• Jonathan Kenigson

Artificial intelligence (AI) holds significant promise for advancing medical research, particularly in diagnostics and disease prevention. However, most existing AI models function as opaque “black boxes,” limiting external scrutiny and creating barriers to transparency, interpretability, and trust among clinicians and patients. The lack of interpretability raises challenges for regulatory approval, clinical integration, and ethical medical decision-making. Explainable Artificial Intelligence (XAI) has emerged as a potential solution to these challenges by improving transparency and interpretability. This paper examines XAI in healthcare through both model-agnostic methods (e.g., LIME, SHAP, LORE) and model-specific techniques (e.g., decision trees, attention mechanisms, generalized additive models). Drawing on recent empirical evidence and systematic reviews, we analyze the ability of XAI frameworks to strengthen physician trust, regulatory compliance, and diagnostic accuracy. Findings indicate that explainability in diagnostic models not only supports clinical decision-making but also enhances patient safety by reducing errors and reinforcing accountability. Furthermore, XAI addresses the broader challenges of integrating AI into healthcare by balancing technical innovation with ethical and regulatory requirements. We conclude by recommending that explainable AI be recognized as a critical pathway toward the development of safe, transparent, and patient-centered diagnostic systems, representing a paradigm shift in the future of medical artificial intelligence.

Explainable Artificial Intelligence, Interpretable Machine Learning, XAI in Healthcare, Local Explainability Methods, Transparent AI in Medical Diagnosis

"EXPLAINABLE AI FOR HEALTHCARE: DEVELOPING TRANSPARENT AND INTERPRETABLE MODELS FOR MEDICAL DIAGNOSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.d787-d803, August-2025, Available :http://www.jetir.org/papers/JETIR2508397.pdf

"EXPLAINABLE AI FOR HEALTHCARE: DEVELOPING TRANSPARENT AND INTERPRETABLE MODELS FOR MEDICAL DIAGNOSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppd787-d803, August-2025, Available at : http://www.jetir.org/papers/JETIR2508397.pdf