ShifaMind predicts ICD-10 diagnoses from clinical notes while providing concept-grounded explanations clinicians can trust and verify. Explainability by design — not afterthought.
BioClinicalBERT 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.
Deep specialization in heart failure, arrhythmias, and vascular conditions with cardiology-specific concept vocabularies
Expert-level reasoning over respiratory conditions — COPD, pneumonia, pleural diseases
Specialized in kidney pathology with lab-value-aware concept grounding and AKI staging
Routes clinical cases to the right specialist, aggregates multi-domain findings, resolves conflicting signals
Current clinical AI is black-box. ShifaMind is the first system with mathematically guaranteed concept completeness (1.0) — every prediction is fully mediated by human-interpretable clinical concepts.
Clinical NLP and medical coding automation is a multi-billion dollar market. Hospital systems spend billions on manual coding. Interpretable AI that clinicians trust is the unlock for widespread adoption.
Novel architecture: multiplicative concept bottleneck with cross-attention fusion. Outperforms vanilla concept models by 7.5% while maintaining perfect interpretability. Published methodology, defensible IP.