Utilization of ChatGPT in Medical Education: Applications and Implications for Curriculum Enhancement

Overview

Journal Acta Inform Med

Specialty General Medicine

Date 2024 Feb 21

PMID 38379690

Authors

Yasar Ahmed

Affiliations

Soon will be listed here.

Abstract

Background: The integration of artificial intelligence (AI) into medical education has sparked a paradigm shift in pedagogical approaches, reshaping the way medical knowledge is accessed, processed, and applied. Medical education is a dynamic field that demands continuous adaptation to the evolving healthcare landscape. ChatGPT, an advanced AI language model, with its natural language understanding and generation capabilities, offers a multifaceted toolset that enhances various aspects of medical education.

Objective: The objective of this paper is to explore how ChatGPT, an advanced AI language model, is transforming medical education by serving as a dynamic information resource and driving curriculum reform. It aims to highlight the multifaceted uses of ChatGPT and its potential to reshape the pedagogical landscape in medical education.

Methods: PubMed, Scopus, Web of Science, ERIC, and Google Scholar databases were searched to assess the literature that met the study objectives from 2019 to August 2023 with explicit inclusion and exclusion criteria.

Results: The results demonstrate that ChatGPT's applications in medical education are diverse and encompass real-time curriculum adaptation, personalized learning, and collaborative learning. Its capacity to provide immediate and contextually relevant information has the potential to enhance the quality of medical education significantly.

Conclusion: ChatGPT's integration into medical education represents a transformative shift in educational approaches. It offers a wide range of capabilities, from serving as a repository of medical knowledge to facilitating collaborative learning. As medical education continues to evolve, ChatGPT emerges as a powerful tool that can reshape pedagogy and drive meaningful curriculum reform to meet the needs of modern healthcare practice.ChatGPT emerges as a transformative tool that holds the potential to reshape the landscape of medical pedagogy and drive meaningful curriculum reform.

Citing Articles

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies.

Xu T, Weng H, Liu F, Yang L, Luo Y, Ding Z J Med Internet Res. 2024; 26:e57896.

PMID: 39196640 PMC: 11391159. DOI: 10.2196/57896.

ChatGPT's Response Consistency: A Study on Repeated Queries of Medical Examination Questions.

Funk P, Hoch C, Knoedler S, Knoedler L, Cotofana S, Sofo G Eur J Investig Health Psychol Educ. 2024; 14(3):657-668.

PMID: 38534904 PMC: 10969490. DOI: 10.3390/ejihpe14030043.

References

Radicchi L, Toledo J, Alliprandini D . Critical success factors for implementation of an incident learning system in radiation oncology department. Rep Pract Oncol Radiother. 2020; 25(6):994-1000. PMC: 7586060. DOI: 10.1016/j.rpor.2020.09.014. View

Kang H, Gong Y . Design of a User-Centered Voluntary Reporting System for Patient Safety Events. Stud Health Technol Inform. 2018; 245:733-737. View

Hewitt T, Chreim S . Fix and forget or fix and report: a qualitative study of tensions at the front line of incident reporting. BMJ Qual Saf. 2015; 24(5):303-10. PMC: 4413736. DOI: 10.1136/bmjqs-2014-003279. View

Anderson J, Kodate N, Walters R, Dodds A . Can incident reporting improve safety? Healthcare practitioners' views of the effectiveness of incident reporting. Int J Qual Health Care. 2013; 25(2):141-50. DOI: 10.1093/intqhc/mzs081. View

Montgomery L, Fava P, Freeman C, Hijal T, Maietta C, Parker W . Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy. J Appl Clin Med Phys. 2017; 19(1):259-270. PMC: 5767999. DOI: 10.1002/acm2.12218. View

Carlfjord S, Ohrn A, Gunnarsson A . Experiences from ten years of incident reporting in health care: a qualitative study among department managers and coordinators. BMC Health Serv Res. 2018; 18(1):113. PMC: 5813432. DOI: 10.1186/s12913-018-2876-5. View

Chang D, Cheetham L, Te Marvelde L, Bressel M, Kron T, Gill S . Risk factors for radiotherapy incidents and impact of an online electronic reporting system. Radiother Oncol. 2014; 112(2):199-204. DOI: 10.1016/j.radonc.2014.07.011. View

Greenham S, Manley S, Turnbull K, Hoffmann M, Fonseca A, Westhuyzen J . Application of an incident taxonomy for radiation therapy: Analysis of five years of data from three integrated cancer centres. Rep Pract Oncol Radiother. 2018; 23(3):220-227. PMC: 5948319. DOI: 10.1016/j.rpor.2018.04.002. View

Pawlicki T, Coffey M, Milosevic M . Incident Learning Systems for Radiation Oncology: Development and Value at the Local, National and International Level. Clin Oncol (R Coll Radiol). 2017; 29(9):562-567. DOI: 10.1016/j.clon.2017.07.009. View

10.

Lam C, Medlam G, Wighton A, Breen S, Bissonnette J, McGowan T . A Practice-based Taxonomy for Radiation Treatment Errors. J Med Imaging Radiat Sci. 2019; 44(4):173-179. DOI: 10.1016/j.jmir.2013.08.001. View

11.

Elnahal S, Blackford A, Smith K, Souranis A, Briner V, McNutt T . Identifying Predictive Factors for Incident Reports in Patients Receiving Radiation Therapy. Int J Radiat Oncol Biol Phys. 2016; 94(5):993-9. DOI: 10.1016/j.ijrobp.2015.11.041. View

12.

Malicki J, Bly R, Bulot M, Godet J, Jahnen A, Krengli M . Patient safety in external beam radiotherapy, results of the ACCIRAD project: Current status of proactive risk assessment, reactive analysis of events, and reporting and learning systems in Europe. Radiother Oncol. 2017; 123(1):29-36. DOI: 10.1016/j.radonc.2017.02.016. View