Manabendra Debnath^1*, Biplab De²

¹Department of Human Physiology, Kabi Nazrul Mahavidyalaya, Sonamura, Sepahijala, Tripura 799131, India
²Regional Institute of Pharmaceutical Science and Technology, Abhoynagar, Agartala, Tripura 799005, India

*Corresponding Author: Manabendra Debnath, Department of Human Physiology, Kabi Nazrul Mahavidyalaya, Sonamura, Sepahijala, Tripura 799131, India.

Received: April 27, 2026; Published: July 06, 2026

Background and Purpose: Artificial intelligence (AI) has emerged as a transformative force in modern healthcare, fundamentally reshaping diagnostics, therapeutics, drug discovery, personalised medicine and health systems management. This comprehensive review critically analyses current evidence on the application of AI technologies — including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision — across multiple domains of human health.

Methods: A systematic narrative review was conducted across PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and arXiv. Peer-reviewed literature published between 2015 and 2025 was considered. Studies were selected based on methodological rigour, clinical relevance, dataset size and validation quality.

Results: AI demonstrates remarkable and increasingly validated performance in medical imaging (lung cancer CT: AUC 0.944; diabetic retinopathy: sensitivity 97.5%), clinical decision support (EHR mortality prediction: AUC 0.83–0.95), drug discovery (AlphaFold2; halicin) and mental health monitoring. Wearable AI algorithms detect atrial fibrillation at population scale. However, significant challenges persist: algorithmic bias systematically disadvantages minority populations; the majority of published AI studies lack external validation; regulatory frameworks lag technological development and clinical workflow integration remains poorly realized.

Conclusions: While AI demonstrates transformative potential in human health, realizing this potential safely and equitably requires rigorous prospective validation, bias mitigation frameworks, fit-for-purpose regulatory pathways and genuinely integrated clinical deployment. The future of AI in medicine lies in multimodal foundation models augmenting — not replacing — human clinical judgement.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Medical Imaging; Algorithmic Bias; Electronic Health Records

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Citation

Citation: Manabendra Debnath and Biplab De. “Artificial Intelligence in Human Health: A Comprehensive Review". Acta Scientific Medical Sciences 10.7 (2026): 48-60.

Copyright

Copyright: © 2026 Manabendra Debnath and Biplab De. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.