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What is Medperf?

MedPerf is an open-source framework for benchmarking medical ML models. It uses Federated Evaluation a method in which medical ML models are securely distributed to multiple global facilities for evaluation prioritizing patient privacy to mitigate legal and regulatory risks. The goal of Federated Evaluation is to make it simple and reliable to share ML models with many data providers, evaluate those ML models against their data in controlled settings, then aggregate and analyze the findings.

The MedPerf approach empowers healthcare stakeholders through neutral governance to assess and verify the performance of ML models in an efficient and human-supervised process without sharing any patient data across facilities during the process.

Federated evaluation of medical AI model using MedPerf on a hypothetical example

Why MedPerf?

MedPerf aims to identify bias and generalizability issues of medical ML models by evaluating them on diverse medical data across the world. This process allows developers of medical ML to efficiently identify performance and reliability issues on their models while healthcare stakeholders (e.g., hospitals, practices, etc.) can validate such models against clinical efficacy.

Importantly, MedPerf supports technology for neutral governance in order to enable full trust and transparency among participating parties (e.g., AI vendor, data provider, regulatory body, etc.). This is all encapsulated in the benchmark committee which is the overseeing body on a benchmark.

Benchmark committee in MedPerf

Benefits to healthcare stakeholders

Anyone who joins our platform can get several benefits, regardless of the role they will assume.

Benefits to healthacare stakeholders using MedPerf

Our paper describes the design philosophy in detail.