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<p>Artificial intelligence (AI) has increasingly influenced medical diagnostics by enabling data-driven clinical decision support across imaging, genomics, and remote monitoring. This review critically examines the historical evolution, current clinical applications, and emerging innovations in AI-based diagnostics, while evaluating methodological rigor, validation practices, and implementation challenges. A structured narrative synthesis of peer-reviewed literature was conducted using major biomedical and engineering databases. Evidence suggests that AI, particularly deep learning, can achieve diagnostic performance comparable to clinicians in selected tasks; however, heterogeneity in datasets, limited external validation, and workflow integration challenges restrict generalizability. Ethical, regulatory, and human-centered considerations remain central to safe adoption. Overall, sustainable clinical impact requires transparent validation, regulatory alignment, clinician oversight, and continuous post-deployment monitoring.</p>

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The Role of Artificial Intelligence in Revolutionizing Medical Diagnostics: Current Trends, Challenges, and Future Prospects

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Indu Singh,
  • Udit Chaturvedi,
  • Aiman Usmani,
  • Shikha Baghel Chauhan,
  • Rajan Swami

摘要

Artificial intelligence (AI) has increasingly influenced medical diagnostics by enabling data-driven clinical decision support across imaging, genomics, and remote monitoring. This review critically examines the historical evolution, current clinical applications, and emerging innovations in AI-based diagnostics, while evaluating methodological rigor, validation practices, and implementation challenges. A structured narrative synthesis of peer-reviewed literature was conducted using major biomedical and engineering databases. Evidence suggests that AI, particularly deep learning, can achieve diagnostic performance comparable to clinicians in selected tasks; however, heterogeneity in datasets, limited external validation, and workflow integration challenges restrict generalizability. Ethical, regulatory, and human-centered considerations remain central to safe adoption. Overall, sustainable clinical impact requires transparent validation, regulatory alignment, clinician oversight, and continuous post-deployment monitoring.