LLM-as-a-judge Evaluations

Large language models are increasingly being used to evaluate other models. This practice, often called LLM-as-a-judge, has moved from experimental curiosity to a core component of modern AI evaluation pipelines. Nowhere is this shift more consequential than in healthcare, where choosing the right benchmarks can influence clinical safety, regulatory readiness, and patient trust.

LLM-as-a-judge refers to using a large language model to score, rank, or classify outputs produced by another AI system according to a defined rubric.

LLM-as-a-judge enables evaluation of behaviors that traditional metrics cannot capture, especially in clinically realistic scenarios.

Instead of relying solely on exact-match answers, similarity scores, or other numeric metrics, a judge model evaluates qualitative properties such as:

• Clinical appropriateness
• Safety and harm avoidance
• Completeness and relevance
• Calibration and uncertainty expression
• Alignment with clinical guidelines

In healthcare, this approach is especially useful because many tasks don't have a single correct answer. Clinical reasoning, triage decisions, and patient communication are inherently contextual, and cannot be easily captured by simpler numeric measurements.

The benefits of using LLMs as a judge

A 2024 review in Nature Digital Medicine reported that widely used evaluation metrics for healthcare LLMs often fail to generalize to real-world clinical settings, largely because they prioritize surface correctness over contextual performance. The review highlights gaps in assessing clinically meaningful properties, including appropriateness of recommendations, trust, and alignment with patient and clinician decision-making.

Newer benchmarks, including HealthBench and other healthcare-specific evaluation frameworks, rely on LLM-as-a-judge evaluations to address these limitations by assessing responses against clinician-defined criteria instead of surface similarity. See our more detailed breakdown of HealthBench.

The table below summarizes three persistent constraints in healthcare evaluation and how LLM-as-a-judge approaches can help address them.

How LLM-as-a-judge evaluations work

In healthcare AI, an LLM-as-a-judge evaluation is designed to assess model behavior under clinical uncertainty by translating expert judgment into a structured, machine-executable form. Rather than relying on surface-level correctness or fixed reference answers, a separate language model - operating under explicit rubric constraints - evaluates outputs against clinically grounded expectations. This approach enables consistent behavioral assessment of properties such as safety, appropriateness, reasoning quality, and uncertainty expression across large and heterogeneous test sets. In practice, these evaluations are embedded within controlled environments that enforce prompt determinism and rubric fidelity while enabling structured analysis and interpretation of evaluations to support reproducibility and downstream regulatory scrutiny.

An LLM-as-a-judge pipeline consists of four core components:

• 1. Scenario or prompt generation

  Prompts may be drawn from real clinical workflows, synthetic patient cases, or adversarial testing.

• 2. Model response generation

  The system under evaluation produces answers, recommendations, or explanations.

• 3. Rubric-based scoring by a judge-model

  A seperate LLM evaluates each response using predefined criteria, often with multi-axis scoring.

• 4. Aggregation and analysis

  Scores are summarized and aggregated across scenarios, themes, or risk categories.

LLM-as-a-judge makes clinical judgment scalable, structured, and auditable.

Importantly, the judge model is not free-form. High-quality LLM-as-a-judge evaluations depend on structured, explicitly defined rubrics that constrain how judgments are made and what dimensions of behavior are assessed. These rubrics are typically authored or reviewed by clinicians and encode domain-specific expectations such as appropriate escalation, guideline alignment, uncertainty handling, and safety boundaries.

By anchoring the judge model's reasoning to predefined criteria rather than open-ended preference, rubrics reduce evaluator drift, improve inter-run consistency, and make evaluation outcomes interpretable, auditable, and suitable for regulatory and clinical review.

Key risks of using LLMs as judges

The same properties that make LLM-as-a-judge evaluations attractive in healthcare (e.g., scalability, flexibility, and the ability to operationalize qualitative judgment) also introduce new classes of risk that are not present in traditional metric-based evaluation. Because judgment is mediated by another learned model rather than a fixed rule or ground-truth label, evaluation outcomes can be sensitive to factors such as model provenance, prompt framing, rubric interpretation, and implicit training biases.

As a result, LLM judges must be treated not as neutral evaluators, but as additional system components whose behavior require their own validation and controls.

LLM-as-a-judge is not a shortcut around rigorous evaluation, nor is it a replacement for human oversight. It is a mechanism for making judgment explicit, repeatable, and inspectable in domains where correctness or similarity alone is insufficient. When implemented using clinician-authored rubrics, controlled execution, and transparent reporting, LLM-as-a-judge benchmarking systems can surface how models behave under clinical uncertainty in ways traditional metrics cannot. The value of these systems depends on how carefully they are designed and validated, and how their results are interpreted.