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Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions

Hanjie Chen, Zhouxiang Fang, Yash Singla, Mark Dredze · Feb 28, 2024 · Citations: 0

Human Eval
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations. However, medical board exams or general clinical questions do not capture the complexity of realistic clinical cases. Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of model decisions, a crucial component of supporting doctors in making complex medical decisions. To address these challenges, we construct two new datasets: JAMA Clinical Challenge and Medbullets. Datasets and code are available at https://github.com/HanjieChen/ChallengeClinicalQA. JAMA Clinical Challenge consists of questions based on challenging clinical cases, while Medbullets comprises simulated clinical questions. Both datasets are structured as multiple-choice question-answering tasks, accompanied by expert-written explanations. We evaluate seven LLMs on the two datasets using various prompts. Experiments demonstrate that our datasets are harder than previous benchmarks. In-depth automatic and human evaluations of model-generated explanations provide insights into the promise and deficiency of LLMs for explainable medical QA.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 30%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations."

Evaluation Modes

partial

Human Eval

Includes extracted eval setup.

"LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations."

Quality Controls

missing

Not reported

No explicit QC controls found.

"LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Both datasets are structured as multiple-choice question-answering tasks, accompanied by expert-written explanations."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: Medicine, Coding

Evaluation Details

  • Evaluation modes: Human Eval
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations.
  • However, medical board exams or general clinical questions do not capture the complexity of realistic clinical cases.
  • Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of model decisions, a crucial component of supporting doctors in making complex medical decisions.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Human evaluation) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • We evaluate seven LLMs on the two datasets using various prompts.
  • Experiments demonstrate that our datasets are harder than previous benchmarks.
  • In-depth automatic and human evaluations of model-generated explanations provide insights into the promise and deficiency of LLMs for explainable medical QA.

Why It Matters For Eval

  • Experiments demonstrate that our datasets are harder than previous benchmarks.
  • In-depth automatic and human evaluations of model-generated explanations provide insights into the promise and deficiency of LLMs for explainable medical QA.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Human Eval

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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