Presentation Description
Lisa Purdy1
Shirley Schipper1 and Shelley Ross1
1 University of Alberta
Shirley Schipper1 and Shelley Ross1
1 University of Alberta
Background:
Advances in technology can spark many emotions for health professions education (HPE) learners, teachers, and programs, ranging from excitement to anxiety. While some advances are rapidly embraced, such as technologies that increase the affordability and fidelity of simulation, others are viewed with caution. The explosive advances in artificial intelligence (AI) and machine learning, especially the emergence of generative large language models (LLM) like OpenAI’s ChatGPT have unprecedented potential but also pose ethical challenges and other dangers to HPE. In this study, we conducted a narrative review of recent literature to identify advantages and dangers of AI in HPE.
Advances in technology can spark many emotions for health professions education (HPE) learners, teachers, and programs, ranging from excitement to anxiety. While some advances are rapidly embraced, such as technologies that increase the affordability and fidelity of simulation, others are viewed with caution. The explosive advances in artificial intelligence (AI) and machine learning, especially the emergence of generative large language models (LLM) like OpenAI’s ChatGPT have unprecedented potential but also pose ethical challenges and other dangers to HPE. In this study, we conducted a narrative review of recent literature to identify advantages and dangers of AI in HPE.
Summary of work:
Search terms for the narrative review included: AI and subdomains (e.g., machine learning); and HPE and subdomains (e.g., medical education). Databases were MEDLINE, EBSCOHOST, and Web of Science; search was restricted to 2015-2023 to capture recent technology developments. Included articles were read in full by all team members. Relevant themes were determined through discussion and consensus.
Search terms for the narrative review included: AI and subdomains (e.g., machine learning); and HPE and subdomains (e.g., medical education). Databases were MEDLINE, EBSCOHOST, and Web of Science; search was restricted to 2015-2023 to capture recent technology developments. Included articles were read in full by all team members. Relevant themes were determined through discussion and consensus.
Results:
We identified two main themes: benefits and dangers. Benefits included: using AI to design and implement automated grading systems; create simulated patient encounters for progress testing; and safe integration of productive failure for learners to facilitate development of adaptive expertise. Dangers included: risk of dehumanizing patient interactions; data privacy and security; and potential for bias and plagiarism inherent in the way that LLMs generate content.
We identified two main themes: benefits and dangers. Benefits included: using AI to design and implement automated grading systems; create simulated patient encounters for progress testing; and safe integration of productive failure for learners to facilitate development of adaptive expertise. Dangers included: risk of dehumanizing patient interactions; data privacy and security; and potential for bias and plagiarism inherent in the way that LLMs generate content.
Discussion:
There is enormous potential of using AI in HPE. However, positives must be balanced by an awareness of the ethical issues and potential detrimental impacts on learning that come with these technology advances..
There is enormous potential of using AI in HPE. However, positives must be balanced by an awareness of the ethical issues and potential detrimental impacts on learning that come with these technology advances..
Conclusions and take-home message:
Education leaders in HPE are in uncharted territory, with few comparable situations to draw upon for guidance. The first step in being prepared for the impact of AI in HPE is to become informed about potentials and pitfalls.
Education leaders in HPE are in uncharted territory, with few comparable situations to draw upon for guidance. The first step in being prepared for the impact of AI in HPE is to become informed about potentials and pitfalls.
References (maximum three)
1. Rampton V, Mittelman M, Goldhahn J. Implications of artificial intelligence for medical education. The Lancet Digital Health. 2020 Mar 1;2(3):e111-2.
2. Lee J, Wu AS, Li D, Kulasegaram KM. Artificial intelligence in undergraduate medical education: a scoping review. Academic Medicine. 2021 Nov 1;96(11S):S62-70.