Optometrists' Perspectives Regarding Artificial Intelligence Aids and Contributing Retinal Images to a Repository: Web-Based Interview Study
Notice bibliographique
Résumé
BACKGROUND: A repository of retinal images for research is being established in Scotland. It will permit researchers to validate, tune, and refine artificial intelligence (AI) decision-support algorithms to accelerate safe deployment in Scottish optometry and beyond. Research demonstrates the potential of AI systems in optometry and ophthalmology, though they are not yet widely adopted. OBJECTIVE: In this study, 18 optometrists were interviewed to (1) identify their expectations and concerns about the national image research repository and their use of AI decision support and (2) gather their suggestions for improving eye health care. The goal was to clarify attitudes among optometrists delivering primary eye care with respect to contributing their patients' images and to using AI assistance. These attitudes are less well studied in primary care contexts. Five ophthalmologists were interviewed to discover their interactions with optometrists. METHODS: Between March and August 2021, 23 semistructured interviews were conducted online lasting for 30-60 minutes. Transcribed and pseudonymized recordings were analyzed using thematic analysis. RESULTS: All optometrists supported contributing retinal images to form an extensive and long-running research repository. Our main findings are summarized as follows. Optometrists were willing to share images of their patients' eyes but expressed concern about technical difficulties, lack of standardization, and the effort involved. Those interviewed thought that sharing digital images would improve collaboration between optometrists and ophthalmologists, for example, during referral to secondary health care. Optometrists welcomed an expanded primary care role in diagnosis and management of diseases by exploiting new technologies and anticipated significant health benefits. Optometrists welcomed AI assistance but insisted that it should not reduce their role and responsibilities. CONCLUSIONS: Our investigation focusing on optometrists is novel because most similar studies on AI assistance were performed in hospital settings. Our findings are consistent with those of studies with professionals in ophthalmology and other medical disciplines: showing near universal willingness to use AI to improve health care, alongside concerns over training, costs, responsibilities, skill retention, data sharing, and disruptions to professional practices. Our study on optometrists' willingness to contribute images to a research repository introduces a new aspect; they hope that a digital image sharing infrastructure will facilitate service integration.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,000 |
| 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 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 ».