CARE-Bench: Benchmarking Patient-Facing LLM Triage
Researchers unveil CARE-Bench, a benchmark to evaluate how well large language models can triage patient symptoms and recommend next actions before human clinician review.
- CARE-Bench is the first benchmark specifically designed to evaluate sequential triage actions in patient-facing LLMs, addressing a critical gap in medical AI safety.
- The benchmark uses 500 real-world cases and 1,059 patient-disclosure prefixes reconstructed from medical dialogues to simulate realistic triage scenarios.
- 11 models were tested under unprompted and minimally prompted protocols, revealing performance gaps in action recommendation accuracy.
- The benchmark emphasizes source-grounded evaluation, ensuring models are assessed on medically appropriate next-step recommendations rather than just response plausibility.
A team of researchers has introduced CARE-Bench, a new benchmark designed to rigorously test the triage capabilities of patient-facing large language models (LLMs) in medical settings. The benchmark focuses on sequential decision-making, where models must determine the appropriate next action for a patient based on their disclosed symptoms. Unlike traditional benchmarks that evaluate static responses, CARE-Bench reconstructs 500 real-world cases and 1,059 patient-disclosure prefixes from medical dialogues, consultations, and follow-up questions to simulate dynamic, real-time triage scenarios.
The benchmark evaluates 11 models under two protocols: unprompted and minimally prompted open-ended settings. These protocols aim to assess how well models perform without heavy hand-holding, reflecting real-world conditions where clinicians may not provide detailed instructions. The evaluation covers 269 held-out rounds, providing a robust measure of each model's ability to recommend accurate next steps, such as seeking immediate care, scheduling a follow-up, or providing self-care advice.
The introduction of CARE-Bench comes at a time when patient-facing LLMs are increasingly being deployed in healthcare settings, often as preliminary triage tools before human clinician interaction. The benchmark's focus on source-grounded evaluation ensures that models are assessed not just on their ability to generate plausible responses but on their accuracy in recommending medically appropriate actions, a critical factor for patient safety and trust in AI-driven healthcare solutions.
Provides a standardized way to evaluate and improve triage capabilities in medical LLMs, ensuring safer deployment in healthcare settings.
Helps companies developing patient-facing AI tools demonstrate compliance with medical safety standards and gain clinician trust.
Highlights the growing importance of rigorous evaluation in medical AI, potentially influencing investment in safer, more reliable models.
Offers a real-world case study in applying LLMs to high-stakes sequential decision-making tasks in healthcare.
- Triage
- The process of determining the priority of patient care based on the severity of their condition.
- Source-grounded evaluation
- Assessing model responses by grounding them in verifiable medical sources or real-world cases to ensure accuracy.
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