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Artificial intelligence (AI) has emerged as a transformative force in healthcare diagnostics, revolutionizing traditional approaches and offering unprecedented opportunities for improving patient outcomes. This review of literature delves into the multifaceted role of AI in diagnostics, encompassing its applications, benefits, challenges, and future directions. The review surveys a wide range of studies and articles published in reputable journals, focusing on the utilization of AI techniques such as machine learning and deep learning in various diagnostic domains. It explores AI's capabilities in interpreting medical images, analyzing clinical data, and predicting disease risk with remarkable accuracy and efficiency. Furthermore, the review highlights the significant benefits associated with AI -driven diagnostics, including enhanced diagnostic accuracy, reduced turnaround times, and personalized treatment recommendations tailored to individual patient profiles. AI's ability to handle large volumes of data and detect subtle patterns not discernible by human observers has the potential to revolutionize disease detection and management. However, the review also acknowledges the challenges and limitations inherent in AI diagnostics, such as concerns regarding data privacy, algorithm bias, and the need for rigorous validation and regulatory oversight. Ethical considerations surrounding the use of AI in healthcare settings, includingtransparency, accountability, and equity, are also discussed.
Shifan Khanday Maya Ahmed (Tue,) studied this question.