Journal of Advances in Medicine and Medical Research

Background: Artificial intelligence (AI) and machine learning (ML) are increasingly used to support earlier diagnosis, risk stratification, clinical decision-making, and personalised care. However, an operational gap persists between algorithmic performance and safe bedside implementation.

Aim: This narrative review synthesises clinically relevant evidence on AI-enabled early diagnostics, precision therapeutic pathways, and the principal structural barriers to responsible adoption.

Methods: Contemporary biomedical and technical literature was identified through structured searches of major bibliographic, engineering, preprint, policy, and regulatory sources. The review focused on cross-specialty evidence concerning medical imaging, electronic health record data, predictive analytics, clinical decision support, precision medicine, algorithmic bias, explainability, data governance, and medical education.

Findings: Across the reviewed literature, AI and ML approaches support earlier recognition of clinically relevant patterns, improved risk stratification, and more individualised therapeutic decision support. Applications are most mature in diagnostic imaging, but increasingly extend to multimodal clinical data and electronic health records. Translation into routine care remains constrained by limited interpretability, dataset representation bias, evolving governance requirements, and uneven AI literacy among clinicians and medical trainees.

Conclusion: AI is best positioned as an augmentation of clinical judgement rather than a replacement for it. Safe and equitable implementation requires reliable validation, representative data, accountable human oversight, and sustained integration of health informatics and AI literacy into medical education.