The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review

Transparency in AI medical tools is critical for patient safety: The paper emphasizes that machine learning models used in healthcare often operate as "black boxes," making their diagnostic decisions unclear. This lack of transparency can jeopardize patient outcomes, highlighting why explainable AI (XAI) is essential for responsible medical AI deployment.

XAI techniques are being successfully applied to major chronic diseases: The review identified nine trending XAI algorithms being used to diagnose and monitor serious conditions including Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease, demonstrating that explainability is achievable across diverse healthcare applications.

Building clinician trust requires more than just better algorithms: While XAI methods show promise, the paper identifies "clinician acceptance" as a significant remaining challenge. This suggests that responsible AI in healthcare requires not only technical solutions but also efforts to help medical professionals understand and trust these new tools.

Real-world implementation faces practical barriers: The review highlights that scalability and validation of XAI methods remain major obstacles to integrating these systems into actual clinical practice, indicating that the gap between promising research and real-world responsible deployment is still substantial.

Further research is needed before widespread adoption: The paper concludes that while XAI offers a "promising path forward," significant work remains in integrating these systems into healthcare settings, suggesting caution against premature implementation without addressing outstanding validation and integration challenges.