A Picture Worth a Thousand Words, Created with One Sentence: Using Artificial Intelligence-created Art to Enhance Medical Education

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

BACKGROUND

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

METHODS

This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

RESULTS

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

CONCLUSION

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

Artificial Intelligence-Generated Facial Images for Medical Education

We evaluated the use of text-to-image models (Microsoft's Bing Image creator (powered by DALL·E) and Shutterstock's AI image generator) to generate realistic images of human faces and their associated pathology, which may be useful for medical education, given they may overcome issues of patient privacy and requirement for consent. These models have potential to augment rare medical image datasets for medical education, as well as provide greater inclusivity and representation of diverse populations.

Using Rasch Wright map to identify hospital employee satisfaction during and before COVID-19

During the surge of the COVID-19 outbreak, medical personnel attended to countless patients, which adversely affected their mental well-being. To support their staff, hospitals implemented guidelines that focused on promoting mental health among medical professionals. The hypothesis that employee satisfaction declined during the COVID-19 pandemic needs confirmation. Several findings were derived from a series of visualizations using Rasch Wright map. The research sample was taken from a medical center in southern Taiwan based on satisfaction survey data from 2017 to 2022 (n = 1222). Perceptions on job satisfaction perceptions during and prior to COVID-19 in 2 stages of 2017 to 2019 and 2020 to 2022 were compared using Rasch Wright map. Through a series of visualizations, including the dimension with the highest satisfaction, the demographical category of hospital employees with the lowest satisfaction during the pandemic, and Rasch Wright map displaying employs' perfections on 4 domains over years. The results indicated: Employee satisfaction was significantly lower during the COVID-19 period in 2 domains: compensation and benefits, work atmosphere; among the 23 questions, Question 5 (regarding meals provided by the hospital to staff) scored the lowest, while Question 23 (regarding the hospital emergency response and disaster prevention capabilities) scored the highest. Among the 4 domains, organizational leadership had the highest satisfaction; out of 104 demographic variables, 21 groups showed that employee satisfaction during the pandemic was significantly (P < .05) lower than before the pandemic; the selection of specific demographic variables is for top-tier supervisors, and they showed that employee satisfaction during the pandemic was significantly (P < .05) lower than before the pandemic across all 4 dimensions. Therefore, this study accepts the hypothesis that employee satisfaction was negatively affected during the COVID-19 period on 2 domains only: compensation and benefits, work atmosphere. The study visual examination, especially using Rasch Wright map, offers a comparative perspective on hospital staff satisfaction and serves as a methodological guide for subsequent satisfaction research.

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

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.