Ethical and Clinical Implications of Artificial Intelligence in Diagnostic Medicine

Farah Naeem Malik; Faiza Nawaz; Hamna Mughal

Authors

Farah Naeem Malik Department of Public Health, Health Services Academy, Islamabad, Pakistan
Faiza Nawaz Department of Computer Engineering, COMSATS University Islamabad, Attock Campus, Pakistan
Hamna Mughal Department of Computer Engineering, COMSATS University Islamabad, Attock Campus, Pakistan

Keywords:

Explainable Artificial Intelligence, Ethical Frameworks, Diagnostic Medicine, Privacy-Preserving Methods

Abstract

Artificial intelligence (AI) is becoming a major transformative force in diagnostic medicine because it allows predicting the disease more accurately and raises the level of clinical efficiency. state-of-the-art machine learning models trained on large-scale integrated datasets have made it possible to have AI systems analyze medical imaging, histopathology samples, and electronic health records at a level comparable to human experts. Irrespective of these developments, there are serious ethical, clinical, and governance issues concerning the extensive use of AI in diagnosis. In this paper, a systematic narrative review of these concerns will be presented based on 30 peer-reviewed publications found in PubMed, IEEE Xplore, Scopus, and Google Scholar, and relating to the years 2023-2025. With the help of Boolean search strategies, the review identifies algorithmic bias, explainability, and data privacy as key ethical issues, each reported in more than 60% of the reviewed studies. The most critical regulatory weaknesses were found to be governance gaps and a lack of monitoring of post-deployment. The results underscore a long-standing trade-off between model performance and explainability, and the necessity of human-in-the-loop systems to maintain clinical judgment and patient trust. Implementing AI responsibly involves strong governance mechanisms and Tran’s disciplinary teams of technologists, clinicians, ethicists, and policymakers to create fair and patient-focused AI diagnostics.

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