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).

G Guilherme Sacchi De Camargo Correia (10Mayo Clinic, Hematology/Oncology, Jacksonville, United States) N Nabil Ghani (Augusta University Georgia Cancer Center, Augusta, GA) C Christopher Terrell (Georgia Cancer Center, Augusta University, Augusta, GA) A Ashley Danielle Schofield (Georgia Cancer Center, Augusta University, Augusta, GA) M Mina Aleksan (Medical College of Georgia, Augusta, GA) M Mark Dalgetty (1Georgia Cancer Center at Augusta University, Augusta, GA) S Sindu Iska C Christian Leurinda (Georgia Cancer Center, Augusta University, Augusta, GA) I Inemesit Akpan (Medical College of Georgia, Augusta, GA) K Kyler Herrington (Georgia Cancer Center, Augusta) L Lyle J. Hickman (Medical College of Georgia at Augusta University, Augusta, GA) A Anvay Shah (Augusta University, Augusta, GA) D Daniel Thomas Peters (Georgia Cancer Center, Augusta) P Paul M. Dainer (Augusta University, Augusta, GA) A Amany R. Keruakous (Georgia Cancer Center, Augusta University, Augusta, GA) G Gerald Carter Wallace (Medical College of Georgia, Augusta, GA) G Girindra Ghanshyam Raval (Medical College of Georgia at Augusta University, Augusta, GA) R Rami Manochakian (Mayo Clinic Florida, Jacksonville, FL)

Abstract

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

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 9030-9030
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (18)

G

Guilherme Sacchi De Camargo Correia

10Mayo Clinic, Hematology/Oncology, Jacksonville, United States

N

Nabil Ghani

Augusta University Georgia Cancer Center, Augusta, GA

C

Christopher Terrell

Georgia Cancer Center, Augusta University, Augusta, GA

A

Ashley Danielle Schofield

Georgia Cancer Center, Augusta University, Augusta, GA

M

Mina Aleksan

Medical College of Georgia, Augusta, GA

M

Mark Dalgetty

1Georgia Cancer Center at Augusta University, Augusta, GA

S

Sindu Iska

C

Christian Leurinda

Georgia Cancer Center, Augusta University, Augusta, GA

I

Inemesit Akpan

Medical College of Georgia, Augusta, GA

K

Kyler Herrington

Georgia Cancer Center, Augusta

L

Lyle J. Hickman

Medical College of Georgia at Augusta University, Augusta, GA

A

Anvay Shah

Augusta University, Augusta, GA

D

Daniel Thomas Peters

Georgia Cancer Center, Augusta

P

Paul M. Dainer

Augusta University, Augusta, GA

A

Amany R. Keruakous

Georgia Cancer Center, Augusta University, Augusta, GA

G

Gerald Carter Wallace

Medical College of Georgia, Augusta, GA

G

Girindra Ghanshyam Raval

Medical College of Georgia at Augusta University, Augusta, GA

R

Rami Manochakian

Mayo Clinic Florida, Jacksonville, FL