Trust-aware clinical decision support in IoMT using hesitant fuzzy sets and deep learning-based medical image analysis

The Internet of Medical Things promises continuous monitoring for patients and intelligent clinical analysis; yet, an important challenge is how to make reliable decisions from uncertain, noisy, and possibly non-trustworthy data. Current methods typically focus on either medical image analysis or integrity assessment of connected medical devices, with limited reliability of automated clinical decision support. In this work, we propose a trust-aware framework using deep learning medical image analysis, reliability-adaptive hesitant fuzzy fusion, and multi-criteria decision ranking to overcome this limitation. The framework was assessed with the BraTS 2021 multimodal magnetic resonance imaging (MRI) dataset for patient level tumour burden classification. The deep image features were combined with controlled device-integrity attributes, and the interdependent decision criteria were globally weighted and then adapted on the patient level based on predictive uncertainty, temporal hesitation and image–integrity agreement. The proposed framework obtained 99.1% accuracy, 98.6% precision, 98.2% recall and 98.4% F1-score under mixed operating conditions. The performance was measured using the same protocol as the strongest deep-learning baseline evaluated, and found to be 4.3% higher in accuracy, 4.7% higher in precision, 5.0% higher in recall, and 4.9% higher in F1-score. Controlled communication and device degradation resulted in a 96.2% decision reliability, a 95.7% trust-index accuracy, and a 5.2% false diagnostic rate. The results demonstrate that the adaptive fusion mechanism enhances the classification of the tumor burden, as well as an operational level of trustworthiness in connected healthcare environments.