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@article{204340,
        author = {Shantanu Vedpathak and Santosh Tondare and Ajay Chopane and Ashish Agrawal and Vandana Agrawal and Sidharth Kamble and Ajay Shinde and Anand Kejkar},
        title = {Artificial Intelligence in Healthcare: Trends, Applications, and Governance a Bibliometric and Conceptual Framework Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9436-9443},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204340},
        abstract = {Artificial intelligence (AI) is rapidly transforming healthcare across diagnostics, therapeutics, operations, and governance. This review synthesizes evidence from contemporary literature to delineate: (i) the drivers and trajectory of AI adoption in health systems; (ii) core AI methods and their health applications (with emphasis on medical imaging, precision medicine, clinical decision support, and operational analytics); (iii) the ethical, legal, and social implications (ELSI) of AI in healthcare, including explain ability and trust; and (iv) challenges and future directions for research, regulation, and practice. We integrate perspectives from bibliometric analyses, systematic reviews, clinical studies, and policy-oriented work to present a cohesive, evidence-based view of how AI is shaping health outcomes, patient experiences, and health-system resilience. Where viewpoints diverge, we identify points of tension and areas needing further evidence. The synthesis draws on diverse sources spanning imaging, digital health, pharmacy, nursing, rehabilitation, and reproductive medicine to illustrate both the breadth and depth of AI-enabled healthcare.},
        keywords = {Artificial Intelligence; Healthcare; Machine Learning; Deep Learning; Bibliometric Analysis; Medical Imaging; Precision Medicine; Explainable AI},
        month = {June},
        }