboard Cite

Background Medical students may increasingly use large language models (LLMs) in their learning. ChatGPT is an LLM at the forefront of this new development in medical education with the capacity to respond to multidisciplinary questions. Objective The aim of this study was to evaluate the ability of ChatGPT 3.5 to complete the Indian undergraduate medical examination in the subject of community medicine. We further compared ChatGPT scores with the scores obtained by the students. Methods The study was conducted at a publicly funded medical college in Hyderabad, India. The study was based on the internal assessment examination conducted in January 2023 for students in the Bachelor of Medicine and Bachelor of Surgery Final Year–Part I program; the examination of focus included 40 questions (divided between two papers) from the community medicine subject syllabus. Each paper had three sections with different weightage of marks for each section: section one had two long essay–type questions worth 15 marks each, section two had 8 short essay–type questions worth 5 marks each, and section three had 10 short-answer questions worth 3 marks each. The same questions were administered as prompts to ChatGPT 3.5 and the responses were recorded. Apart from scoring ChatGPT responses, two independent evaluators explored the responses to each question to further analyze their quality with regard to three subdomains: relevancy, coherence, and completeness. Each question was scored in these subdomains on a Likert scale of 1-5. The average of the two evaluators was taken as the subdomain score of the question. The proportion of questions with a score 50% of the maximum score (5) in each subdomain was calculated. Results ChatGPT 3.5 scored 72.3% on paper 1 and 61% on paper 2. The mean score of the 94 students was 43% on paper 1 and 45% on paper 2. The responses of ChatGPT 3.5 were also rated to be satisfactorily relevant, coherent, and complete for most of the questions (>80%). Conclusions ChatGPT 3.5 appears to have substantial and sufficient knowledge to understand and answer the Indian medical undergraduate examination in the subject of community medicine. ChatGPT may be introduced to students to enable the self-directed learning of community medicine in pilot mode. However, faculty oversight will be required as ChatGPT is still in the initial stages of development, and thus its potential and reliability of medical content from the Indian context need to be further explored comprehensively.

show abstract

“…The recent guidelines issued by the World Association of Medical Editors (WAME) place strong emphasis that AI chatbots, both from an ethical and legal standpoint, should not be recognized as coauthors of manuscripts in scientific literature authorship [ 47 ]. This not only underscores the pressing need for standardized reporting and the implementation of checklists for the utilization of AI tools in medical research, but also advocates for meticulous disclosure of pertinent information about the AI tool employed, which includes its name, version, and specific prompts.…”

Section: Ai’s Unethical Role In Scholarly Writingmentioning

confidence: 99%

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

Miao,

Thongprayoon,

Suppadungsuk

et al. 2023

Clinics and Practice

View full text Add to dashboard Cite

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI’s capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an “AI scrutiny” phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI’s role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI’s capabilities while upholding stringent academic standards can be envisioned.

show abstract

ChatGPT: roles and boundaries of the new artificial intelligence tool in medical education and health research – correspondence

Cited by 18 publications

References 7 publications

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

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

Performance of ChatGPT on the India Undergraduate Community Medicine Examination: Cross-Sectional Study

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

Contact Info

Product

Resources

About