Zavo

Train and evaluate medical AI models using clinical datasets, reinforcement learning environments, and expert benchmarks

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You can use Zavo to build, train, and test healthcare AI models with real clinical data and specialist environments. It connects AI teams to structured and unstructured medical records, such as EHRs, radiology scans, pathology slides, and clinician-patient conversations. The service offers interactive reinforcement learning environments grounded in actual clinical workflows, enabling agents to make diagnostic decisions and receive clinical feedback. Teams can also benchmark model performance using custom clinical evaluations and collaborate directly with a global network of medical doctors for data annotation and review.

What Zavo does

Source de-identified multimodal medical records for model trainingRun clinical reinforcement learning simulations for healthcare agentsEvaluate medical LLMs on evidence interpretation benchmarksCollect expert clinical annotations on medical imaging and notesTrack diagnostic model performance across multiple iterationsFormulate reward signals derived from real patient care outcomesAccess structured and unstructured clinical datasets across 95+ medical specialtiesTrain AI agents in interactive reinforcement learning environments based on patient careTest clinical reasoning and multi-step decision-making against verifiable medical evidenceAccess multimodal patient records linking notes, imaging, audio, and laboratory resultsBenchmark model performance against clinician-developed tasks and scoring rubricsConnect with a global network of vetted medical professionals for data annotation