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Papers on “explainable AI clinical decision support”

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1. Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges

   Qaiser Abbas, Woo-Min Jeong, Seung Won Lee · 2025 · Healthcare · 275 cites

   Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mecha

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2. Co-design of Human-centered, Explainable AI for Clinical Decision Support

   Cecilia Panigutti, Andrea Beretta, Daniele Fadda, et al. · 2023 · ACM Transactions on Interactive Intelligent Systems · 122 cites

   eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique and its explanation user interface for clinical Decision Support Systems (D

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3. Explainable AI for enhanced decision-making

   Kristof Coussement, Mohammad Zoynul Abedin, Mathias Kraus, et al. · 2024 · Decision Support Systems · 108 cites

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4. Explainability in medicine in an era of AI-based clinical decision support systems

   Robin L Pierce, Wim Van Biesen, Daan Van Cauwenberge, et al. · 2022 · Frontiers in Genetics · 94 cites

   The combination of "Big Data" and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making. However, the responsible adoption of AI-based clinical decision support systems faces several challenges at both the individual and societal level. One of the features that has given rise to particular concern is the issue of explainability, since, if the way an algorithm arrived at a particular output is not known (or knowable) to a physician, this may lead to multiple challenges, including an inability to evaluate the merits of the output. This "opacity" problem has led to questions about whether physician

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5. Explainable AI for Clinical Decision Support Systems: Literature Review, Key Gaps, and Research Synthesis

   Mozhgan Salimparsa, K. Sedig, Dan Lizotte, et al. · 2025 · Informatics · 49 cites

   While Artificial Intelligence (AI) promises significant enhancements for Clinical Decision Support Systems (CDSSs), the opacity of many AI models remains a major barrier to clinical adoption, primarily due to interpretability and trust challenges. Explainable AI (XAI) seeks to bridge this gap by making model reasoning understandable to clinicians, but technical XAI solutions have too often failed to address real-world clinician needs, workflow integration, and usability concerns. This study synthesizes persistent challenges in applying XAI to CDSS—including mismatched explanation methods, suboptimal interface designs, and insufficient evaluation practices—and proposes a structured, user-cent

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6. A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems

   A. Gambetti, Qi-Wei Han, Hong Shen, et al. · 2025 · ArXiv · 23 cites

   Explainable Artificial Intelligence (XAI) is essential for the transparency and clinical adoption of Clinical Decision Support Systems (CDSS). However, the real-world effectiveness of existing XAI methods remains limited and is inconsistently evaluated. This study conducts a systematic PRISMA-guided survey of 31 human-centered evaluations (HCE) of XAI applied to CDSS, classifying them by XAI methodology, evaluation design, and adoption barrier. Our findings reveal that most existing studies employ post-hoc, model-agnostic approaches such as SHAP and Grad-CAM, typically assessed through small-scale clinician studies. The results show that over 80% of the studies adopt post-hoc, model-agnostic

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7. Explainable AI in Healthcare: Systematic Review of Clinical Decision Support Systems

   N. Aziz, A. Manzoor, M. D. Mazhar Qureshi, et al. · 2024 · 18 cites

   This systematic review examines the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in Clinical Decision Support Systems (CDSS), highlighting significant advancements and identifying persistent challenges. Utilising the PRISMA protocol, we searched major indexed databases such as Scopus, Web of Science, PubMed, and the Cochrane Library, to analyse publications from January 2000 to April 2024. This timeframe captures the progressive integration of XAI in CDSS, offering a historical and technological overview. The review covers the datasets, application areas, machine learning models, explainable AI methods, and evaluation strategies for multiple XAI methods. Analy

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8. Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment

   Zhi-Yuan Li, Li-Wei Wang · 2025 · Scientific Reports · 14 cites

   This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized treatment planning in complex dentofacial deformities by formulating treatment as a sequential decision-making process optimizing multiple clinical objectives simultaneously. We developed a comprehensive framework comprising: (1) a multi-task reinforcement learning core with specialized state-action representations for craniofacial structures; (2) complementary explainable AI components that render complex model decisions interpretable within clini

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