{
  "abstract": "Background: Artificial-intelligence-based diagnostic systems increasingly influence healthcare delivery, yet strong aggregate performance can coexist with clinically meaningful disparities across demographic subgroups. Such disparities raise not only model-development concerns but also health-equity, implementation, and governance challenges for healthcare organizations. Methods: We conducted a systematic review reported in accordance with PRISMA 2020 of literature published from January 2019 through August 2026. The search identified 1842 records; 87 records were retained in the overall evidence corpus and 46 underwent structured quantitative extraction. The corpus included primary empirical/model-evaluation evidence together with secondary or adjacent methodological evidence used for qualitative contextualization. We also developed FRCS-10, a ten-item descriptive reporting-completeness index derived from established reporting and governance guidance, and applied it to the full corpus. Results: Peer-reviewed primary sources reported baseline true-positive-rate gaps of 0.062-0.281 across sex, age, and race on CheXpert and MIMIC-CXR, while a contextual dermatology meta-analysis reported AUROC values of 0.89 for Fitzpatrick I-III and 0.82 for Fitzpatrick IV-VI. These values were synthesized as source-reported estimates and were not independently re-analysed from patient-level data. Data representation was the most frequently coded bias source (38%), followed by algorithmic design (21%) and label or annotation bias (19%). The descriptive FRCS-10 mean was 4.6/10; only 7 of 87 records described post-deployment fairness monitoring. Five primary empirical studies with sufficiently explicit estimands were assessed for compatibility; their disparity definitions were not commensurable, so no pooled effect size was computed. Conclusions: We propose the Equity-Aware Bias Detection and Mitigation Framework (EA-BDMF), a literature-derived, implementation-oriented lifecycle specification for healthcare AI. It links data auditing, prevalence-aware preprocessing, fairness-constrained training, calibration and threshold governance, and post-deployment monitoring. The framework is accompanied by a Lifecycle Equity Debt (LED) accounting measure, checkpoint documentation, a Bias Provenance-Mitigation Traceability Matrix, and descriptive regulatory mapping. The complete framework has not yet been validated end to end, and its clinical and organizational utility requires prospective evaluation in real-world healthcare settings.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Reyadh Alluhaibi",
      "orcid": "0000-0001-5794-1002"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/02n04mw77cgf4.bib",
  "cited_by": 0,
  "doi": "10.3390/healthcare14193293",
  "fulltext_url": "https://europepmc.org/articles/PMC13665230",
  "id": "02n04mw77cgf4",
  "issns": [
    "2227-9032"
  ],
  "landing_url": "https://europepmc.org/article/MED/42881732",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3311|Ethics and Social Impacts of AI"
    ]
  ],
  "openalex": "W7215983111",
  "pdf_url": "https://europepmc.org/articles/PMC13665230?pdf=render",
  "pmcid": "PMC13665230",
  "pmid": "42881732",
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/02n04mw77cgf4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42881732",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42881732"
    }
  ],
  "subjects": [],
  "title": "Algorithmic Bias in AI-Based Diagnostic Models: A Systematic Review and Lifecycle Framework for Fairness Auditing and Governance in Healthcare.",
  "title_text": "Algorithmic Bias in AI-Based Diagnostic Models: A Systematic Review and Lifecycle Framework for Fairness Auditing and Governance in Healthcare.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:00Z",
  "url": "https://research.draconitic.com/paper/02n04mw77cgf4",
  "venue": "Healthcare (Basel, Switzerland)",
  "versions": []
}