@inproceedings{singh-etal-2026-max,
    title = "{MAX}-{EVAL}-11: A Large Scale Benchmark for Evaluating Large Language Models on Full-Spectrum {ICD}-11 Medical Coding",
    author = "Singh, Ujjwal  and
      Deshwal, Sarthak  and
      Dube, Nitish  and
      Sharma, Arjun",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.bionlp-1.23/",
    doi = "10.18653/v1/2026.bionlp-1.23",
    pages = "282--291",
    ISBN = "979-8-89176-434-7",
    abstract = "The global transition to the ICD-11 taxonomy demands robust automated medical coding, yet comprehensive benchmarks to evaluate Large Language Models (LLMs) on this task remain absent. We introduce MAX-EVAL-11, the first large-scale benchmark for full-spectrum ICD-11 medical coding. MAX-EVAL-11 comprises 10,000 MIMIC-III discharge summaries with mapped, expert-validated ICD-11 annotations spanning 99.87{\textbackslash}{\%} of the diagnostic taxonomy. To better reflect clinical utility, we propose a novel hierarchical evaluation framework that assigns partial credit based on ICD-11{'}s 5-level structure, addressing the brittleness of traditional exact-match metrics. Our evaluation of state-of-the-art LLMs reveals significant performance gaps. The best-performing model (Claude 4 Sonnet) achieves a weighted score of 0.433, outperforming both general-purpose peers and specialized medical models (MedCoder). Crucially, all models exhibit near-zero exact match rates (0?4.8{\textbackslash}{\%}) and rely primarily on hierarchical credit, underscoring the extreme difficulty of precise ICD-11 code generation. Furthermore, the superiority of general-purpose LLMs over legacy ICD-10 medical models (with ICD-11 codelist) suggests that broad reasoning capabilities currently outweigh domain-specific training for complex taxonomy scaling."
}