AI-Driven Federated and Transfer Learning Platform for Health Predictions

Abstract

Medical negligence, errors, and delayed diagnoses lead to preventable deaths and serious health issues. These problems are mainly caused by a shortage or lack of access to qualified healthcare professionals. In the U.S. alone, medical errors cause an estimated 44,000 to 98,000 deaths annually. The rise of artificial intelligence in healthcare has introduced new opportunities for more accurate diagnoses and predictions, with numerous artificial intelligence (AI)-driven tools now available for detecting diseases such as cancer, heart conditions, lung diseases, and liver diseases. While these tools have revolutionized diagnostics, they are primarily designed for healthcare professionals, limiting their accessibility to the general public. Providing patients with a reliable platform to help them accurately assess their health condition can be transformative, reducing medical errors, enhancing patient engagement, and improving overall care. This study introduces E-Doctor, a Generative AI-powered platform designed to provide reliable second opinions for medical diagnoses. Utilizing advanced AI technologies such as BioBERT for analyzing medical texts and federated learning for maintaining data privacy, the platform addresses the shortage of professional healthcare advice. E-Doctor platform achieved high accuracy rates: 92.4% for heart attack-related advice, 90.2% for full-body checkups, and 93.9% for Deep Vein Thrombosis prediction. By combining medical image analysis with AI-driven learning models, e-Doctor offers a robust platform for enhancing patient outcomes, while ensuring data privacy and security.