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Artificial Intelligence in Health AI-driven personalized learning in residency

institutional studies, integrate longitudinal performance Ethical approval and consent to participate
tracking, and incorporate resident and faculty perspectives
on AI adoption in medical education. This study qualifies for exemption from IRB review under
the U.S. Department of Health and Human Services (HHS)
5. Conclusion Human Subject Regulations Decision Charts. Research
involving educational tests, survey procedures, interview
Integrating AI-driven adaptive learning platforms presents procedures, or observation of public behavior, where
several opportunities for improving residency training and, identifiable information is not recorded or disclosure
potentially, quality of care. Program directors and medical would not place subjects at risk. As such, this study does not
educators should consider incorporating AI-assisted require formal approval by an Institutional Review Board
flipped classrooms to supplement traditional didactic or the acquisition of informed consent, in accordance with
instruction, allowing residents to engage with material at federal guidelines.
their own pace while receiving real-time feedback on areas
requiring improvement. In addition, AI platforms should Consent for publication
be designed with human oversight mechanisms, ensuring This educational research project used only aggregated,
that educational content remains accurate, unbiased, fully de-identified assessment data, with no protected
and aligned with competency-based training standards. health information (PHI), personally identifiable
To further enhance engagement, residency programs information, or images of participants. As such, this study
should integrate faculty development initiatives that train qualifies for exemption from IRB review under the U.S.
educators in AI-assisted pedagogical strategies, fostering Department of Health and Human Services (HHS) Human
collaborative learning models that combine AI-driven Subject Regulations Decision Charts.
insights with expert mentoring. As AI technologies evolve,
ongoing evaluation of their educational impact, scalability, Availability of data
and ethical use is essential. Future research should also Data are available from the corresponding author upon
examine how AI-based learning affects long-term clinical reasonable request.
performance, interprofessional collaboration, and patient
outcomes – areas critical to the future of medical education References
and healthcare delivery.
1. Lo CK, Hew KF. A review of integrating AI-based chatbots
Acknowledgment into flipped learning: New possibilities and challenges. Front
Educ. 2023;8:1-7.
None. doi: 10.3389/feduc.2023.1175715

Funding 2. Dushyanthen S, Zamri NI, Chapman W, Capurro D, Lyons K.
Evaluation of an interdisciplinary educational program to
The authors declare that edYOU provided the e-learning foster learning health systems: Education evaluation. JMIR
platform for the study. No external grants or additional Med Educ. 2025;11:e54152.
financial support were received for this article’s research, doi: 10.2196/54152
analysis, or publication.
3. Dave D, Raval V. AI-Powered Flipped Classrooms for English
Conflict of interest Language Learning. 2024.

The authors declare that they have no competing interests. 4. Roll I, Wylie R. Evolution and revolution in artificial
intelligence in education. Int J Artif Intell Educ.
Author contributions 2016;26(2):582-599.

Conceptualization: Marcos A. Sanchez-Gonzalez doi: 10.1007/s40593-016-0110-3
Data curation: Noelani-Mei Ascio, Omar Shah, Salman 5. Chan KS, Zary N. Applications and challenges of
Muddassir implementing artificial intelligence in medical education:
Investigation: Marcos A. Sanchez-Gonzalez, Noelani-Mei Integrative review. JMIR Med Educ. 2019;5(1):e13930.
Ascio, Omar Shah, Salman Muddassir doi: 10.2196/13930
Methodology: Ashley Matejka, Mark Terrell 6. Barrera Castro GP, Chiappe A, Ramírez-Montoya MS,
Supervision: Marcos A. Sanchez-Gonzalez Alcántar Nieblas C. Key barriers to personalized learning in
Writing – original draft: Marcos A. Sanchez-Gonzalez times of artificial intelligence: A literature review. Appl Sci.
Writing – review & editing: All authors 2025;15(6):3103.

Volume 2 Issue 4 (2025) 144 doi: 10.36922/AIH025130023