January 29, 2026

Evaluations

Open & Proprietary Benchmarks

Rigorous healthcare AI evaluation requires combining open and proprietary benchmarks to balance transparency, comparability, and real-world clinical utility.

Written by Golda Manuel, PharmD., MS

Co-founder and CEO, Quantiles

AI evaluation has come a long way from the days when “what’s the accuracy?” was the only question that mattered. Today, it has expanded into a sprawling ecosystem of open benchmarks, proprietary eval suites, regulatory guidance, and constantly evolving industry standards. And yet, for many teams, evaluation still feels fragmented and opaque, especially in healthcare, where “correct” depends on context, data is tightly regulated, and edge cases are the rule rather than the exception.

Healthcare AI evaluation has moved beyond abstract correctness to asking how systems behave under real-world context, uncertainty, and consequence.

In clinical settings, performance claims have to withstand scrutiny from clinicians, patients, regulators, and payers. Unlike consumer AI, healthcare systems operate under safety, equity, and accountability constraints that demand far more testing, monitoring, and reporting. These realities are why healthcare AI teams rely on benchmarks of all kinds - both open and proprietary - to evaluate systems rigourously, identify edge cases, and build evidence that holds up in real-world clinical and regulatory contexts.

Open vs. Proprietary Benchmarks

Open benchmarks are publicly available datasets and evaluation protocols that anyone can inspect, run, and critique, making them a foundational tool in healthcare AI research and development. Common examples we mention in our Benchmark Hub include PubMedQA, HELM, and MMLU. A more recent example is OpenAI’s HealthBench, which uses an LLM-as-a-judge approach to evaluation.

The value of open benchmarks is that they're accessible and transparent. They establish shared reference points that allow teams to compare approaches, reproduce results, and speak a common evaluation language across organizations. Performance claims have to be defensible in healthcare and these benchmarks anchor discussions in evidence that others can independently verify.

Pros and Cons of Open Benchmarks

Pro

Con

Independent replication of results

Often rely on static, curated datasets that don't evolve with clinical practice

Cross-model comparison

May overrepresent academic, English-language, or well-documented contexts

Community-driven error analysis and critique

Rarely reflect real clinical workflows or constraints

Early detection of systemic bias across models

Vulnerable to optimization for benchmark scores over real-world performance

Proprietary benchmarks in comparison are internally developed evaluation datasets and protocols designed to reflect a specific organization's data, workflows, and risk profile. In healthcare AI, they often incorporate real clinical notes, operational constraints, and edge cases that rarely appear in public datasets.

Relevance is the primary strength of proprietary benchmarks, because they can be tailored to the healthcare variables that matter most in practice, such as patient populations, care settings, geographic location, clinical workflows, and institutional practices. While they lack the broad comparability of open benchmarks, proprietary evaluations are essential for stress-testing models against realistic clinical scenarios and generating evidence that is directly applicable to deployment, ongoing monitoring, and regulatory review.

Pros and Cons of Proprietary Benchmarks

Pro

Con

High clinical relevance by reflecting real patient populations, care settings, and workflows

Limited external comparability across organizations or models

Evaluation of deployment-specific risks that rarely appear in public datasets

Reduced transparency and reproducibility outside the owning institution

Stress-testing against realistic edge cases and failure modes seen in practice

Risk of reinforcing local bias if dataset reflect narrow populations or practices

Evidence generation aligned with regulatory and operational needs

Higher cost and maintenance burden to curate, update, and govern responsibly

Combining Open vs. Proprietary Benchmarks

Robust healthcare AI evaluations rarely rely on a single class of benchmarks. Open and proprietary benchmarks serve complementary roles, and teams deliberately plan their use to balance transparency, comparability, and real-world relevance. When used together, disagreement between the two can sometimes be more informative than agreement: strong performance on open benchmarks paired with weak proprietary benchmark results, for example, often signals domain shift, workflow mismatch, or unexamined assumptions in model design. Interpreting these benchmarks correctly is one of the most critical and challenging parts of benchmarking.

Open benchmarks typically form the foundation. Running models against well-known public benchmarks early in development helps teams understand baseline behavior, identify obvious failure modes, and communicate performance in a language the broader research and regulatory community recognizes.

The strongest healthcare AI evaluation strategies use both open and proprietary benchmarks.

Proprietary benchmarks build on that foundation with real-world relevance, reflecting target populations, workflows, and operational constraints while stress-testing safety-critical behavior and performance under distribution shift. This is where evaluation becomes tied to risk management, post-deployment monitoring, and regulatory readiness.

Open & Proprietary Benchmarks Are Complimentary

OPEN

Shared reference points

Cross-model comparability

Community scrutiny

Early weakness detection

Reusable for peer review and baseline validation

PROPRIETARY

Institution-specific data and workflows

Local distribution fidelity

Deployment-critical behavior testing

Operational risk detection

Combining benchmarks also improves traceability. Open benchmarks anchor evaluation in methods others can inspect, while proprietary benchmarks show how those methods translate to specific clinical contexts. Together, they create an evaluation record that others can understand and teams can act on. This is increasingly becoming an important requirement as more healthcare AI systems move from research into regulated, real-world use.

FAQs

Common questions this article helps answer

Are open benchmarks sufficient for evaluating healthcare AI systems?▼

Open benchmarks are essential for transparency, reproducibility, and cross-model comparisons, but they're insufficient on their own. They rarely capture real clinical workflows, population heterogeneity, or deployment constraints. In practice, they're often combined with proprietary benchmarks to build a strong foundation for evaluations.

Why do models sometimes perform well on open benchmarks but poorly in deployment?▼

This usually signals domain shift or workflow mismatch. Public benchmarks often reflect curated, academic, or English-language data that differs from real clinical environments. When proprietary evaluations reveal weaker performance, it often exposes unexamined assumptions in model design, training data, or intended use.

When should teams introduce proprietary benchmarks into the evaluation process?▼

As early as the realistic deployment scenario can be defined. Open benchmarks are useful for establishing baseline behavior early in development, but proprietary benchmarks should be introduced once teams begin making decisions about populations, workflows, or institutional-specific practices. Waiting until late-stage validation often delays the discovery of critical failure modes.

How should discrepancies between open and proprietary benchmark results be interpreted?▼

Discrepancies are signals. Strong public benchmark results paired with weak internal performance often point to issues like population mismatch or operational constraints. These gaps are often more informative than agreement and can be used to guide targeted model iteration and risk mitigation.

What does 'rigorous evaluation' actually mean in a regulated healthcare context?▼

Evaluation rigor comes from intentionally selecting, sequencing, and carefully interpreting benchmarks. A rigorous strategy produces evidence that is externally interpretable, internally actionable, and traceable over time. This supports cross-model comparison, deployment decisions, monitoring, and regulatory scrutiny.