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Assessing medical students' readiness for artificial intelligence after pre-clinical training
by
Masters, Ken
, AlZaabi, Adhari
in
Artificial intelligence
/ Educational aspects
/ Educational research
/ Medical students
/ Surveys
/ Training
2025
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Assessing medical students' readiness for artificial intelligence after pre-clinical training
by
Masters, Ken
, AlZaabi, Adhari
in
Artificial intelligence
/ Educational aspects
/ Educational research
/ Medical students
/ Surveys
/ Training
2025
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Assessing medical students' readiness for artificial intelligence after pre-clinical training
Journal Article
Assessing medical students' readiness for artificial intelligence after pre-clinical training
2025
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Overview
Artificial intelligence (AI) is becoming increasingly relevant in healthcare, necessitating healthcare professionals' proficiency in its use. Medical students and practitioners require fundamental understanding and skills development to manage data, oversee AI tools and make informed decisions based on AI applications. Integrating AI into medical education is essential to meet this demand. This cross-sectional study aimed to evaluate the level of undergraduate medical students' readiness for AI as they enter their clinical years at Sultan Qaboos University's College of Medicine and Health Sciences. The students' readiness was assessed after being exposed to various AI related topics in several courses in the preclinical phases of the medical curriculum. The Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS) questionnaire was used as the study instrument. A total of 84 out of 115 students completed the questionnaire (73.04% response rate). Of these, 45 (53.57%) were female while 39 (46.43%) were male. The cognition section, which evaluated the participants' cognitive preparedness in terms of knowledge of medical AI terminology, the logic behind AI applications, and data science, received the lowest score (Mean = 3.52). Conversely, the vision section of the questionnaire, which assessed the participants' capacity to comprehend the limitations and potential of medical AI, and anticipate opportunities and risks displayed the highest level of preparedness, had the highest score (Mean = 3.90). Notably, there were no statistically significant differences in AI competency scores by gender or academic year. This study's findings suggest while medical students demonstrate a moderate level of AI-readiness as they enter their clinical years, significant gaps remain, particularly in cognitive areas such as understanding AI terminology, logic, and data science. The majority of students use ChatGPT as their AI tool, with a notable difference in attitudes between tech-savvy and non-tech-savvy individuals. Further efforts are needed to improve students' competency in evaluating AI tools. Medical schools should consider integrating AI into their curricula to enhance students' preparedness for future medical practice. Assessing students' readiness for AI in healthcare is crucial for identifying knowledge and skills gaps and guiding future training efforts.
Publisher
BioMed Central Ltd
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