By improving diagnostic precision, lowering clinician workload, and facilitating early illness identification, artificial intelligence (AI) is transforming healthcare. In order to enhance patient outcomes, cut mortality, and save healthcare expenses, early illness detection is essential. AI processes massive medical datasets, evaluates medical pictures, and helps physicians make clinical decisions by utilizing machine learning, deep learning, and predictive analytics. With a focus on applications in cancer, cardiology, neurology, infectious illnesses, and personalized medicine, this study explores current developments in AI-assisted diagnostics. The advantages AI offers medical practitioners are also covered, such as increased patient monitoring, less mistakes, and higher diagnosis accuracy. Despite these benefits, issues including algorithmic bias, high implementation costs, data privacy, inadequate physician training, and ethical considerations continue to be major obstacles. In order to maximize AI adoption in early illness diagnosis and healthcare delivery, the study concludes by examining future views and highlighting the necessity of cooperative research, policy frameworks, and integration techniques.
Abba et al. (Thu,) studied this question.