Physician-validated evaluation datasets for clinical AI. Test reasoning, not retrieval.
Fabrica Evaluation helps clinical AI teams build gold-standard benchmarks that measure what matters: clinical reasoning under uncertainty, not knowledge retrieval from training data. Our physician network authors, validates, and continuously refreshes evaluation cases sourced from real clinical encounters.
The result is evaluation datasets that resist contamination, test multi-step inference, and provide multi-dimensional scoring — so you know whether your model is actually ready for clinical use.
Public medical benchmarks are failing. Models score ~90% on board-style MCQ benchmarks but 35.7% on real-world clinical scenarios. The gap comes from data contamination, reliance on multiple-choice formats, and disconnection from actual clinical practice.
If you're evaluating a clinical model against public benchmarks, you likely don't know how it will perform on the tasks that matter. Fabrica Evaluation gives you that answer.
Specify the clinical domain, task type, and reasoning complexity you need to test. We work with you to define what “good” looks like for your specific model and use case.
Board-certified physicians create evaluation cases derived from real clinical encounters — complete with clinical context, expected reasoning chains, calibrated difficulty levels, and multi-dimensional scoring rubrics.
Every case is independently validated by additional physicians for clinical plausibility, reasoning defensibility, and appropriate difficulty. Cases that don't pass validation are revised or discarded.
Evaluation sets are versioned and periodically updated with new cases to resist contamination and reflect evolving clinical guidelines. Version history is preserved for longitudinal performance tracking.
Evaluation cases that never touch the public internet — sourced from de-identified clinical encounters and physician-authored scenarios. No overlap with LLM training corpora.
Every case includes physician-validated reasoning — not just answer keys. Evaluate whether your model reaches the right answer through the right process.
Score models on accuracy, safety, uncertainty awareness, reasoning quality, and robustness — not just final-answer correctness. Understand where your model is strong and where it breaks.
Cases span the difficulty spectrum — from floor validation to specialist-level reasoning to genuinely ambiguous presentations. Distinguish between models at different capability levels.
Test whether your clinical model is ready for real-world use before deployment — with evaluation data that reflects actual clinical complexity.
Compare foundation models, fine-tuned variants, or different architectures on the same physician-validated evaluation set. Make model selection decisions based on clinical reasoning quality, not benchmark scores.
Track model performance over time with versioned evaluation sets. Detect regression, measure the impact of fine-tuning rounds, and maintain confidence in deployed systems.
Learn more about why clinical AI evaluation is broken and how to fix it in our Building Gold-Standard Evaluation Sets guide.
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