The need for artificial intelligence curriculum in medical education: A Canadian cross-sectional study of future oncology trainees.
Notice bibliographique
Résumé
e13583 Background: Emerging artificial intelligence (AI) technologies have diverse applications in medicine, with early evidence suggesting that AI tools can accurately perform key tasks in oncology. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study seeks to assess exposure to AI in medical education among trainees interested in pursuing a career in oncology, and the need for AI education in medicine. Methods: A 32 question survey for Canadian undergraduate medical students was distributed to students at all 17 Canadian medical schools. The survey assessed the currently available and perceived need for learning opportunities about AI and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale (LS) survey questions were scored from 1 (disagree) to 5 (agree), and analyzed using a two-sided one sample t-test vs a neutral value. Interview transcripts were analyzed using qualitative thematic analysis. Results are described as mean LS score ± standard deviation. Results: We received 486 responses from 17 of 17 medical schools. Of these respondents, 98 (20.2%) are willing to pursue a residency in an oncology-related field (pathology, radiology, general surgery, internal medicine, radiation oncology). Respondents agreed that AI applications in medicine would become common in the future (3.80±0.38) and would improve medicine (3.71±0.54). Further, respondents agreed that they would need to use and understand AI during their medical careers (3.76±0.572; 3.43±0.773), and that AI should be formally taught in medical education (3.43±0.756). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (1.76±0.785) and that AI-related learning opportunities were inadequate (2.12±0.802). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and logistical challenges in adding AI to curriculum. Conclusions: A lack of educational opportunities about AI in medicine were identified across Canadian medical students. Given that medical students overwhelmingly believe that AI is important to the future of medicine, and AI tools are currently progressing towards clinical implementation, AI should be considered for inclusion in formal medical curriculum.
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».