ClinEval Benchmark
Measuring Clinical AI Quality
A systematic evaluation framework for assessing LLM responses in clinical practice. Six weighted dimensions, 112 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
30% weightEvaluates emergency and urgent situation recognition with asymmetric error weighting—missing emergencies is penalized heavily.
Triage Accuracy
25% weightMeasures accuracy of clinical classification, severity assessment, and module triggering across conditions.
Escalation Quality
20% weightAssesses human handoff decisions—timing, accuracy, and the critical balance between false positives and missed escalations.
Response Appropriateness
15% weightEvaluates clinical accuracy, guideline adherence, tone appropriateness, and absence of harmful content.
Confidence Calibration
5% weightMeasures reliability of confidence scores—a well-calibrated system should be right 70% of the time when it reports 70% confidence.
Contextual Coherence
5% weightTests multi-turn consistency, RAG context utilization, and proper use of patient history.
Comprehensive Test Coverage
112 expert-authored test cases spanning emergency detection, clinical triage, adversarial inputs, and domain-specific scenarios.
Emergency Detection
Explicit, implicit, and multilingual emergency cases
Triage Scenarios
Clinical classification, severity, and module selection
Escalation Decisions
Required handoffs, non-response, and escalation timing
Response Quality
Required content, tone, and prohibited guidance
Adversarial Inputs
Prompt injection, misleading symptoms, and unsafe requests
Clinical-First Methodology
ClinEval applies asymmetric error weights because missed emergencies have greater clinical risk.
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
Teams can compare completed reports against an approved baseline. Automated release gating is not active.
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.