arXiv:2605.21496v1 Announce Type: new Abstract: Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmarks miss the failure modes that matter in emergency medicine: trajectory-level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforcement-learning environment that rewards trajectory-level safety under realistic emergency-medicine conditions, adapted from Corecraft. It is built on a FHIR R4 world state with 14 entity types
The rapid deployment of frontier language models into sensitive clinical workflows necessitates robust safety evaluation frameworks, which this research addresses.
This development creates a crucial safety evaluation environment, mitigating risks associated with AI deployment in high-stakes fields like emergency medicine and potentially accelerating wider clinical adoption.
The ability to rigorously test AI models for trajectory-level safety, tool misuse, and performance under pressure in a simulated emergency medicine environment will improve AI reliability and trust.
- · AI safety researchers
- · Healthcare AI developers
- · Patients
- · Regulatory bodies
- · AI models with unaddressed safety issues
- · Developers neglecting safety in clinical AI
HealthCraft provides a standardized, public platform for evaluating and improving the safety of AI agents in healthcare.
Improved AI safety benchmarks could accelerate the adoption and integration of AI into critical medical processes, leading to better patient outcomes.
The methodology developed could extend beyond healthcare, influencing safety standards for AI agents across other high-risk sectors.
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Read at arXiv cs.LG