Listening to Patients’ Voices on the Use of AI in Health Care: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Artificial intelligence (AI) holds great promise in transforming health care delivery. However, successful implementation of AI projects in health care depends on patients' acceptance and trust. There is limited empirical research examining public perceptions, particularly the use of personal health data in AI applications in health care. Objective: This study examined public knowledge and comfort levels with AI use in health care, including use of personal health data with and without consent, and assessed how sociodemographic factors, digital literacy, and health conditions influence these perceptions. Methods: We analyzed data from 6904 Canadian adults who participated in the 2023 Canadian Digital Health Survey. AI-related knowledge and comfort levels were measured using ordinal scales. Sociodemographic characteristics, digital health literacy, and self-reported chronic health conditions were included as predictors. Ordinal logistic regression models were used to assess associations between these factors and AI-related attitudes. Results: A majority of 2919 (42.3%) reported moderate knowledge of AI; only 7.8% (542) described themselves as very knowledgeable. Overall, 44.6% were comfortable with AI use in health care, increasing to 64.7% when personal health data were used with consent but decreasing when used without consent (52.6% uncomfortable). Respondents were most comfortable with AI use for epidemic tracking and workflow management and less for clinical tasks. Fully weighted ordinal logistic regression models indicated that men (odds ratio [OR]=1.57, P<.001), noncitizens (OR=1.71, P<.001), higher-income respondents (OR=1.29, P<.001), those with graduate education (OR=1.43, P<.001), higher digital health literacy (OR=1.08, P<.001), and more chronic conditions (OR=1.08, P<.001) exhibited greater odds of reporting higher AI knowledge. For comfort with AI use in health care, those aged 65+ years (OR=1.47, P<.001), men (OR=1.50, P<.001), noncitizens (OR=1.49, P<.001), higher-income respondents (OR=1.21, P<.001), and those with higher digital health literacy (OR=1.06, P<.001) or more chronic conditions (OR=1.04, P=.04) exhibited greater comfort. Lower-income (OR=0.87, P=.03) and White respondents (OR=0.77, P<.001) reported lower comfort levels. For comfort with using personal health data in AI with consent, adults aged 35-54 years (OR=0.72, P<.001) were less comfortable than those aged 16-24 years. Men (OR=1.39, P<.001), higher-income respondents (OR=1.16, P=.01), and those with higher digital health literacy (OR=1.05, P<.001) or more chronic conditions (OR=1.07, P<.001) showed greater comfort; White (OR=0.78, P<.001), other racial groups (OR=0.77, P=.03), and lower-income respondents were less comfortable (OR=0.83, P=.01). For comfort with using personal health data in AI without consent, men (OR=1.56, P<.001), noncitizens (OR=1.28, P=.03), and those with higher digital health literacy (OR=1.04, P<.001) exhibited greater comfort. Lower-income respondents (OR=0.86, P=.02), adults aged 35-54 years (OR=0.73, P<.001) or 55-64 years (OR=0.77, P=.01), and White (OR=0.69, P<.001) and Black or African-origin (OR=0.71, P=.02) respondents reported lower comfort levels. Conclusions: The findings point to enhancing transparent policies, digital literacy, and ethical data governance as key to increasing public trust in AI-driven health care.
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».