ShifaMind predicts ICD-10 diagnoses from clinical notes while providing concept-grounded explanations clinicians can trust and verify. Explainability by design - not afterthought.
BioClinicalModernBERT encodes discharge summaries into rich clinical representations
Cross-attention grounds 111 clinical concepts (symptoms, findings, treatments)
Concept activations multiply with diagnosis embeddings - zero concepts = zero signal
ICD-10 predictions with concept-level confidence scores for full transparency
Doctors can analyze real clinical notes, explore concept activations, and discuss cases with AI - all grounded in ShifaMind’s interpretable predictions.
Deploying specialized AI agents for different clinical domains alongside a central orchestrating agent.
Deeply integrating transparent reasoning models with live hospital EHR data streams and sensors.
Dynamically generating evidence-based medical graphs and grounding predictions in structured knowledge.
Expanding the concept-based reasoning architecture to incorporate medical imaging and structured lab data.
Current clinical AI is black-box. ShifaMind clears the interpretability gap by achieving state-of-the-art predictive performance without compromising transparency — every prediction remains fully mediated by human-interpretable clinical concepts.
Clinical NLP and medical coding automation is a massive, multi-billion dollar market. Transparent, explainable AI that clinicians fundamentally trust is the critical unlock for widespread clinical adoption and deployment.
Novel architecture: multiplicative concept bottleneck with cross-attention fusion. Outperforms all current baselines while maintaining perfect interpretability. Under review, not published.