Computer Science · Literature

Research papers on Explainable AI in healthcare

Recent and highly-cited academic work on explainable ai in healthcare, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. Explainable AI for enhanced decision-making

   Kristof Coussement, Mohammad Zoynul Abedin, Mathias Kraus, et al. · 2024 · Decision Support Systems · 111 citations

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2. Leveraging ChatGPT and explainable AI for enhancing clinical decision support

   Radwa El Shawi, Leila Jamel · 2025 · Scientific Reports · 5 citations

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3. From Data to Diagnosis: Rethinking Clinical Decision Support With Explainable AI

   Norita Ahmad, Aqilah Julaihi, Preman Rajalingam · 2025 · Computer · 5 citations

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4. A User-Centered Design of Explainable AI for Clinical Decision Support

   Mozhgan Salimiparsa, Daniel Lizotte, Kamran Sedig · 2021 · Proceedings of the Canadian Conference on Artificial Intelligence · 2 citations

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5. EXPLAINABLE AI FOR THERAPEUTIC DECISION-MAKING AND PRESCRIPTION SAFETY: A LONGITUDINAL FRAMEWORK FOR CLINICAL DECISION SUPPORT

   Albert Bacelar · 2026 · International Journal of Advanced Research

   Background: Clinical artificial intelligence has been evaluated mainly through diagnosis, triage, image interpretation, and isolated question answering. Therapeutic decision-making has a different structure: it converts clinical reasoning into action through drug choice, dose, route, timing, contraindication screening, monitoring, reassessment, escalation, de-escalation, and discontinuation. An explainable system that names a diagnosis but does not account for this action chain remains incomplete as clinical decision support.

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

   Ronak Goyal · 2026 · International Journal on Science and Technology

   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%

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7. An explainable AI framework for early mental health screening and clinical decision support using reflective listening and generative AI

   Mahrukh Shakoor, Tamim Ahmed Khan, Hina Ghafoor · 2026 · Discover Psychology

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8. Explainable Artificial Intelligence for Trustworthy Clinical Decision Support Systems

   Dr. Aarav Sharma · 2026 · European International Journal of Multidisciplinary Research and Management Studies

   The increasing integration of Artificial Intelligence (AI) into healthcare has transformed Clinical Decision Support Systems (CDSS) from rule-based advisory platforms into intelligent systems capable of diagnosing diseases, predicting clinical outcomes, recommending treatments, and optimizing healthcare workflows. Despite remarkable improvements in predictive performance through deep learning, ensemble learning, and generative artificial intelligence, the widespread clinical adoption of AI remains constrained by the limited transparency of complex machine learning models. Most high-performing AI algorithms function as "black-box" systems, providing highly accurate predictions while offering

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