National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Artificial intelligence (AI) is changing the way healthcare is delivered, from disease prevention and diagnosis through to treatment, monitoring, drug discovery, and public health management. Techniques such as machine learning, deep learning, natural language processing, computer vision, and generative AI are now capable of analyzing the large, complex datasets that modern medicine produces, including electronic health records, medical images, genomic data, laboratory results, and information from wearable devices. Used well, these tools can sharpen diagnostic accuracy, bring disease detection forward in time, support more personalized treatment, and ease the workload carried by clinicians. AI has already been applied to cancer, cardiovascular disease, diabetes, infectious disease, neurological disorders, and eye disease, among other conditions. Its adoption, however, brings real challenges: data quality and bias, privacy, cybersecurity, clinical validation, regulation, and unequal access all need to be addressed before AI can be trusted at scale. AI is best understood as a tool that augments clinical judgement rather than replaces it, and its responsible use depends on human oversight, transparent evaluation, multidisciplinary collaboration, and ongoing monitoring after deployment. This review sets out the main applications of AI in health and disease, along with its benefits, limitations, ethical implications, and likely future direction.
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