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Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study

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Publicado en:BMC Medical Education vol. 24 (2024), p. 1
Autor principal: Nata Pratama Hardjo Lugito
Otros Autores: Cucunawangsih, Cucunawangsih, Suryadinata, Neneng, Kurniawan, Andree, Rhendy Wijayanto, Sungono, Veli, Sabran, Mohammad Zuhriansyah, Albert, Nikolaus, Claresta, Janice Budianto, Rubismo, Kenza Yogasvara, Nyoman Bagus Satcitta Ananda Purushotama, Zebua, Alfred
Publicado:
Springer Nature B.V.
Materias:
Malaysia
Indonesia
Accuracy
Curricula
Questionnaires
Likert scale
Medical students
Ethics
Patients
Electronic health records
Medicine
Perceptions
Artificial intelligence
Knowledge
Data collection
Attitudes
Education
Medical education
Cross-sectional studies
Paying for College
Basic Skills
Positive Attitudes
Physicians
Negative Attitudes
College Faculty
Likert Scales
Elective Courses
Medical Evaluation
Computer Oriented Programs
Outcome Based Education
Knowledge Level
Data Analysis
Allied Health Occupations Education
Language Processing
Ability Identification
Educational Background
Algorithms
Acceso en línea:Citation/Abstract
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Resumen:IntroductionArtificial intelligence (AI) enables machines to perform many complicated human skills which require various levels of human intelligence. In the field of medicine, AI helps physicians in making diagnoses and treatments for patients with more efficiency, accuracy, and precision. In order to prepare medical students who are the future healthcare workforce, it is important to enhance their readiness, knowledge and perception toward AI. This study aims to assess Pelita Harapan University (PHU) medical students’ readiness, knowledge, and perception toward AI.MethodsA quantitative cross-sectional study was conducted to assess respondents’ readiness, knowledge and perception toward AI. An online questionnaire was distributed via Google Forms to all batch of medical students. Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) questionnaire was used to evaluate AI readiness, while an adapted questionnaires was used to evaluate knowledge and perception toward AI. Data were then analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23.0.ResultsA total of 650 respondents were included in this study. Most respondents were in pre-clinical phase (88%) while the remaining were in clinical phase (12%). Overall, the total mean score for AI readiness was 73.34 of 100. Respondents had a mean score 24.52 ± 5.26 of 40, 27.78 ± 4.65 of 40, 10.57 ± 2.07 of 15, and 10.47 ± 2.00 of 15 in the cognitive, ability, vision, and ethics domain respectively. Generally, respondents had sufficient knowledge and positive perception toward AI. There were also significant correlation between readiness and knowledge with gender, having studied coding previously in high school, and having family or close friends working in AI field. Social media also had a good influence on enchancing readiness in the domain of ability and ethics, and perception towards AI.ConclusionMedical students of PHU mostly showed neutral to favorable response on readiness, knowledge, and perception towards AI. Incorporating AI into high school and medical curriculum is an important step to prepare medical students’ encounter and partnership with AI as the future workforce in medicine.
ISSN:1472-6920
DOI:10.1186/s12909-024-06058-x
Fuente:Healthcare Administration Database