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New M4Bench benchmark tests AI agents on clinical EHR data derivation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed M4Bench, a new benchmark designed to evaluate the ability of AI coding agents to accurately derive variables from clinical electronic health records (EHRs). The benchmark consists of 28 tasks across 15 families using MIMIC-IV and eICU data. Initial tests using GPT 5.4 Mini and GPT 5.5 showed that AI agents with procedural specification skills improved performance by 0.136, with specification completeness being the most significant factor. A replication using Claude Code with Opus 4.7 and Sonnet 4.6 yielded similar positive results, though the gains represent partial agreement with reference data rather than fully completed tasks. AI

IMPACT This benchmark could improve the reliability and accuracy of AI agents used in clinical research by providing a standardized evaluation method.

RANK_REASON The cluster describes a new academic benchmark for evaluating AI agents on a specific task (clinical EHR derivation). [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New M4Bench benchmark tests AI agents on clinical EHR data derivation

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The cluster describes a new academic benchmark for evaluating AI agents on a specific task (clinical EHR derivation). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    M4Bench: Evaluating Procedural Specification for Clinical EHR Derivation Agents

    Clinical research over electronic health records depends on derived variables whose definitions sit outside the schema, in item codes, time windows, missingness rules, and output-grain choices that change cohorts and downstream conclusions. We introduce M4Bench, 28 clinical deriv…

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