aceArtificial Intelligence Models for Early Disease Detection Using Clinical Data
In a variety of chronic and acute diseases, earlier detection of disease is a major determinant of better patient outcomes, better treatment costs savings and reduced mortality.The current diagnostic process, mostly manual, manual interpretation of clinical data, is limited by the capacity of the human mind, by inter-observer variability and by the amount of data produced by modern healthcare systems.Artificial Intelligence (AI) that leverages machine learning (ML) and deep learning (DL) capabilities has become…
In simulated experiments, ensemble and deep learning models, especially XGBoost and CNN, forecast disease more accurately than traditional statistical models on clinical data.
The same higher-performing ensemble and deep learning models are less interpretable and more resource-demanding than traditional statistical models, which complicates their use in clinical settings.
The performance findings come from simulated experiments rather than real-world clinical deployment, and the paper notes unresolved issues with privacy, class imbalance, explainability, and clinical integration.
Evidence
- Peer-reviewedInternational Journal of Drug Delivery Technology2026-10-14
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Truvace Impact Record TRV-2026-1479, v1: “Artificial Intelligence Models for Early Disease Detection Using Clinical Data.” Truvace, 2026-10-15. /record/TRV-2026-1479 (accessed at citation time). sha256 1ee8fe075342d920…
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