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Terrell, Christopher

Mayo Clinic

Augusta University

Citations 1

Applying learning science principles (LSP) in a workshop (WS) for hematology and oncology (HemOnc) trainees (HOT) about how to use artificial intelligence (AI) in medical education (MedEd) and clinical practice (CP).
Correia, Guilherme Sacchi de Camargo, Ghani, Nabil , Terrell, Christopher, Schofield, Ashley Danielle, Aleksan, Mina, Dalgetty, Mark, Iska, Sindu , Leurinda, Christian, Akpan, Inemesit , Herrington, Kyler, Hickman, Lyle J., Shah, Anvay , Peters, Daniel Thomas, Dainer, Paul M., Wallace, Gerald , Raval, Girindra , Dalgetty, Mark, Hickman, Lyle J., Dainer, Paul , Keruakous, Amany R. , Manochakian, Rami

Journal of Clinical Oncology | May 28, 2026 | Volume: 44 | Issue: 16_suppl | Pages: 9030-9030

10.1200/jco.2026.44.16_suppl.9030 Get It

9030 Background: AI use is ubiquitous among HOT and healthcare workers, but with variable base knowledge and comfort level. Its indiscriminate use may be problematic. To address this educational gap, we developed a WS for HOT to assess their use of AI and educate them on how to employ it in MedEd and patient-centered CP based on LSP. Methods: A faculty member developed a WS on how to use AI in MedEd and CP based on LSP of analogy, contrasting cases, elaboration, generation, and question driven learning. The WS was for HOT, but faculty could join. It had 3 sections: introduction to basic concepts of AI; principles of the Health Insurance Portability and Accountability Act and protected health information; AI in MedEd; and AI in CP. Sections had real use case examples of AI, for participants to interact, ask questions, and share their personal use cases. HOT were invited to compare and generate examples during the WS, based on their educational and clinical experience and needs. Participants’ AI use and self-perceived and objective knowledge about it were assessed in a pre activity survey, a post WS survey, and a post 8 week survey to evaluate long term learning. Results: A 2 hour WS with 9 HOT and 2 faculty was completed successfully. 10 participants completed the pre WS survey, and 11 completed the post WS and the post 8 week surveys. 100% of them used generative AI tools before the WS, while 10% had prior training in AI. 90% used AI in personal life and MedEd, 70% used it in CP, and 30% in academic research. Table 1 shows participants’ self-assessed knowledge of AI, comfort level in using AI for MedEd and CP, self-assessed preparedness to implement AI in MedEd and CP, and objective knowledge. 8 weeks post WS, 91% of participants reported increased AI use in CP, leading anywhere from slight to significant decreases in time spent on documentation tasks for 91% of them. In MedEd, 73% reported increased AI use, and 82% described slight to significant improvement in the quality of time dedicated to it. 82% described the WS as extremely relevant to their daily responsibilities, and 91% reported confidence navigating ethical and privacy risks of AI in HemOnc. All participants were interested in having a similar WS in the future. Conclusions: An LSP based WS about AI increased the self-perceived and objective knowledge of HOT about using this tool in MedEd and CP. It also increased HOT comfort level, preparedness, and efficiency using AI. With the ubiquitous use of AI by HOT, ongoing education about it focused on supporting high-quality patient care should be a part of fellowship curriculum. Pre WS % Post WS % Post 8 week % Moderately/Very knowledgeable about AI 50 91 100 Comfortable using AI in MedEd 60 82 82 Prepared to implement AI in MedEd 30 91 73 Comfortable using AI in CP 40 73 82 Prepared to implement AI in CP 0 91 73 Correct answers on objective assessment 53 68 71