Artificial intelligence (AI) has emerged as a revolutionary tool in healthcare, significantly improving the ability to predict patient outcomes. From chronic disease management to personalized treatments, AI can analyze vast datasets and provide timely, accurate predictions that guide clinical decision-making. Traditional statistical models have been useful but often limited in handling complex data relationships. AI, through machine learning (ML) and deep learning (DL), transcends these limitations, offering improved predictions across diseases like cancer, tuberculosis, and HIV/AIDS. This paper investigates current AI methods, advantages, challenges, and ethical considerations associated with its use in healthcare, highlighting the transformative potential of AI in outcome prediction while addressing concerns around data privacy, regulatory constraints, and model transparency.
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Achieng M. Nyambura (2024) studied this question.
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