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Data & Model Evaluation

What is Healthcare AI Model Evaluation | SCILabel

What is Healthcare AI Model Evaluation?

Building a healthcare AI model is not enough — it must be shown to work safely, accurately, and fairly before it touches a patient pathway. SCILabel's Evaluation service deploys domain-expert clinicians to evaluate AI model outputs using rigorous, structured methods that go far beyond automated metrics. We provide the human-in-the-loop oversight that regulators increasingly require and that patients deserve.

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Evaluation Services | SCILabel (Carousel)

Evaluation Services

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RLHF — Reinforcement Learning from Human Feedback

Clinicians compare AI-generated responses side by side and select the preferred response, rate individual responses on structured rubrics, and provide written rationale for their assessments. Preference data is exported in reward-model-ready format (JSON). Used for LLM fine-tuning, clinical chatbot improvement, and AI documentation assistant optimisation.

Clinical Accuracy Evaluation

Domain-expert evaluators assess model outputs against clinician ground truth and published clinical guidelines. Metrics include sensitivity, specificity, PPV/NPV, AUC, and Cohen's Kappa. Used for diagnostic AI, clinical decision support, and medical coding automation.

Red-Teaming & Adversarial Safety Testing

Structured adversarial testers probe AI systems with challenging, ambiguous, and edge-case queries designed to surface unsafe, biased, or hallucinated outputs. Findings are classified by severity (Critical/Major/Minor) and delivered with a remediation priority report. Mandatory before deployment of patient-facing AI.

Bias & Fairness Assessment

Evaluators analyse model performance disaggregated by demographic subgroups (age, sex, ethnicity, socioeconomic status, geography) to detect differential performance. Fairness metrics include demographic parity, equalised odds, and calibration across subgroups. Delivered with a bias analysis report and mitigation recommendations.

Dataset Quality Evaluation

Before training begins, evaluators assess dataset completeness, label accuracy, class balance, demographic representativeness, and annotation consistency. Identifies issues that would degrade model performance and provides a dataset quality score with remediation recommendations.

Regulatory Compliance Audit

Track 5 specialists audit annotation datasets and model evaluation evidence against regulatory frameworks — FDA SaMD, EU AI Act, HIPAA, GDPR — and produce documentation packages to support regulatory submissions.

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