Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
BACKGROUND: Artificial Intelligence (AI) has significantly reshaped Primary Health Care (PHC), offering various possibilities and complexities across all functional dimensions. The objective is to review and synthesize available evidence on the opportunities, challenges, and requirements of AI implementation in PHC based on the Primary Care Evaluation Tool (PCET). METHODS: We conducted a systematic review, following the Cochrane Collaboration method, to identify the latest evidence regarding AI implementation in PHC. A comprehensive search across eight databases- PubMed, Web of Science, Scopus, Science Direct, Embase, CINAHL, IEEE, and Cochrane was conducted using MeSH terms alongside the SPIDER framework to pinpoint quantitative and qualitative literature published from 2000 to 2024. Two reviewers independently applied inclusion and exclusion criteria, guided by the SPIDER framework, to review full texts and extract data. We synthesized extracted data from the study characteristics, opportunities, challenges, and requirements, employing thematic-framework analysis, according to the PCET model. The quality of the studies was evaluated using the JBI critical appraisal tools. RESULTS: In this review, we included a total of 109 articles, most of which were conducted in North America (n = 49, 44%), followed by Europe (n = 36, 33%). The included studies employed a diverse range of study designs. Using the PCET model, we categorized AI-related opportunities, challenges, and requirements across four key dimensions. The greatest opportunities for AI integration in PHC were centered on enhancing comprehensive service delivery, particularly by improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction. However, the most challenges emerged within the stewardship and resource generation functions, with key concerns related to data security and privacy, technical performance issues, and limitations in data accessibility. Ensuring successful AI integration requires a robust stewardship function, strategic investments in resource generation, and a collaborative approach that fosters co-development, scientific advancements, and continuous evaluation. CONCLUSIONS: Successful AI integration in PHC requires a coordinated, multidimensional approach, with stewardship, resource generation, and financing playing key roles in enabling service delivery. Addressing existing knowledge gaps, examining interactions among these dimensions, and fostering a collaborative approach in developing AI solutions among stakeholders are essential steps toward achieving an equitable and efficient AI-driven PHC system. PROTOCOL: Registered in Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/HG2DV ).
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.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 découle