{
  "abstract": "AI models trained on the same data can disagree about patient risk, with disagreement potentially concentrated in clinically important subgroups. We propose V(S), a statistically grounded vulnerability index combining an observable lower-bound witness of model disagreement with clinical severity, and develop inference and multiplicity-adjustment procedures for auditing prespecified subgroups. We applied the framework to two large critical-care cohorts, MIMIC-IV (n = 65,078) for model development and eICU-CRD (n = 188,230 admissions, 208 hospitals) for external validation, comparing a random forest with logistic regression across 158 prespecified subgroups. The two primary models did not both satisfy the prespecified epsilon = 0.02 Rashomon-set tolerance: the logistic-regression AUC was 0.0488 below the best candidate-model AUC. The RF-LR discrimination gap is thus interpreted as disagreement between two specific models, not as a guaranteed lower bound on the full Rashomon set. Nine subgroups had discrimination gaps distinguishable from a prespecified clinical floor. The age >=80 and cardiac subgroup had the largest point estimate of V(S) (0.307), but was underpowered and did not meet the full high-priority decision rule. The univariate cardiac subgroup (V(S) = 0.193, 95% CI [0.163, 0.223]) was the only statistically distinguishable subgroup with adequate power. Post hoc analyses identified lactate as important for both models but did not establish a causal explanation for the disagreement. Four simulation studies quantified operating characteristics of the proposed procedures, including inflated small-sample detection rates and imperfect Wald-interval coverage. The framework offers a reproducible approach for ranking subgroup vulnerability to model disagreement while separating exploratory signals from adequately supported findings.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37064",
  "author_total": 1,
  "authors": [
    {
      "name": "Enock Adu Bonsu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0mdrkfq20rq25.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37064",
  "fulltext_url": null,
  "id": "0mdrkfq20rq25",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37064",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7214979819",
  "pdf_url": "https://arxiv.org/pdf/2609.37064",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0mdrkfq20rq25.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:41Z",
      "native_id": "2609.37064",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37064"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A statistical framework for identifying subgroup vulnerability to predictive multiplicity in clinical AI",
  "title_text": "A statistical framework for identifying subgroup vulnerability to predictive multiplicity in clinical AI",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:41Z",
  "url": "https://research.draconitic.com/paper/0mdrkfq20rq25",
  "venue": "arXiv",
  "versions": []
}