Explainable AI for healthcare professionals: Advancing risk assessment, diagnostic precision, and ethical clinical interventions

Pappala, Suneel and Malyadri, M. and Naganjaneyulu, K Venkata and Prakashini, A. and Santosh, Pasuladi (2025) Explainable AI for healthcare professionals: Advancing risk assessment, diagnostic precision, and ethical clinical interventions. World Journal of Biology Pharmacy and Health Sciences, 22 (1). pp. 446-453. ISSN 2582-5542

Artificial intelligence (AI) becomes increasingly integrated into healthcare, the demand for transparency, trust, and accountability in AI-driven decisions has grown significantly. Explainable AI (XAI) provides a solution by making complex AI models interpretable and understandable for clinicians, patients, and regulators. This explores the role of XAI in enhancing predictive accuracy and improving risk assessment in clinical environments. By offering insights into how AI models arrive at diagnoses, treatment plans, and patient risk scores, XAI facilitates safer and more informed medical decision-making. Techniques such as SHAP, LIME, attention mechanisms, and saliency maps are highlighted for their ability to clarify AI behaviour at both the global and local levels. In addition to supporting clinical trust and regulatory compliance, XAI also plays a crucial role in bias detection, ensuring fairness across diverse patient populations. The integration of XAI promotes a human-cantered approach to healthcare AI, enabling collaborative decision-making where medical professionals can validate, adjust, or override AI outputs. As healthcare systems increasingly rely on predictive algorithms for early diagnosis and preventive care, XAI emerges as a critical component in achieving ethical, accurate, and equitable outcomes.