Overcoming diagnostic and data privacy challenges in viral disease detection: an integrated approach using generative AI, vision transformers, explainable AI, and federated learning - Srinivasulu, Asadi; Agrawal, Anupam; Vedaiyan, Ramchand (Frontiers in Virology, 2025)
The growing dependence on artificial intelligence (AI) in healthcare has significantly advanced the detection and diagnosis of viral diseases. However, existing AI models encounter key obstacles such as data privacy concerns, limited interpretability, poor generalization, and overfitting, which restrict their practical application and broader adoption. This research tackles these issues by introducing an integrated framework that combines Generative AI, Vision Transformers, Explainable AI (XAI), and Federated Learning (FL) to improve diagnostic accuracy and safeguard data privacy. By utilizing Generative AI, the framework produces synthetic datasets that supplement limited medical data and bolster model resilience. Vision Transformers enhance the precision and efficiency of image-based disease detection. Explainable AI fosters transparency, ensuring that deep learning models’ decisions are clear and reliable for healthcare practitioners. Federated Learning facilitates decentralized model training, maintaining patient privacy while enabling collaborative learning across institutions. Experimental findings show that this framework enhances diagnostic accuracy in viral diseases, including COVID-19, while addressing privacy concerns and improving the interpretability of AI systems. This integrated approach offers a secure, transparent, and scalable solution to the critical challenges in AI-driven healthcare, providing real-time, effective disease detection and analysis.