| Title | Explainable Artificial Intelligence for Healthcare Decision Support: Methods, Applications, Challenges, and Future Directions |
| Research Area | Explainable Artificial Intelligence |
| Abstract | Artificial Intelligence (AI) has become an important technology for healthcare decision support, ena-bling computational systems to analyze medical images, laboratory measurements, electronic health records, physiological signals, and clinical information. Machine learning and deep learning models can identify complex relationships within medical data and assist healthcare professionals in diagno-sis, prognosis, risk prediction, treatment planning, and patient monitoring. Despite these capabilities, the increasing use of highly complex AI models has created an important challenge: many models operate as black boxes whose predictions are difficult for clinicians and patients to understand. Ex-plainable Artificial Intelligence (XAI) has emerged as a research direction intended to make AI-as-sisted healthcare decisions more transparent, interpretable, and trustworthy.
This paper presents a comprehensive review of explainable AI for healthcare decision support, exam-ining fundamental concepts, major explainability techniques, clinical applications, system architec-tures, evaluation criteria, ethical considerations, privacy requirements, and deployment challenges. The study discusses feature-based explanation methods, surrogate models, rule-based explanations, counterfactual explanations, attention-based approaches, and visualization techniques. It further ex-amines the application of XAI in medical imaging, disease prediction, clinical risk assessment, per-sonalized medicine, remote patient monitoring, and decision-support systems. A conceptual explain-able healthcare AI framework is proposed in which clinical data are processed through preprocessing, predictive modeling, explanation generation, clinician validation, and decision-support layers. The proposed framework emphasizes the importance of balancing predictive performance with interpreta-bility, fairness, privacy, robustness, and clinical usefulness |
| Keywords | Explainable Artificial Intelligence, Healthcare, Machine Learning, Deep Learning, Clini-cal Decision Support, Medical Diagnosis, Interpretability, Trustworthy AI, Medical Imaging, Healthcare Analytics |
| Paper Status | Published |
| Volume | 2 |
| Issue | 3 |
| Published On | 14/05/2026 |
| Published File |
IJSRTD_4880.pdf
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