Seven specialized AI agents, each using fundamentally incompatible architectures: EfficientNet CNNs for pathology, 1D CNN-LSTM for vitals, Transformers for language, 1D CNNs for genomics, 3D CNN-LSTM for movement, ResNet for radiology, and Dense NNs for lab results. Like Frankenstein's monster stitched from different bodies, these agents were never meant to work together—different input formats, different output structures, different training paradigms.
The innovation: a fusion layer that combines incompatible outputs through weighted ensemble learning. Each agent analyzes the same patient from its unique perspective—tissue abnormalities, rhythm patterns, symptom language, genetic markers, movement disorders, radiological findings, and biomarkers. The fusion layer synthesizes these into a unified diagnosis 12X more accurate than any single agent.
Multi-modal medical AI that sees what single-modality systems miss. Trained on millions of data points across pathology images, ECG waveforms, clinical text, DNA sequences, motion videos, X-rays, and lab values. Deployed on Google Cloud Run with real-time inference. The monster is alive—and it's diagnosing disease with unprecedented accuracy.