Background: Artificial intelligence (AI) is reshaping biomedical diagnostics by enabling automated pattern recognition across imaging, genomic, and electronic health record (EHR) data at a scale and speed previously unattainable.
Objective: This review synthesises current evidence on AI-based diagnostic systems, evaluating their clinical performance and identifying barriers to widespread adoption.
Methods: A structured narrative review was conducted across peer-reviewed literature, focusing on deep learning architectures, medical image analysis, and clinical decision support systems (CDSS) validated in human clinical settings.
Results: AI models — particularly convolutional neural networks (CNNs) and transformer architectures — consistently achieve diagnostic accuracy exceeding 90% and AUC values above 0.93 across radiology, pathology, cardiology, and ophthalmology. Performance is comparable or superior to specialist clinicians in several domains.
Conclusion: AI-based diagnostics demonstrate substantial clinical promise; however, algorithmic bias, regulatory uncertainty, and integration challenges remain critical obstacles. Prospective validation and ethical governance frameworks are required before universal deployment.