arXiv:2606.02568v1 Announce Type: cross Abstract: Clinical practice is not the selection of an answer from enumerated options: a physician gathers heterogeneous information incrementally and commits to sequential, irreversible decisions under uncertainty. Static benchmarks cannot probe and existing interactive medical benchmarks each compromise on at least one of them. We present ClinEnv, an interactive benchmark that evaluates LLMs as attending physicians over real inpatient admissions under a paradigm we term Longitudinal Inpatient Simulation. Each case is automatically constructed into an o
The rapid advancement in Large Language Models (LLMs) and the increasing demand for robust evaluation methods in complex, real-world applications like healthcare make this benchmark timely.
This development is crucial for validating the capabilities of AI agents in high-stakes environments, potentially accelerating their deployment in medical practice and other critical sectors.
The ability to interactively evaluate LLMs in multi-stage, long-horizon scenarios moves beyond static benchmarks, allowing for a more realistic assessment of their decision-making and adaptive capabilities.
- · AI developers
- · Healthcare providers
- · Medical AI startups
- · Patients
- · Traditional medical diagnostics
- · Inefficient healthcare systems
- · Developers of static AI benchmarks
Improved AI agent performance in complex sequential decision-making tasks, particularly in healthcare.
Accelerated adoption of AI-driven diagnostic and treatment planning tools in clinical settings as confidence in their reliability grows.
Transformation of medical education and training to incorporate AI-assisted clinical reasoning, potentially leading to fully autonomous clinical agents over the long term.
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Read at arXiv cs.CL