Autonomous Healthcare Diagnostics : A Multi-Modal AI Framework Using AWS SageMaker, Lambda, and Deep Learning Orchestration for Real-Time Medical Image Analysis

The exponential growth in medical imaging data coupled with radiologist shortages has created critical bottlenecks in healthcare diagnostics, particularly in emergency and rural settings where immediate expert analysis is unavailable. This paper presents a novel autonomous healthcare diagnostics framework leveraging AWS cloud infrastructure, combining Amazon SageMaker for distributed model training, AWS Lambda for serverless inference orchestration, and Amazon Bedrock for large language model integration. Our multi-modal approach integrates Vision Transformers, Convolutional Neural Networks, and Graph Neural Networks within a unified architecture that processes CT scans, MRIs, X-rays, and clinical metadata simultaneously. The system employs advanced feature fusion techniques using attention mechanisms, federated learning across multiple hospital networks, and real-time model adaptation through AWS Step Functions. Experimental evaluation on a dataset comprising 1.2 million medical images from 45 healthcare institutions demonstrates 94.7% diagnostic accuracy across 15 pathological conditions, with inference times averaging 2.3 seconds per case. The framework achieved 97.2% sensitivity for critical conditions requiring immediate intervention, while maintaining 91.8% specificity to minimize false positives. Integration with AWS HealthLake enables seamless electronic health record correlation, while HIPAA-compliant data processing ensures patient privacy. This research addresses the critical gap between AI model performance and clinical deployment requirements, providing a scalable, secure, and clinically validated solution for autonomous medical diagnostics.