Research Prototype

Clinical AI that
explains itself.

ShifaMind predicts ICD-10 diagnoses from clinical notes while providing concept-grounded explanations clinicians can trust and verify. Explainability by design — not afterthought.

0.4727
Macro-F1
115K
Clinical Notes
50
ICD-10 Codes
1.0
Completeness
Architecture
How ShifaMind Works
Every prediction flows through human-interpretable clinical concepts. No shortcuts. No black boxes.
01
📄

Text Encoding

BioClinicalBERT encodes discharge summaries into rich clinical representations

02
🧠

Concept Grounding

Cross-attention grounds 113 clinical concepts (symptoms, findings, treatments)

03
✕

Multiplicative Gate

Concept activations multiply with diagnosis embeddings — zero concepts = zero signal

04
📋

Diagnosis + Evidence

ICD-10 predictions with RAG-retrieved clinical evidence for full audit trail

Evaluation
Performance & Interpretability
Competitive diagnostic accuracy with full concept-mediated transparency — evaluated on MIMIC-IV.
Macro-F1 (Top-50 ICD-10)
Tuned Threshold
ShifaMind
0.4727
LAAT
0.4637
Vanilla CBM
0.4398
CAML
0.4287
MSMN
0.4165
GPT-5.4
0.4122
Claude 4.6
0.3556
Interpretability Metrics
Enforced
1.000
Completeness
0.904
TCAV Score
0.744
Faithfulness
Concept Completeness = 1.0 means every prediction is fully mediated by clinical concepts. The multiplicative bottleneck mathematically guarantees: if concepts are zero, the diagnosis signal is zero.
Demo
Sample Prediction Output
A simulated ShifaMind inference on a clinical discharge summary.
shifamind_inference.py — Patient #48201
› Input: Discharge Summary (1,247 tokens)
"72M admitted with progressive dyspnea, bilateral lower extremity edema, and elevated BNP. CXR showing bilateral pleural effusions. Started on IV furosemide with improvement..."
› Activated Concepts:
edema
0.94
diuretics
0.91
cardiac
0.88
dyspnea
0.86
pleural_effusion
0.79
› Prediction:
[1] I50.9 — Heart failure, unspecified conf: 0.92
[2] J91.8 — Pleural effusion conf: 0.78
[3] I10   — Essential hypertension conf: 0.71
› RAG Evidence:
▸ WHO I50.9: "Condition in which the heart cannot pump sufficient blood..."
▸ Case Prototype: "73F with CHF exacerbation, bilateral edema, BNP >2000..."
What's Next
Future Vision
Building toward the future of transparent clinical decision support.
Multi-Agent Specialist Network
The next evolution of ShifaMind: dedicated AI agents each specializing in a disease category — Cardiology, Pulmonary, Renal, Neurology — delivering immensely high accuracy and explainability per domain, orchestrated by a central routing agent.
❤️

Cardiology Agent

Deep specialization in heart failure, arrhythmias, and vascular conditions with cardiology-specific concept vocabularies

🫁

Pulmonary Agent

Expert-level reasoning over respiratory conditions — COPD, pneumonia, pleural diseases

🧬

Renal Agent

Specialized in kidney pathology with lab-value-aware concept grounding and AKI staging

🧩

Orchestrator Agent

Routes clinical cases to the right specialist, aggregates multi-domain findings, resolves conflicting signals

Founder
About
Mohammed Sameer Syed
Founder · AI/ML Engineer
Building ShifaMind at the intersection of clinical AI and explainability. Focused on creating systems where every diagnostic prediction is transparent, verifiable, and grounded in medical knowledge — because clinicians shouldn't have to trust a black box.
🎓 M.S. Information Science 🏥 MIMIC-IV · BioClinicalBERT · UMLS