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Revolutionizing personalized medicine using artificial intelligence: a meta-analysis of predictive diagnostics and their impacts on drug development
3
Zitationen
8
Autoren
2025
Jahr
Abstract
= 91.01%), attributed to differences in model architecture, diagnostic domains, and data quality. Subgroup analyses showed that convolutional neural networks and random forest models achieved higher AUC values, while domains like endocrinology demonstrated greater performance variability. Funnel plot inspection and sensitivity analysis indicated the presence of publication bias. AI shows strong potential to enhance diagnostic accuracy in personalized laboratory medicine. Nonetheless, methodological heterogeneity and publication bias remain significant challenges. Future research should prioritize standardized evaluation frameworks, transparency, and the development of explainable AI systems to ensure responsible clinical integration.
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