HealthBench: Evaluating Large Language Models Towards Improved Human Health
This addresses the need for realistic, open-ended evaluation of AI models in healthcare, benefiting developers and practitioners, though it is incremental as it builds on existing benchmarking approaches.
The authors introduced HealthBench, an open-source benchmark with 5,000 multi-turn conversations and 48,562 rubric criteria to evaluate large language models in healthcare, showing improvements from GPT-3.5 Turbo's 16% to GPT-4o's 32% and o3's 60% scores.
We present HealthBench, an open-source benchmark measuring the performance and safety of large language models in healthcare. HealthBench consists of 5,000 multi-turn conversations between a model and an individual user or healthcare professional. Responses are evaluated using conversation-specific rubrics created by 262 physicians. Unlike previous multiple-choice or short-answer benchmarks, HealthBench enables realistic, open-ended evaluation through 48,562 unique rubric criteria spanning several health contexts (e.g., emergencies, transforming clinical data, global health) and behavioral dimensions (e.g., accuracy, instruction following, communication). HealthBench performance over the last two years reflects steady initial progress (compare GPT-3.5 Turbo's 16% to GPT-4o's 32%) and more rapid recent improvements (o3 scores 60%). Smaller models have especially improved: GPT-4.1 nano outperforms GPT-4o and is 25 times cheaper. We additionally release two HealthBench variations: HealthBench Consensus, which includes 34 particularly important dimensions of model behavior validated via physician consensus, and HealthBench Hard, where the current top score is 32%. We hope that HealthBench grounds progress towards model development and applications that benefit human health.