Knowledge is not all you need to generate trust in AI use in healthcare
Notice bibliographique
Résumé
Abstract Background Canada has invested significantly in artificial intelligence (AI) research and development over the last several years. Canadians’ knowledge of and attitudes towards AI in healthcare are understudied. Objectives To explore the relationships between age, gender, education level, and income on Canadians’ knowledge of AI, their comfort with its use in healthcare, and their comfort with using personal health data in AI research. Methods Ordinal logistics regression and multivariate polynomial regression were applied to data from the 2021 Canadian Digital Health Survey using RStudio and SigmaZone’s Design of Experiments Pro. Results Female and older Canadians self-report less knowledge about AI than males and other genders and younger Canadians. Female Canadians and healthcare professionals are less comfortable with use of AI in healthcare compared to males and people with other levels of education. Discomfort appears to stem from concerns about data security and the current maturity level of the technology. Conclusion Knowledge of AI and the use of AI in healthcare are inversely correlated with age and directly correlated with education and income levels. Overall, female respondents self-reported less knowledge and comfort with AI in healthcare and research than other genders. Privacy concerns should continue to be addressed as a major consideration when implementing AI tools. Canadians, especially older females, not only need more education about AI in healthcare, but also need more reassurance about the safe and responsible use of their data and how bias and other issues with AI are being addressed. Author Summary Artificial intelligence (AI) and its application has garnered significant public interest and excitement within healthcare in recent years. However, its successful integration and use in healthcare will depend on patient and user adoption. As a result, AI tools may be limited in healthcare when user concerns are not carefully addressed and if patients are not educated about how these technologies work. While there have been studies on the attitudes of clinicians and healthcare professionals toward AI, little is known about the general public’s perception of AI within the healthcare setting. Our study addresses this gap in the literature by analyzing data from the 2021 Canadian Digital Health Survey to understand the relationships between Canadians’ attitudes towards AI and various socioeconomic and demographic factors. Our results found that older Canadians, Canadians with less formal education and women need to be better informed about the safe and responsible use of AI and be reassured about good data security practices before it can be broadly accepted by them. In addition, the element of trust may be a factor that is contributing to the higher levels of discomfort with AI observed in middle-aged Canadians. The findings from this study will help stakeholders better implement and broaden the accessibility of AI technologies.
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 | 0,020 | 0,117 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,011 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
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 ».