Explainable AI for Healthcare Revolution: Opportunities for Trust and Ethical Challenges

Abstract

AI requires explainable intelligence features (XAI) to provide Healthcare 5.0 systems with opportunities to build strong trust and ethical alignment combined with system transparency throughout clinical decision-making processes. Traditional AI black boxes produce opaque information but XAI offers understandable insights so healthcare workers can both determine and verify AI-generated recommendations. The implementation of Explainable Artificial Intelligence provides stakeholders with both trust and accountability when making crucial patient care choices. XAI creates equitable patient-centred healthcare through its ability to eliminate data bias while fostering complete transparency during diagnosis and treatment. AI systems alongside clinicians can collaborate effectively when healthcare providers receive intelligent insights which they can understand through XAI. However, challenges remain. The implementation of XAI faces strong obstacles including datasets with algorithmic biases alongside privacy limitations and persistent resistance from healthcare providers to adopt new approaches. The challenges demand a solution through strong governance structures that should pair with interdisciplinary work and ethical boundaries for proper resolution. XAI faces significant challenges which if solved could transform Healthcare 5.0 by producing better patient outcomes through achieving trust and equity across evolving healthcare systems.