Uncertainty-Aware Neural Network Framework for Reliable Medical Diagnosis and Prognosis

Abstract

Recent developments in artificial intelligence have greatly improved diagnostic and predictive outcomes in healthcare; yet, most current deep learning algorithms make deterministic predictions without uncertainty assessment, which reduces their effectiveness in critical decision-making. In this work, we present an uncertainty-aware neural network approach that combines probabilistic inference with deep learning to improve diagnostic certainty and confidence. The approach incorporates Bayesian inference through Monte Carlo dropout, to quantify uncertainty along with classification predictions. The model accounts for both model and data uncertainties, enhancing decision-making in the context of uncertain medical data. The proposed framework achieves better accuracy, calibration and confidence prediction performance on medical datasets compared to traditional neural networks. This finding underscores the benefits of uncertainty-aware learning in mitigating overconfidence and increasing confidence in AI-based medical systems. This research offers a scalable approach to decision support for dependable diagnosis and prognosis.