arXiv:2606.08969v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing error-detection methods produce heuristic or uncalibrated scores, providing no formal control over missed errors and no principled way to trade off safety against clinician review burden. We introduce Conformal Assessment for Risk Evaluation (CARE), a post-hoc, model-agnostic safety layer that uses conformal risk control to overlay calibrated omission and hallucinatio
The increasing deployment of LLMs in sensitive domains like medicine necessitates robust safety mechanisms to mitigate risks and gain public trust.
Ensuring the safety and reliability of AI in medical applications is critical for widespread adoption and prevents potentially harmful outcomes from erroneous outputs.
This development introduces a formal, calibrated approach to managing risks in medical AI summarization, moving beyond heuristic error detection.
- · Healthcare providers
- · Patients
- · AI safety researchers
- · LLM developers in healthcare
- · Developers of uncalibrated AI safety methods
- · LLMs without robust safety layers
Increased trust and adoption of AI systems within validated medical workflows.
Development of industry standards for AI safety layers in critical applications, potentially extending beyond medicine.
Reduced regulatory hurdles for AI deployment in healthcare due to demonstrable safety controls, accelerating AI integration into patient care.
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Read at arXiv cs.AI