Artificial Intelligence in Medical Diagnostics: Opportunities, Limitations, and Ethical Challenges

Abstract

Artificial intelligence (AI) is increasingly transforming medical diagnostics by improving image interpretation, accelerating clinical workflows, and supporting diagnostic decision-making. Its use in radiology, pathology, ophthalmology, dermatology, and clinical triage demonstrates that AI can enhance efficiency and expand access to diagnostic support. At the same time, the rapid adoption of AI in medicine has raised serious methodological, ethical, and regulatory concerns. Diagnostic performance remains uneven across tools, settings, and specialties, while bias in training data can reproduce or intensify existing health disparities. In addition, limited transparency, insufficient external validation, privacy risks, and unclear accountability continue to constrain safe clinical implementation. This article examines the main opportunities offered by AI in medical diagnostics, analyzes its major limitations, and discusses the ethical challenges associated with its growing use in healthcare. It argues that AI should be integrated as a form of augmented intelligence rather than a replacement for physician judgment. Responsible implementation requires strong governance, local validation, human oversight, privacy protection, and fairness-oriented design. A balanced approach is therefore essential if AI is to contribute meaningfully to accurate, equitable, and trustworthy diagnostic care.