Part I: Foundations of Healthcare AI Evaluation

In healthcare, the barrier to AI deployment is rarely ambition, creativity, or model size - it's evaluation. The problem isn't the lack of benchmarks, but understanding what they measure and analyzing the results deeply enough to determine production readiness. This gap is especially visible in LLM-based clinical systems, where traditional metrics such as BLEU, ROUGE, or F1 cannot capture the multidimensional safety and clinical reasoning behaviors required for real-world use.

A 2021 seminal paper by Suresh and Guttag cataloged six places in the ML lifecycle where harm can be introduced - from data preparation to model evaluation and beyond - and highlighted why narrow benchmarks often fail to detect these risks. Their argument is even more relevant in the era of generative AI: broad, targeted evaluation is the only way to build modern, safe, reproducible, and clinically trustworthy systems.

The limiting factor in healthcare AI is neither dataset nor model size, but evaluation quality, because only rigorous, targeted benchmarks can expose the safety and reasoning failures that matter in clinical use.

How Modern AI Benchmarks Are Run in Healthcare

Healthcare AI evaluation has matured into a pipeline discipline, not just a metrics discipline. High quality evaluations require high quality data provenance tooling, prompt and hyperparameter controls, model-level instrumentation, and more. All the appropriate technologies must be packaged in such a way that evaluations are fully reproducible.

Benchmarking begins with task definition, a relatively underrated step in evaluation. A underspecified or otherwise imprecise task definition will distort every downstream metric, but a precise, well-specified one creates the foundation to test the right capability and trust the results, enabling trustworthy model comparison.

Task formulations in clinical AI often are derived from general model capabilities, including:

• Classification: predicting categories (e.g., “symptom present / absent,” “appropriate / inappropriate medication”).
• Information extraction: pulling structured information from unstructured data (e.g. clinician notes).
• Summarization: producing a summary or summaries of an input (e.g. patient timelines, or evidence synopses), without losing clinical nuance.
• Structured prediction: Producing complex, schema-conforming outputs (e.g., ICD-10 codes, PMR/SOAP components) that can be used to predict a patient outcome, generally from unstructured, often long-form inputs.

Benchmarks for these tasks must clearly specify what the model sees, what it should output, how correctness is defined, and what qualifies as a safety violation.

Equally important in defining a task is interpretability, which covers:

• Transparency — what features or source passages influenced the output?
• Traceability — can we see what the model retrieved or reasoned over?
• Failure mode clarity — does the model fail safely?

Foundational work by Doshi-Velez & Kim (2017) and Lipton (2018) formalized interpretability as a scientific discipline, when it was previously considered to be an afterthought to raw model performance. In healthcare, interpretability research is critical to help clinical experts quickly determine whether an error is benign (e.g., the model chose a slightly different phrasing) or clinically dangerous (e.g., the model had all relevant data but still produced an unsafe conclusion).

Input generation

Evaluations are almost always based on input data, often referred to as prompts. In some cases, prompts will be readily available. Examples of such prompts include de-identified production data or public datasets from published benchmarks.

In many cases, however, there will be no readily available prompts with which to run an evaluation. In these cases, large language models (LLMs) or other text-generation models will be used in a pipeline to generate a corpus of prompts. These pipelines are generally focused only on generating prompts, but in advanced cases, they can incorporate synthetic or augmented data (e.g. to produce a corpus that reflects a distribution of patients). Regardless of how inputs are acquired or generated, it's critical to use diverse input sets that reflect the real-world distribution a model will encounter.

Generally speaking, a bigger set is not necessarily better, but often, more samples are required to capture the target distribution, and the inputs must be high-quality. High-quality prompts can themselves be evaluated by the following criteria:

• Task format: does the corpus represent the type of data the model will actually see (e.g., clinician notes, lab panels, MRI images)?
• Relevant context: does the corpus include relevant, domain-specific information? Often, these data can be pulled from vector stores using RAG so each prompt includes relevant evidence.
• Correct formatting: are prompts structured exactly as the model expects? Often, formatting aligns with clinical documentation standards.

Hyperparameters

Prompts, hyperparameters, and other aspects of model configuration can all materially influence generated behavior, and they must be specified as part of the input set. While input generation involves some science (ensuring target distributions are met, etc.) and some non-scientific activities (getting prompts right, using human experts, etc.), hyperparameters are defined completely by the formulae below:

• Temperature: controls randomness in token selection by scaling each logit by a positive, nonzero number as we apply softmax to convert them to a probability distribution. Higher values increase diversity (and hallucination risk), while lower values make outputs more deterministic.
  • If $p_i(T)$is defined as a temperature-controlled probability distribution of tokens, then $p_i(T) = \frac{e^{z_i / T}}{\sum_j e^{z_j / T}}$.
  • Note that if $T = 1$, then $p_i(T)$ reduces to softmax.
• Top-p (nucleus sampling): restricts token choices to the most likely subset. Often used to exclude extreme or unexpected generations.
  • To get the top-p token choices, we apply (possibly temperature-controlled) softmax on logits to get a token distribution $p_1, p_2, ..., p_n$, then sort the tokens such that$p_1 \gt p_2 \gt ... \gt p_n$, then find the smallest $k$ such that$\sum_{i=1}^{k} p_i \gt p_{threshold}$, where $threshold$ is our target top-p value.
  • After we have our top-p $p_i$ values, we re-normalize them with another softmax and sample from the adjusted distribution.

A benchmark is only as valid as its inputs, which means prompt construction, retrieval context, formatting, and fixed hyperparameters must all be controlled with the same discipline as model execution.

For benchmarking to be scientifically valid, all inputs and all hyperparameters must be fixed and versioned. Deterministic settings are essential for reproducibility and without them, benchmark results cannot be trusted or compared.

Execution of model-under-test

The way we execute benchmarks directly affects both performance and reproducibility. System-level settings - such as batching, concurrency, and latency instrumentation - must be measured and controlled, because they can influence token distributions, throughput, and the availability and consistency of results.

After a benchmark run, we must analyze a set of quantitative signals that describe how the model actually behaved.

At a minimum, these data should be collected during benchmarking:

Assuming we have all these data, we're then ready to take the measurements we need. We'll be left with a (sometimes large!) set of numbers that we need to convert into information about how our model is likely to perform in the real world. Regardless of what measurements we take (e.g., the benchmarks we run), we often use statistical tools to help us understand what we've measured:

• Variance estimation: measures how much outputs fluctuate across repeated runs due to the stochasticity of a model, and how temperature, top-p, and other hyperparameters affect these outputs. Variance across multiple otherwise-identical benchmark runs is a direct indicator of reliability.
• Confidence intervals: specify the range where the true performance metric likely falls (e.g., 95% CI), relative to the observed metric, helping distinguish meaningful differences from statistical noise.
• Bootstrapping: a resampling method that estimates variability and confidence intervals without assuming any specific distribution.

Rigorous benchmarks provide standardized inputs and consistent evaluation conditions, producing quantitative signals that allow us to assess model behavior. We start here to build a toolkit for analyzing these signals and understanding how they translate to real-world deployment. Part 2 of this post will explain the specific benchmarks we can run and how to use their results to evaluate robustness, safety, bias, and true production readiness.