Document Type
Abstract
Publication Date
2-11-2026
Abstract
Introduction: As a constantly evolving field, medicine is prone to change with acquisition of new knowledge and information. As such, medical learners are obligated to practice Self-Regulated Learning (SRL); being behaviorally, metacognitively, and motivationally proactive in the learning process. With this project, we attempt to explore the use of AI as a tool to mimic clinical encounters and advance SRL.
Methods: Python was used to build an application that stimulates patient interactions under guidance of an instructor. Written case scenarios and AI instructions were parsed, chunked and stored into vectors using LangChain. To facilitate the clinician-patient interaction, OpenAI’s ChatGPT-4o-mini was used, with strict limitations put in place to only use trusted sources of information.
Results: We developed a platform that uses AI to stimulate a patient interaction that is guided by a stimulated instructor. The platform mimics a stepwise patient interaction that involves history taking, physical exam interpretation, and treatment considerations.
Discussion/ Conclusions: The platform is a great tool for increasing clinical practice and exposure under the guidance of a stimulated instructor. Such an application could be very beneficial for medical students and residents in their effort towards Self-Regulated Learning.
Recommended Citation
Sadat, Said Omer and Ratliff, MD, Jeffrey, "ChiefComplaint: An AI Based Application for Self Regulated Learning and Practicing Clinical Skills" (2026). Phase 1. Paper 20.
https://jdc.jefferson.edu/si_des_2028_phase1/20
Language
English
Comments
Presented at the 2026 Scholarly Inquiry (SI) Research Project Symposium.