Patient Attitudes Toward Artificial Intelligence in Cancer Care: Scoping Review
Notice bibliographique
Résumé
Background: Artificial intelligence is reshaping cancer care, but little is known about how people with cancer perceive its integration into their care. Understanding these perspectives is essential to ensuring artificial intelligence adoption aligns with patient needs and preferences while supporting a patient-centered approach. Objective: The aim of this study is to synthesize existing literature on patient attitudes toward artificial intelligence in cancer care and identify knowledge gaps that can inform future research and clinical implementation. Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. MEDLINE, Embase, PsycINFO, and CINAHL were searched for peer-reviewed primary research studies published until February 1, 2025. The Population-Concept-Context framework guided study selection, focusing on adult patients with cancer and their attitudes toward artificial intelligence. Studies with quantitative or qualitative data were included. Two independent reviewers screened studies, with a third resolving disagreements. Data were synthesized into tabular and narrative summaries. Results: Our search yielded 1240 citations, of which 19 studies met the inclusion criteria, representing 2114 patients with cancer across 15 countries. Most studies used quantitative methods (9/19, 47%) such as questionnaires or surveys. The most studied cancers were melanoma (375/2114, 17.7%), prostate (n=323, 15.3%), breast (n=263, 12.4%), and colorectal cancer (n=251, 11.9%). Although patients with cancer generally supported artificial intelligence when used as a physician-guided tool (9/19, 47%), concerns about depersonalization, treatment bias, and data security highlighted challenges in implementation. Trust in artificial intelligence (10/19, 53%) was shaped by physician endorsement and patient familiarity, with greater trust when artificial intelligence was physician-guided. Geographic differences were observed, with greater artificial intelligence acceptance in Asia, while skepticism was more prevalent in North America and Europe. Additionally, patients with metastatic cancer (99/2114, 5%) were underrepresented, limiting insights into artificial intelligence perceptions in this population. Conclusions: This scoping review provides the first synthesis of patient attitudes toward artificial intelligence across all cancer types and highlights concerns unique to patients with cancer. Clinicians can use these findings to enhance patient acceptance of artificial intelligence by positioning it as a physician-guided tool and ensuring its integration aligns with patient values and expectations.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».