Explainable Machine Learning in Healthcare Current Developments, Clinical Applications, and Future Perspectives

Abstract

Explainable AI is becoming more relevant in making decisions in the medical domain based on complex AI predictions that can be explained. This study summarizes the theoretical background, main techniques, ongoing research, clinical uses, assessment needs, and prospects of Explainable Machine Learning (XAI) in healthcare. There are several major approaches such as feature-importance analysis, model explanation given by SHAP and LIME, saliency mapping, counterfactual explanations, rule-based explanations, decision-tree models, attention mechanisms, example-based reasoning, and natural-language explanations or multimodal explanations. They are used in applications such as disease diagnosis, disease risk prediction, medical imaging, personalised treatment, drug discovery, critical-care support, remote monitoring and population-health surveillance. The study emphasizes that the quality of the explanation is dependent not only on visual clarity, but also on fidelity, stability, robustness, clinical relevance, actionability and consistency across settings and populations. Human-centred evaluation is also crucial, as explanations can enhance understanding while also fostering automation bias or reliance. To implement this in clinical practice, external validation, fairness assessment, workflow integration, transparent reporting, reproducibility, data governance, and continuous monitoring are essential. Facing these are a lack of standardized evaluation measures, biased and unrepresentative datasets, privacy/security concerns, regulatory uncertainty, and misleading post-hoc explanations. Future efforts should focus on communicating with stakeholders, future clinical trials, communicating uncertainty, and collaboration across disciplines. Finally, trustworthy explainable systems should empower patient involvement, enhance clinical decision-making, and facilitate safe, equitable and clinically relevant human-AI interactions.