Applying a Device for Artificial Intelligence Decision Support in practice during screening for cervical cancer in Bangladesh and Uganda: a CFIR analysis (Preprint)
Notice bibliographique
Résumé
Abstract Background Screening is important for early detection of cervical cancer in low- and middle-income countries. Visual inspection with acetic acid (VIA) is usually the method of choice in these settings. However, interpretation of VIA results is subject to interobserver and intraobserver variability. AI decision support systems (AI-DSSs) could contribute to better decisions by health workers. Objective The aim of this study was to analyze the barriers and facilitators of introducing an AI-DSS device under field conditions in the context of VIA screening in rural Bangladesh and Uganda, with the goal of improving the operational systems of applying an AI-DSS device. Methods We operationalized the Consolidated Framework for Implementation Research for this specific study and defined the constructs for analysis. The study was performed in rural Uganda and Bangladesh. We extracted relevant information from routine data, patient surveys, and facility surveys. We also interviewed health workers who used the AI-DSS devices. A panel of experts performed an analysis of the quality of pictures and the performance of health workers. We monitored implementation through quarterly meetings and documented the process. This trial was registered under ClinicalTrials.gov identifier NCT05234112. Results The applied AI-DSS passed tests under laboratory conditions but performed less well under field conditions. The hardware design using an adapted mobile phone was adequate, and the user interface was user-friendly and intuitive. However, operating the device while performing VIA in clinics was challenging. Since the device was not registered as a medical device, it was only used for research purposes. In both countries, there was no official government policy concerning the use of AI in health care. All facilities had gynecological examination rooms, but some facilities did not have permanent electricity or internet connection. The normal procedure for VIA was followed, and AI-DSS did not interfere with routine screening procedures. The AI-DSS was well accepted by women when privacy was guaranteed, but there seemed to be more trust in the judgment of health workers. The pictures helped supervisors to give a second opinion from a distance and were used for training purposes. The technical support team was able to help remotely and improved the performance. Training health workers in taking good pictures was very important. A permanent monitoring process during implementation was established, which led to early detection and correction of shortfalls. Conclusions According to this study, the device has the potential for improving the quality of the assessment by health workers who perform VIA. However, competent staff trained in performing VIA will be indispensable for capturing high-quality pictures. The algorithm requires further fine-tuning. This study showed that the Consolidated Framework for Implementation Research is a proper tool for categorizing barriers and facilitators when introducing an AI-DSS device in practice.
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 | 0,029 | 0,105 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».