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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| Wydane w: | BMC Medical Education vol. 24 (2024), p. 1 |
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| Kolejni autorzy: | , , , , , , , , , , |
| Wydane: |
Springer Nature B.V.
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| Hasła przedmiotowe: | |
| Dostęp online: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3115122223 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1472-6920 | ||
| 024 | 7 | |a 10.1186/s12909-024-06058-x |2 doi | |
| 035 | |a 3115122223 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 58506 |2 nlm | ||
| 100 | 1 | |a Nata Pratama Hardjo Lugito | |
| 245 | 1 | |a Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study | |
| 260 | |b Springer Nature B.V. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Malaysia | |
| 651 | 4 | |a Indonesia | |
| 653 | |a Accuracy | ||
| 653 | |a Curricula | ||
| 653 | |a Questionnaires | ||
| 653 | |a Likert scale | ||
| 653 | |a Medical students | ||
| 653 | |a Ethics | ||
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Medicine | ||
| 653 | |a Perceptions | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Knowledge | ||
| 653 | |a Data collection | ||
| 653 | |a Attitudes | ||
| 653 | |a Education | ||
| 653 | |a Medical education | ||
| 653 | |a Cross-sectional studies | ||
| 653 | |a Paying for College | ||
| 653 | |a Basic Skills | ||
| 653 | |a Positive Attitudes | ||
| 653 | |a Physicians | ||
| 653 | |a Negative Attitudes | ||
| 653 | |a College Faculty | ||
| 653 | |a Likert Scales | ||
| 653 | |a Elective Courses | ||
| 653 | |a Medical Evaluation | ||
| 653 | |a Computer Oriented Programs | ||
| 653 | |a Outcome Based Education | ||
| 653 | |a Knowledge Level | ||
| 653 | |a Data Analysis | ||
| 653 | |a Allied Health Occupations Education | ||
| 653 | |a Language Processing | ||
| 653 | |a Ability Identification | ||
| 653 | |a Educational Background | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Cucunawangsih, Cucunawangsih | |
| 700 | 1 | |a Suryadinata, Neneng | |
| 700 | 1 | |a Kurniawan, Andree | |
| 700 | 1 | |a Rhendy Wijayanto | |
| 700 | 1 | |a Sungono, Veli | |
| 700 | 1 | |a Sabran, Mohammad Zuhriansyah | |
| 700 | 1 | |a Albert, Nikolaus | |
| 700 | 1 | |a Claresta, Janice Budianto | |
| 700 | 1 | |a Rubismo, Kenza Yogasvara | |
| 700 | 1 | |a Nyoman Bagus Satcitta Ananda Purushotama | |
| 700 | 1 | |a Zebua, Alfred | |
| 773 | 0 | |t BMC Medical Education |g vol. 24 (2024), p. 1 | |
| 786 | 0 | |d ProQuest |t Healthcare Administration Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3115122223/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3115122223/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3115122223/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |