Abstract PR-04: A practical framework for operationalizing responsible and equitable AI in healthcare: Tackling bias, inequity, and implementation challenges
Notice bibliographique
Résumé
Abstract Background: Artificial intelligence (AI) is promising to rapidly transform healthcare by enhancing clinical workflows and improving patient outcomes. However, the integration of AI solutions also carries significant risk of harm due to discriminatory performance and inequitable outcomes across diverse patient populations. Existing frameworks aimed at promoting responsible AI development, such as SPIRIT-AI, CONSORT-AI, and TRIPOD+AI, provide guidelines for clinical trial design but lack concrete recommendations to identify and mitigate bias during clinical integration. Frameworks emphasizing ethical principles like equity, transparency, and accountability, including HEAAL, JustEFAB, and the Normative Framework, similarly fall short of offering detailed operational guidance for real-world AI deployment. Recognizing these gaps, we developed a novel Framework for Responsible AI Deployment in healthcare settings, incorporating structured, actionable steps to identify, mitigate, and monitor biases throughout the AI lifecycle. Methodology: Our framework (https://github.com/pmcdi/responsible-ai) was developed through a multidisciplinary collaborative approach whereby stakeholders with expertise in biostatistics, machine learning, ethics, clinical care, institutional governance, diversity and inclusion, and patient advocates, synthesized insights from existing frameworks and engaged in iterative and structured feedback sessions to ensure practical applicability and robustness. Results: This framework is organized into four distinct stages: (1) Problem Identification and Study Design, emphasizing equity-focused clinical question formulation and ethical compliance; (2) Model Training and Development, addressing biases in retrospective data and ensuring transparent performance evaluations; (3) Silent Deployment and Clinical Evaluation, prospectively validating model fairness and clinical applicability without direct patient impact; and (4) Clinical Deployment and Lifecycle Monitoring, providing continuous oversight of AI systems integrated into clinical workflows, emphasizing patient and clinician education, compliance monitoring, and adaptive maintenance. The framework is accompanied by a supplemental appendix which contextualizes each stage with concrete detail such as recommended methods, pain points to consider, and academic references for exploration. Conclusions: Our framework addresses critical shortcomings in current practices to facilitate ethical and equitable AI deployment in healthcare. We are actively working with researchers at the Princess Margaret Cancer Centre to evaluate its utility across a breadth of clinical AI solutions at all stages of development. This framework can help institutions meet their ethical obligations; ensure AI-driven innovations align with foundational healthcare principles of fairness, safety, and quality; safeguard against harm; and ultimately improve trust in AI-enhanced clinical care. Citation Format: Benjamin Grant, Mattea Welch, Christopher Deutschman, Clare McElcheran, Adam Badzynski, Jennifer A.H. Bell, Andrew Hope, Robert C. Grant, Tran Truong, Kelly Lane, Patti Leake, Divya Sharma, Ian Stedman, Mike Lovas, Jeremy Petch, Muammar Kabir, Alejandro Berlin, James A. Anderson, Benjamin Haibe-Kains. A practical framework for operationalizing responsible and equitable AI in healthcare: Tackling bias, inequity, and implementation challenges [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr PR-04.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,234 | 0,224 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,019 |
| Communication savante | 0,014 | 0,013 |
| Science ouverte | 0,009 | 0,016 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».