How artificial intelligence (AI) is used in aphasia rehabilitation: A scoping review
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Résumé
Background In recent years, artificial intelligence (AI) has become commonplace in our daily lives, making its way into many different settings, including health and rehabilitation. While there is an increase in research on AI use in different sectors, information is sparse regarding whether and how AI is used in aphasia rehabilitation.Aims The objective of this scoping review was to describe and understand how AI is currently being used in the rehabilitation of people with aphasia (PWA). Our secondary goal was to determine if and how AI is being integrated into Augmentative and alternative communication (AAC) devices or applications for aphasia rehabilitation.Methods Using the Arksey and O’Malley (2005) Levac and colleagues (2010) frameworks, we identified the research question: In what way is artificial intelligence (AI) used in language rehabilitation for people with aphasia (PWA)? We then selected search terms and searched six databases which resulted in the identification of 663 studies. Based on the inclusion criteria, 28 suitable studies were retained. We then charted, collated and summarised the data in order to generate four main themes: (1) AI used for the classification or diagnosis of aphasia/aphasic syndromes or for the classification or diagnosis of primary progressive aphasia (PPA)/PPA variants; (2) AI used for aphasia therapy; (3) AI used to create models of lexicalization; and (4) AI used to classify paraphasic errors.Results None of the articles retained incorporated AI in AAC devices or applications in the context of aphasia rehabilitation. The majority of articles (n=17) used AI to classify aphasic syndromes or to differentiate PWA from healthy controls or persons with dementia. Another subset of articles (n=7) used AI in the attempt to augment an aphasia therapy intervention. Finally, two articles used AI to create a model of lexicalization and another two used AI to classify different types of paraphasias in the utterances of PWA.Conclusion Regarding performance accuracy of the diagnosis tools, results show that, regardless the type of AI approach used, models were able to differentiate between aphasic syndromes with a relatively high level of accuracy. Although significant advancements in AI and more interaction between the fields of aphasia rehabilitation and AI are required before AI can be integrated in aphasia rehabilitation, it nevertheless has the potential to be a central component of novel AAC devices or applications and be incorporated into innovative methods for aphasia assessment and therapy. However, for a transition to the clinic, new technologies or interventions using AI will need to be assessed to determine their efficacy and acceptance by both speech-language pathologists and PWA.
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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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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