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HFEPX · Eval paper review

HealMed: Multilingual Evaluation of Large Language Models in Medicine

Yingjian Chen, Fan Gao, Sherry T. Tong, Haoyu Zhang +41 more

Published

Aug 20, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Aug 20, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Critique Edit Medicine Multilingual

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.

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

Best use

Background context only

Use if you need

Background context only.

What to verify

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

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in each of nine languages, drawn from nine datasets and covering three task formats: MCQA, NLI and open-ended QA. The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions. Each translation was evaluated and revised by two experts fluent in English and the corresponding target language. On HealMed, performance declined most in low-resource languages, although the size of the gap varied markedly across languages and models. The strongest proprietary models were the most stable across languages, whereas many open-source and medically specialized models showed larger and less consistent gaps. Medical specialization alone did not ensure multilingual robustness. Furthermore, expert revision could either raise or lower measured performance, indicating that translation quality materially affects cross-language evaluation results.

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

partial

Critique Edit

Directly usable for protocol triage.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Quality Controls

missing

Not reported

No explicit QC controls found.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine."

Benchmarks and datasets

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

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Critique Edit
Rater population
Domain Experts
Expertise required
Medicine, Multilingual
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.

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

Key takeaways

  • We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
  • HealMed contains 1,000 examples in each of nine languages, drawn from nine datasets and covering three task formats: MCQA, NLI and open-ended QA.
  • The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • 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.

Recommended queries

Expert verification evaluation

Contribution summary

  • We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
  • The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions.
  • Furthermore, expert revision could either raise or lower measured performance, indicating that translation quality materially affects cross-language evaluation results.

Why it matters for eval

  • We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
  • The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Critique Edit

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.