# OpenAIs HealthBench in Action: Evaluating an LLM-Based Medical Assistant on Realistic Clinical Queries

arXiv: https://arxiv.org/abs/2509.02594 · HTML: https://mlsanity.com/paper/2509.02594/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild](https://mlsanity.com/paper/2406.04770/) (2024)
- [MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs](https://mlsanity.com/paper/2501.17399/) (2025)
- [Sequential Diagnosis with Language Models](https://mlsanity.com/paper/2506.22405/) (2025)

## Built on by (most cited first, 5 of 5)

- [How Inference Compute Shapes Frontier LLM Evaluation](https://mlsanity.com/paper/2606.17930/) (2026)
- [MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional](https://mlsanity.com/paper/2605.24699/) (2026)
- [Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages](https://mlsanity.com/paper/2605.30529/) (2026)
- [A Living Benchmark for Information Retrieval from Electronic Health Records](https://mlsanity.com/paper/2609.30205/) (2026)
- [A Proposed Rubric for Evaluating Expressed Clinical Reasoning in Large Language Model Responses](https://mlsanity.com/paper/2609.37788/) (2026)

## Abstract (arXiv)

Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios. Traditional evaluations are often limited to multiple-choice questions that fail to capture essential competencies such as contextual reasoning, contextual awareness, and uncertainty handling. To address these limitations, we evaluate our agentic RAG-based clinical support assistant, DR. INFO, using HealthBench, a rubric-driven benchmark composed of open-ended, expert-annotated health conversations. On the Hard subset of 1,000 challenging examples, DR. INFO achieves a HealthBench Hard score of 0.68, outperforming leading frontier LLMs including the GPT-5 model family (GPT-5: 0.46, GPT-5.2: 0.42, GPT-5.1: 0.40), Grok 3 (0.23), Gemini 2.5 Pro (0.19), and Claude 3.7 Sonnet (0.02) across all behavioral axes (accuracy, completeness, instruction following, etc.). In a separate 100-sample evaluation against similar agentic RAG assistants (OpenEvidence and Pathway.md, now DoxGPT by Doximity), it maintains a performance lead with a HealthBench Hard score of 0.72. These results highlight the strengths of DR. INFO in communication, instruction following, and accuracy, while also revealing areas for improvement in context awareness and response completeness. Overall, the findings underscore the utility of behavior-level, rubric-based evaluation for building reliable and trustworthy AI-enabled clinical support systems.