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Peer-Reviewed Publication
NPJ Digit Med2025;8(1):578.September 26, 2025Journal Article

Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour.

Sujeong Hur1,2, Yura Lee3, Joongheum Park2,4,5, Yeong Jeong Jeon6, Jong Ho Cho6, Duck Cho7, Dobin Lim8, Wonil Hwang8, Won Chul Cha1,9,10, Junsang Yoo11
1Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
2AvoMD, New York, NY, USA.
3Department of Information Medicine, Asan Medical Center, Seoul, Republic of Korea.
4Assistant Professor, Chobanian & Avedisian School of Medicine, Boston, MA, USA.
5Associated Harvard Medical Faculty Physician, Beth Israel Deaconess Medical Center, Boston, MA, USA.
6Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
7Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
8Department of Industrial and Information Systems Engineering, Soongsil University, Seoul, Republic of Korea.
9Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
10Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea.
11Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea. junnsang@skku.edu.
PubMed PMC DOI PDF

Abstract

Clinical decision-making substantially impacts patients' lives and their quality of life. However, the black-box nature of AI-powered clinical decision support systems (CDSSs) complicates the interpretation of how decisions are derived. Explainable AI (XAI) improves acceptance and trust with explanations, but the effectiveness of different methods remains uncertain. We compared the acceptance, tru…

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