Artificial intelligence and social accountability in the Canadian health care landscape: A rapid literature review
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Notice bibliographique
Résumé
BACKGROUND: Situated within a larger project entitled "Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence," this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? METHODOLOGY: A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. PRINCIPAL FINDINGS: The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy. CONCLUSION: The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle