ClinEval Benchmark
Measuring Clinical AI Quality
A systematic evaluation framework for assessing LLM responses in clinical practice. Six weighted dimensions, 84 test cases, and asymmetric error weighting that prioritizes patient safety above all else.
Six Dimensions of Clinical Quality
Each dimension is weighted by clinical importance. Safety detection carries the highest weight because missing an emergency has irreversible consequences.
Safety Detection
Evaluates emergency and urgent situation recognition with asymmetric error weighting—missing emergencies is penalized heavily.
Triage Accuracy
Measures accuracy of clinical classification, severity assessment, and module triggering across conditions.
Escalation Quality
Assesses human handoff decisions—timing, accuracy, and the critical balance between false positives and missed escalations.
Response Appropriateness
Evaluates clinical accuracy, guideline adherence, tone appropriateness, and absence of harmful content.
Confidence Calibration
Measures reliability of confidence scores—a well-calibrated system should be right 70% of the time when it reports 70% confidence.
Contextual Coherence
Tests multi-turn consistency, RAG context utilization, and proper use of patient history.
Comprehensive Test Coverage
84 expert-authored test cases spanning emergency detection, clinical triage, adversarial inputs, and domain-specific scenarios.
Emergency Detection
Explicit, implicit, multilingual emergencies
Triage Scenarios
Single-condition, comorbidity, age-specific
Escalation Edge Cases
Non-response, sentiment volatility, cumulative risk
Response Quality
Guidelines, cultural sensitivity, mental health
Adversarial Inputs
Prompt injection, misleading symptoms, jailbreaks
Domain: Cardiology
Heart failure, AFib, hypertension, anticoagulation
Domain: Mental Health
Depression, anxiety, crisis intervention
Domain: Diabetes
Hypo/hyperglycemia, complications, lifestyle
Clinical-First Methodology
Unlike generic LLM benchmarks, ClinEval is built for healthcare—where the cost of errors is asymmetric and patient safety is non-negotiable.
Asymmetric Weighting
Missing an emergency is penalized 10x more than a false alarm. The scoring reflects real clinical consequences.
Latency Requirements
Emergency detection must complete in under 100ms. Clinical AI can't afford to be slow when seconds matter.
Baseline Tracking
Compare results against baselines to catch regressions before they reach patients. CI/CD integration ready.
Part of the Digital Twin Ecosystem
ClinEval integrates with TherapyPod's synthetic patient simulation. Run benchmarks against the same infrastructure that powers real clinical conversations.
Medical Safety Engine
Emergency and urgent detection with multilingual support (English, Hindi, code-switching).
Triage System
Module-based classification with confidence scoring and escalation recommendations.
Escalation Rules
Context-aware human handoff decisions with SLA tracking and notification routing.