LLM-as-a-Judge in Healthcare: A Scoping Analysis of Applications, Methods, and Human Alignment

Lingyao Li, Deyi Li, Chen Chen, Renkai Ma, Runlong Yu, Mingquan Lin, Rui Yin, Lizhou Fan, Cathy Shyr, Siyuan Ma, Mei Liu, Steven Bethard/LLM-as-a-Judge in Healthcare: A Scoping Analysis of Applications, Methods, and Human AlignmentUnknown

Large language models (LLMs) are increasingly deployed across healthcare applications, including clinical documentation, diagnostic reasoning, medicine recommendation, and medical education. Their outputs are largely unstructured clinical text, which is difficult to reliably evaluate at scale. LLM-as-a-Judge, in which an LLM evaluates another system's output against task-specific criteria, offers a scalable alternative and is increasingly used in clinical evaluation, yet its validity in healthcare remains underexamined. Existing reviews focus on general-purpose LLM evaluation or on risk framework, rather than systematically characterizing how LLM-as-a-Judge is applied in healthcare and how well their judgments align with human experts. We therefore conduct a PRISMA-guided comprehensive review of LLM-as-a-Judge applications in healthcare, searching five databases for studies published between January 2023 and February 2026. After screening 541 records, 134 studies meet the eligibility and are coded by health scenario, judge configuration, technical approach, and validation design. LLM-as-a-Judge is concentrated in clinical decision support, clinical natural language processing (NLP), medical knowledge and question answering (QA), and medical communication. OpenAI models are the most frequently used judges, and prompt engineering appears in nearly all studies, with ensemble, multi-agent, and retrieval-augmented designs as common extensions. Among studies reporting human validation, LLM judges often show moderate to strong alignment with expert judgments, although reliability varies substantially across tasks. Overall, this review positions LLM-as-a-Judge as a promising framework for scalable healthcare AI evaluation, while emphasizing that its clinical value depends on model design and rigorous validation.

agent app

Stars0

Forks0

HF Downloads—30d

Last commit—

Refreshed4d ago

Project healthUnknownNo activity data.

Production readinessResearch / EarlyBest for exploration and prototyping.

Risk notesUnknown licenseVerify license before production use.

AgentHub Score

48 / 100

Composite score from 6 signals. How we score →

Active project

48Score

Growth

40C

Activity

30C

Documentation

70C+

Maturity

45C

Community

42C

Production

58C

GitHub stars · 90 days0 +0.0%

30d90d1y

Commit activity · 52 weeksActive contributor activity

LowHigh

JunSepDecMarNow

Practical assessment

Should you use it?

✓ Best for

Research and experimentation
Prototype development
Learning agentic patterns

◎ Strengths

Active community
Open source
Well-documented API

✕ Not ideal for

Untested at scale without validation
Teams without AI/ML expertise

⚠ Watch-outs

Review changelog before updating
Verify license for commercial use

Technical details

What's inside

Language—

License—

Sourcearxiv

Open source✗ No

Commercial use—

Docs—

Demo—

PaperarXiv ↗

AgentHub Score

Score 48/100

Below average

Alternatives

crewai

26.1k · Multi-Agent

autogen

42.7k · Multi-Agent

smolagents

11.2k · Coding

openai-agents-python

9.4k · Multi-Agent

Compare all →

Recent activity

Latest commit ——

Indexed by AgentHub crawler4d ago

Monitor for new releasesongoing