Knowledge And Attitude of Artificial Intelligence Among Medical And Dental Students of a Public Sector University of Karachi: A Cross-Sectional Study
Notice bibliographique
Résumé
Background: Artificial Intelligence (AI) is transforming healthcare, revolutionizing treatment, diagnosis and patient care. It has the potential to not only predict clinical events but also provide prognosis and aid drug discovery. As AI advances, it is crucial to train medical experts for an AI-enhanced healthcare system. This can be achieved by educating future medical and dental students. The first step in doing so is to assess the baseline knowledge at which they currently stand as well as exploring their insights on the famous stigma of AI replacing their future career choices and the current status of AI use in their country. Additionally, exploring ways through which students prefer AI to be incorporated; either curricular or extra-curricular. Methods: A questionnaire-based survey was conducted from August-November 2023, following IRB approval (Reference No: JSMU/IRB/2023/752) from the Institutional Review Board of JSMU, which was granted on June 24, 2023. The questionnaire, adapted from a similar Canadian study, was distributed via social media to medical and dental students over 18 years old at JSMU. A convenience sampling technique collected 324 completed and consented responses via forms. Statistical analysis was performed with SPSS version 20, using Chi-square test to compare factor variables (gender and year of study) with outcome variable (knowledge about AI). Results: Most students (74.38%) obtained information about AI from social media.Majority (79.9%) reported being somewhat familiar with AI, but only 22.53% chose accurate definition. Students' program and year of study impacted their knowledge of AI (P < 0.05), while gender did not (P > 0.05). Over half (56.17%) of the population believed students need to learn AI basics, and 53.09% agreed AI will revolutionize healthcare. Top benefits selected were early accurate diagnosis, improved accessibility, and automation of routine tasks. Most students (48.15%) favored integrating AI into their curriculum. A majority of MBBS (47.7%) and BDS (41.33%) students believed AI might replace their careers in the future. Notably, 60.19% of students believed AI will improve patient care, while 45.68% thought it would raise healthcare costs. Conclusion: Students acknowledge AI’s potential, but lack the necessary knowledge. Recognition of this gap highlights the need for robust educational planning either curricular or extracurricular with the learning objectives as highlighted by the preferences chosen by the students. This study helped address students’ notion about AI replacing careers and identifies the need to educate students about the potential of AI and how it can efficiently supplement but not replace healthcare. We believe that these findings offer valuable guidance for education and health policymakers to plan curricula and integrate modulations for future advancements. The limitations include the study being limited to a single university and two departments, and the use of convenience sampling, which may have affected the generalizability of the findings.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».