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This study evaluated 11 models on MIMIC-IV under heterogeneous conditions (the full 7942-code space, top-50 self-trained baselines, and 200-admission zero-shot LLM samples) and proposed two revenue-sensitive metrics: the Revenue Sensitivity Index (RSI) and Coding Reimbursement Score (CRS). Performance was compared across US Medicare Severity DRG (MS-DRG) and Taiwan DRG (Tw-DRG) systems, with five human-AI review strategies simulated. PLM-ICD achieved the highest micro-averaged F1 (0.5934), while open-source zero-shot LLMs performed markedly worse in this exploratory comparison. A 26.5% CRS gap separated the best and worst fine-tuned models. Rankings were identical under both DRG schemes (Spearman\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20091.00), indicating stability under a tiered Tw-DRG approximation (93.9% coverage), not the official grouper. At a 20% review rate, revenue-targeted prioritization achieved 43.2% CRS reduction versus 20.0% for random sampling, reaching 91% of the oracle bound. Revenue-aware evaluation captures financially meaningful differences missed by standard metrics, and revenue-guided human-AI collaboration emerges as a candidate deployment framework requiring prospective validation.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-57682-0","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:23:38Z","timestamp":1781195018000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revenue-sensitive evaluation of AI-assisted ICD-10-CM coding and human-AI collaboration under dual DRG payment systems"],"prefix":"10.1038","volume":"16","author":[{"given":"Ming-I.","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Lin","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hsin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"57682_CR1","unstructured":"World Health Organization. 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