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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 derive variables from clinical electronic health records. The benchmark consists of 28 tasks across 15 families using MIMIC-IV and eICU data. Initial tests with GPT 5.4 Mini and GPT 5.5 showed that AI skills improved derivation accuracy 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 full task completion remained a challenge. AI

IMPACT Establishes a new standard for evaluating AI's ability to handle complex, real-world clinical data derivation tasks.

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

Read on arXiv cs.AI →

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. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hannes Ill, Rafi Al Attrach, Rajna Fani, Ahram Han, Ryohei Yamamoto, Marlene Louisa Moerig, Dukyong Yoon, Leo Anthony Celi ·

    M4Bench: Evaluating Procedural Specification for Clinical EHR Derivation Agents

    arXiv:2610.37459v1 Announce Type: new Abstract: 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 conc…

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