Explainable AI for Clinical Decision Support: A Study on Interpretable Models for Disease Diagnosis

Author(s)	Mr. Ronak Goyal
Country	India
Abstract	This study explores the impact of explainable artificial intelligence (XAI) components on enhancing Diagnostic Accuracy (DA) within clinical decision support systems (CDSS). Focusing on three key predictors—Model Interpretability (MI), Clinician Trust (CT), and Case Complexity (CM)—the study utilizes primary data collected from 250 healthcare professionals in New York. A structured questionnaire measured all constructs on a 5-point Likert scale. Data analysis was conducted using R Studio, applying multiple regression to assess the relationships among variables. Results indicate that MI, CT, and CM each have a significant and positive effect on DA, with the model explaining approximately 65% of the variance. Visual diagnostics support the model’s validity and confirm compliance with key statistical assumptions. This research highlights the importance of integrating interpretable AI in healthcare to improve clinical outcomes and practitioner confidence. The study offers practical implications for healthcare AI design and future research in medical decision-making systems.
Field	Computer Applications
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-09

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International Journal on Science and Technology