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Abstract

The study examines how the use of AI, used in clinical spaces and general public health measures, may change medical care and addressing global health barriers. This research investigated how AI methods such as machine learning and predictive modelling could not only enhance the diagnostic process, experiment with bespoke treatment approaches, avoid illnesses, and enhance efficiency in resource distribution. In the research performance of AI algorithms was evaluated with the use of four algorithms. “To analyze the data, research used various algorithms, including SVM, RF, NN, and KNN. In the light of the investigation, SVM had the highest accuracy rate at 92%, followed by RF at 89%, NN at 86%, and finally KNN at 84%.” More specifically, the results indicate that AI is central in addressing critical global health challenges such as climate change, NCDs, and pandemic prevention initiatives. Among the key outcomes of the research was the finding that AI can detect upcoming outbreaks of disease and help in smarter policy-making, providing for optimal resource allocation. The researchers state that AI holds great promise for reinventing healthcare through greater efficiency, more accuracy, and wider reach of information. Although challenges persist, especially because of fair access to AI, there is a considerable benefit for the future health policy evolution due to the possibility of AI changing the world of health systems entirely