Authors: Alfiya Mehboob Kazi, Prof. S. K. Said, Prof. M. C. Kolse
Abstract: Artificial Intelligence (AI) is reshaping healthcare by accelerating diagnosis, shortening parts of the drug-development cycle and making treatment decisions more individual. This review consolidates evidence from four connected areas: medical imaging, drug discovery, personalized medicine, and the ethical and regulatory conditions under which these tools can be trusted. Imaging is the most mature domain, where deep convolutional networks now detect abnormalities, triage urgent studies and improve reconstruction, while pathology, cardiology and neurology are rapidly following. In drug discovery, learning-based models help identify targets, generate candidate molecules and select trial participants, although gains are concentrated in early pipeline stages. Personalized medicine uses electronic health records and multi-omics data to estimate individual treatment response. Alongside these benefits, the paper analyses risks concerning patient privacy, dataset bias, opacity and accountability, and maps them to the Indian context of the Ayushman Bharat Digital Mission, the Digital Personal Data Protection Act 2023 and national AI strategy documents. A layered reference architecture for responsible clinical AI is proposed, together with a research gap analysis and objectives. The review concludes that healthcare AI should be judged by measurable improvement in patient outcomes, supported by prospective validation, secure data practice, fairness auditing and human oversight.
DOI: https://doi.org/10.5281/zenodo.24037844
