Abstract: Rare diseases present a unique diagnostic challenge. With only a handful of identified cases and wide-ranging clinical manifestations, these diseases rarely appear in the differential diagnosis of physician decision-support systems. Consequently, they are infrequently considered at the initial visit. Lack of awareness thus leads to severe, irreversible complications, considerable impairment or even death, ultimately resulting in a loss of human life comparable to that of common diseases like breast cancer. A real-time diagnostic service for several rare diseases—stargardt disease, idiopathic pulmonary fibrosis, systemic lupus erythematosus, scleroderma, Crohn disease, and Cushing syndrome—based on federated learning and cloud artificial intelligence can help overcome the problem. The objective of the federated-learning service is to train an artificial-intelligence model at each hospital site without collecting or sharing sensitive data in a central cloud. The multi-institutional architecture is designed to produce collaborative real-time diagnoses without the large time lags associated with multisite diagnosis requested from typical cloud-based platforms.
The proposed research framework guarantees that highly sensitive data from different locations remains on-site during the training process, can receive real-time predictions through the AI model of other sites, and thus supports local specialists in correctly diagnosing rare diseases. A federated approach minimizes the potential presence of low-quality data and enhances the diagnostic reliability of models used to support the decision-making process. Given the richness of medical data from different areas supplied by different medical centers, the approach is applicable across a broad range of federated-learning scenarios.

Keywords: Rare Disease Diagnosis, Federated Learning in Healthcare, Privacy-Preserving Medical AI, Distributed Clinical Decision Support, Real-Time Diagnostic Services, Multi-Institutional AI Architectures, Collaborative Model Training, Sensitive Medical Data Protection, Cloud-Based Artificial Intelligence, Federated Diagnostic Frameworks, Clinical Decision Support Systems (CDSS), Cross-Site Medical Learning, Diagnostic Reliability Enhancement, Low-Prevalence Disease Detection, Medical Data Heterogeneity, Secure AI Model Exchange, Hospital-Based AI Deployment, Federated Prediction Services, Trustworthy Medical AI, Scalable Federated Healthcare Systems.

Downloads: | DOI: 10.17148/IJIREEICE.2023.111218