Ferdus, Mst Zannatun and Bhuiyan, Rowsan Jahan and Monsur, Md Hasan and Shafi, Abdullah Hel and Nessa, Most. Jafrun and Tabassum CN, Mariya and Brydie, Dr. Daryl and Sani, Zamadi Uz (2026) Explainable AI Systems to Enhance Patient Safety and Clinical Accountability. American Journal of Pediatric Medicine and Health Sciences, 4 (1). pp. 59-65. ISSN 2993-2149

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Abstract

The growing use of artificial intelligence (AI) in medical services has brought with it some potent clinical decision tools, diagnostic tools, and patient monitoring tools. Nevertheless, the complexity of most AI systems, especially deep learning methods, casts doubt over the safety of patients and stakeholder confidence in clinics and the law. Explainable Artificial Intelligence (XAI) has become a highly important measure to overcome these issues by offering clear, interpretable and understandable explanations of AI-based decisions. The paper discusses how XAI systems can be used to improve patient safety and improve clinical accountability in a health care setting. It explores the role of explainability in helping clinicians to justify AI recommendations, detect possible errors or biases, and gain more confidence in their decisions. Moreover, the paper explains XAI implications on regulatory compliance, ethical governance, and medico-legal responsibility. Through the incorporation of explainable mechanisms in clinical AI systems, healthcare facilities can enable trust and improve patient outcomes with greater accuracy and create more transparent accountability units. The results emphasize XAI as the basis of responsible and sustainable implementation of AI technologies in the contemporary healthcare systems.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 May 2026 09:56
Last Modified:	07 May 2026 09:56
URI:	http://eprints.umsida.ac.id/id/eprint/16466

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