The use of artificial intelligence in rehabilitation of adults with chronic conditions in Canada: A scoping review
Notice bibliographique
Résumé
This is a protocol for a scoping review. The aim of this scoping review is to understand the use of artificial intelligence (AI) in rehabilitation of adults with chronic conditions in a Canadian context. Artificial intelligence (AI) is a rapidly evolving field and AI technology is becoming increasingly integrated into healthcare. However, the use of AI in rehabilitation of individuals with chronic conditions in Canada has not been explored in the literature. This exploration is crucial to understand if AI is currently being used for the rehabilitation of individuals with chronic conditions, how it is being used, and to identify potential areas to integrate AI into treatment. AI is defined as computer systems or machines that perform tasks that normally require human intelligence1. This may include the creation of personalized rehabilitation protocols, predictive models that analyze patient data, or the control of robotic devices that support movement training3. The implementation of AI in healthcare in Canada is complex due to the various tiers of regulation from federal to private law. There are also vague surveillance and reporting requirements that may result in safety risks4. Other barriers include ethical challenges such as informed consent to use, safely, and data privacy5. The integration of AI technologies into current workflows can also pose a challenge, including the data quantity, the education of training of staff, and funding limitations. However, perceived benefit, usefulness, accuracy, and ease of use have been discussed as facilitators for the implementation of AI in healthcare6. Research Objective: Understand the use of artificial intelligence (AI) in rehabilitation of adults with chronic conditions in a Canadian context. Additionally, this review will explore where AI is currently being used, how AI is used, the characteristics and parameters of physical rehabilitation interventions using AI, identify potential gaps in use where AI could be integrated into treatment in the future, and the barriers and facilitators of implementing these interventions Research Question: What literature exists describing the use of artificial intelligence (AI) in the rehabilitation of adults with chronic conditions in Canada? How is AI used in rehabilitation for adults with chronic conditions in Canada? What are the characteristics and parameters of rehabilitation interventions using AI for adults with chronic conditions in Canada? What are the barriers and facilitators to rehabilitation interventions using AI for adults with chronic conditions in Canada? Contributions of authors: Conceptualization (JST, HE, EB, HC, JC, AC, SR, PS); Writing original draft (EB, HC, JC, AC, SR, PS); Writing – reviewing and editing (JST, HE, EB, HC, JC, AC, SR, PS); Supervision (JST, HE); Funding Acquisition (JST, HE) Author Affiliations: School of Rehabilitation Sciences, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada (JST, HE, );
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Comment cette classification a été obtenuedéplier
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 | 0,007 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,019 |
| Études des sciences et des technologies | 0,000 | 0,006 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,006 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».