Explainable Artificial Intelligence in Healthcare Decision-Making: Ethical Justice, Clinical Trust, and Human-Centered Interpretability
Keywords:
Explainable Artificial Intelligence, Healthcare Machine Learning, Algorithmic Transparency, Clinical Decision SupportAbstract
Explainable Artificial Intelligence (XAI) has emerged as a foundational requirement for the responsible deployment of machine learning systems in healthcare. While predictive accuracy has historically dominated the evaluation of medical AI, recent scholarship emphasizes that transparency, interpretability, fairness, and trustworthiness are equally critical for real-world clinical adoption. This research article presents an extensive theoretical and analytical examination of explainable artificial intelligence in healthcare decision-making, drawing strictly from established academic literature. The study integrates perspectives from algorithmic justice, clinical machine learning, human–computer interaction, and medical ethics to explore how explainability reshapes the relationship between clinicians, patients, and intelligent systems. Through a qualitative synthesis of prior empirical and conceptual studies, this article investigates how opaque models influence perceptions of fairness, accountability, and legitimacy, particularly in high-stakes medical contexts. The methodology relies on structured interpretive analysis of peer-reviewed research, focusing on intelligible model design, explainability frameworks, bias mitigation, system causability, and clinical workflow integration. The results highlight that explainability is not a singular technical feature but a socio-technical property shaped by context, user expertise, and institutional norms. The discussion critically evaluates limitations of current XAI approaches, including cognitive overload, false transparency, and ethical trade-offs between performance and interpretability. The article concludes by positioning explainable AI as a moral, epistemic, and clinical necessity, arguing that sustainable medical AI must prioritize human understanding alongside algorithmic capability. This work contributes a comprehensive, theory-driven foundation for future research and policy development in explainable healthcare AI.
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