Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.

Dental caries remains a widespread chronic disease, with early detection being vital to prevent lesion progression and tooth loss. Conventional diagnostic methods, such as visual-tactile examination and bitewing radiographs, have limited sensitivity for early lesions, particularly in children. Near-infrared light transillumination (NILT), as used in the DIAGNOcam system, provides a radiation-free and non-invasive alternative for detecting occlusal and proximal caries. Recently, deep learning approaches, especially convolutional neural networks (CNNs), have shown great promise in automating dental image analysis and improving diagnostic accuracy. This systematic review analyzed studies published from 2012 to 2025 on caries detection using DIAGNOcam images. A total of six clinical and three deep learning studies met the inclusion criteria. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. Clinical studies confirmed DIAGNOcam’s reliability for early lesion detection, while AI-based approaches achieved comparable or superior performance to traditional methods. However, limited datasets, small sample sizes, and inconsistent annotation standards restrict generalization. Future work should emphasize larger, standardized datasets and interpretable AI models. Combining DIAGNOcam imaging with deep learning may enable earlier, more objective, and patient-friendly caries diagnosis.