Perceived Challenges of Artificial Intelligence in \nHealthcare among Undergraduate Medical Students at a \nPublic Medical School in Sarawak, Malaysia
Notice bibliographique
Résumé
Introduction: As artificial intelligence (AI) becomes more integrated into healthcare, it also brings challenges. Understanding these perceived challenges among medical students is crucial for developing educational frameworks that prepare them to navigate these \nchallenges in clinical practice. However, the perceived challenges of AI in healthcare among medical students in Sarawak, Malaysia, remain underexplored. \nAim/Purpose/Objective: This study aimed to assess the perceived challenges of AI in healthcare among undergraduate medical students at a public medical school in Sarawak, \nMalaysia. \n \nMethod: A mixed-method cross-sectional survey was conducted from October 2023 to August 2024 among 185 undergraduate medical students from year one to year five at a public medical school in Sarawak. A convenience sampling method was employed. Data were collected using a validated questionnaire adapted from a previous Canadian study \nassessing medical students’ perceived challenges of AI in healthcare. Participants rated their agreement on a 5-point Likert scale. Quantitative responses were analysed descriptively while qualitative data from open-ended questions were thematically analysed \n \nResults: Most students expressed concerns about AI-related challenges: 73.0% (30.3% strongly agree, 42.7% agree) supported the statement that “AI in medicine will raise new \nethical challenges” while 79.5% (30.3% strongly agree, 49.2% agree) supported that “AI in medicine will raise new social challenges.” Additionally, 73.5% (27.0% strongly agree, \n46.5% agree) supported that “AI in medicine will raise new challenges around health equity.” In contrast, only 22.2% (6.5% strongly agree, 15.7% agree) supported that “The \nMalaysian healthcare system is currently well prepared to deal with challenges having to do with AI”. Qualitative thematic analysis highlighted key themes of “Ethical, privacy, and security issues” and “Trust and reliability concerns”. \n \nConclusion: Most of the medical students in this study expressed concerns about challenges of AI in healthcare, especially in ethical, privacy and security challenges. Comprehensive AI training, including ethical guidelines, is needed to equip future healthcare professionals to address these challenges effectively. \n \nKeywords: Artificial intelligence; challenges; healthcare; medical students; medical \neducation
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».