Theora
Datasets and environments to help healthcare agents succeed at complex multi-step tasks
Theora provides datasets, training methods, and simulated environments for developing AI agents that handle healthcare tasks. The company states that current AI models perform well on standardized tests but struggle with the multi-step, context-dependent decision-making found in real clinical practice, where errors compound over long chains of decisions. Theora's approach covers supervised fine-tuning, reinforcement learning, and high-fidelity environments to help agents master valuable healthcare tasks.
Its supervised fine-tuning teaches models how experts reason across multi-step tasks such as navigating PACS, EHR, and multimodal data. Reinforcement learning uses expert-crafted rubrics grounded in real-world outcomes to generate scalable reward signals across realistic settings. The high-fidelity environments include FHIR-native EHR environments and PACS simulations for agent navigation, populated with de-identified real data.
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