Explainable Artificial Intelligence in Personalized Patient Engagement and Digital Healthcare Decision-Making: A Comprehensive Study

Abstract

Artificial intelligence (AI) is increasingly used in digital healthcare to support patient engagement, personalized services and clinical decision-making. The opacity of many models reduces clinical trust, acceptance of patients and responsible deployment. This review focuses on how explainable artificial intelligence (XAI) can enhance the transparency, accountability and user trust for personalizing patient engagement and digital healthcare decision-making, and how interpretable machine learning (iML) can support these efforts. We discuss the possibility of integrating demographic, clinical, behavioral and digital interaction data with explanation methods in the context of personalized recommendations and patient-cented interventions. We then assess the evidence for XAI in clinical decision support, risk prediction, treatment personalization, patient communication and digital health platforms. The principles of interpretability, fairness, privacy, reliability, and human oversight are considered key pillars of trustworthy AI-driven care. Evidence suggests that explanations can play a part in facilitating collaboration among clinicians, patients, and AI systems, by providing reasons for predictions, but that explanations alone are insufficient to ensure appropriate levels of trust and safer decisions. In conclusion, we summarize key technical and ethical issues and identify the areas of AI research most likely to result in clinically responsible, patient-focused and transparent AI frameworks.