TY - GEN
T1 - Artificial Intelligence in Narrative Feedback Analysis for Competency-Based Medical Education
T2 - 35th Medical Informatics Europe Conference, MIE 2025
AU - Khan, Sameer Asim
AU - Taiyara, Jamal
AU - Zary, Nabil
AU - Otaki, Farah
N1 - Publisher Copyright:
© 2025 The Authors.
PY - 2025/5/15
Y1 - 2025/5/15
N2 - Competency-Based Medical Education (CBME) generates large volumes of qualitative data in the form of narrative feedback. Traditional qualitative analysis methods face limitations in managing this data's scale and complexity. This review explores the applications, impact, and challenges of Artificial Intelligence (AI), particularly Natural Language Processing (NLP), for analyzing and visualizing medical student performance feedback within CBME contexts. We conducted a comprehensive search of PubMed and Google Scholar databases, identifying key studies that met our inclusion criteria. Our findings highlight how AI can enhance traditional analysis methods by automating theme extraction, reducing educator workload, and improving feedback evaluation processes. The review also addresses challenges associated with AI implementations, including contextual limitations and the need for human oversight. We conclude by emphasizing AI's transformative potential in CBME while identifying critical areas for further research to ensure effective integration into educational workflows.
AB - Competency-Based Medical Education (CBME) generates large volumes of qualitative data in the form of narrative feedback. Traditional qualitative analysis methods face limitations in managing this data's scale and complexity. This review explores the applications, impact, and challenges of Artificial Intelligence (AI), particularly Natural Language Processing (NLP), for analyzing and visualizing medical student performance feedback within CBME contexts. We conducted a comprehensive search of PubMed and Google Scholar databases, identifying key studies that met our inclusion criteria. Our findings highlight how AI can enhance traditional analysis methods by automating theme extraction, reducing educator workload, and improving feedback evaluation processes. The review also addresses challenges associated with AI implementations, including contextual limitations and the need for human oversight. We conclude by emphasizing AI's transformative potential in CBME while identifying critical areas for further research to ensure effective integration into educational workflows.
KW - Artificial intelligence
KW - competency-based medical education
KW - feedback
KW - medical education
KW - natural language processing
KW - qualitative analysis
UR - https://www.scopus.com/pages/publications/105005817071
U2 - 10.3233/SHTI250637
DO - 10.3233/SHTI250637
M3 - Conference contribution
C2 - 40380740
AN - SCOPUS:105005817071
T3 - Studies in Health Technology and Informatics
SP - 1423
EP - 1427
BT - Intelligent Health Systems - From Technology to Data and Knowledge, Proceedings of MIE 2025
A2 - Andrikopoulou, Elisavet
A2 - Gallos, Parisis
A2 - Arvanitis, Theodoros N.
A2 - Austin, Rosalynn
A2 - Benis, Arriel
A2 - Cornet, Ronald
A2 - Chatzistergos, Panagiotis
A2 - Dejaco, Alexander
A2 - Dusseljee-Peute, Linda
A2 - Mohasseb, Alaa
A2 - Natsiavas, Pantelis
A2 - Nakkas, Haythem
A2 - Scott, Philip
PB - IOS Press BV
Y2 - 19 May 2025 through 21 May 2025
ER -