Publication date: Apr 16, 2025

AI-enhanced precision medicine improves cancer detection, treatment outcomes, and personalization by analyzing imaging and pathology data, offering more accurate predictions and reducing health care disparities. Insufficient data limit the models ability to generalize beyond the training examples, resulting in overfitting to the specific data set and poor performance on unseen or diverse data. Key Studies Employing AI Methodologies Across Various Cancer TypesConclusionThe integration of AI and ML technologies has transformed health care, providing innovative tools for disease diagnosis and treatment planning. AI-driven personalized treatment, leveraging models like NAFNet, is improving prostate cancer risk stratification by accurately predicting biochemical recurrence and adverse pathology from MRI scans. However, in the field of precision medicine, such data sets are often scarce due to the complexity and multifaceted nature of biological data. Moreover, AI-powered telemedicine and virtual care have significant potential to improve health care access, particularly in underserved regions. Algorithmic biases have profound implications in precision medicine, where unequal representation in data sets can exacerbate existing health disparities.

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