Openai came out HealthAn open source evaluation framework designed to measure the performance and safety of large languages (LLM) models in realistic health care scenarios. Developed in collaboration with 262 doctors in 60 countries and 26 medical specialties, Healthbench addresses the limits of existing benchmarks by focusing on the applicability of the real world, validation of experts and diagnostic coverage.
Tackle gaps in comparative analysis in health care AI
The existing benchmarks for the AI of health care are generally based on close and structured formats such as multiple choice exams. Although useful for initial assessments, these formats fail to grasp the complexity and nuance of clinical interactions of the real world. Healthbench moves to a more representative evaluation paradigm, incorporating 5,000 multi-round conversations Between models and secular users or health professionals. Each conversation ends with a user prompt and model responses are assessed using Specifications specific to the example Written by doctors.
Each section consists of clearly defined – positive and negative criteria – with associated occasional values. These criteria capture behavioral attributes such as clinical accuracy, communication clarity, completeness and adherence to education. Healthbench evaluates 48,000 unique criteriaWith a rating managed by a level -based level -based level against expert judgment.
Reference structure and design
Healthbench organizes its evaluation on seven key themes: emergency references, global health, health data tasks, context search, communication contained by expertise, depth of response and response in uncertainty. Each theme represents a distinct challenge of the real world in medical decision -making and user interaction.
In addition to the standard reference, Openai has two variants:
- Healthbench consensus: A subset emphasizing 34 criteria validated by doctors, designed to reflect the critical aspects of the behavior of models such as advising emergency care or the search for additional context.
- Hard healthbench: A more difficult subset of 1,000 conversations selected for their ability to challenge current border models.
These components allow a detailed stratification of the behavior of the model by the type of conversation and the evaluation axis, offering more granular information on the capacities of the model and the gaps.
Model performance assessment
OPENAI has evaluated several models on Healthbench, including GPT-3.5 Turbo, GPT-4O, GPT-4.1 and the new O3 model. The results show marked progress: the GPT-3.5 has reached 16%, the GPT-4O reached 32% and the O3 reached 60% overall. Notably, GPT-4.1 NANOA smaller and profitable model has surpassed the GPT-4O while reducing the cost of inference of a factor of 25.
The performance varied according to the theme and the evaluation axis. Emergency references and tailor -made communication were areas of relative strength, while the search for context and exhaustiveness posed greater challenges. A detailed rupture revealed that exhaustiveness was the most correlated with the overall score, stressing its importance in tasks related to health.
OPENAI also compared the outputs of the model with answers written by doctors. Non -assisted doctors have generally produced responses at low score that models, although they can improve the drafts generated by the model, especially when working with previous models. These results suggest a potential role for LLM as collaborative tools in clinical documentation and decision -making assistance.
Reliability and meta-evaluation
Healthbench includes mechanisms to assess the consistency of the model. The metric “the worst to the maximum” quantifies the degradation of performance on several series. While new models have shown improved stability, variability remains a field for current research.
To assess the reliability of its automated level, Openai has made a meta-evaluation using more than 60,000 annotated examples. GPT-4.1, used as default level, corresponded or exceeded the average performance of individual doctors in most themes, suggesting its utility as a coherent evaluator.
Conclusion
Healthbench represents a technically rigorous and scalable executive to assess the performance of the AI model in complex health care contexts. By combining realistic interactions, detailed sections and a validation of experts, it offers a more nuanced image of the behavior of the model than existing alternatives. Openai published Healthbench via the Simple github repositoryOffering researchers tools to compare, analyze and improve models for health -related applications.
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