Author:
Al-Qerem Walid,Eberhardt Judith,Jarab Anan,Al Bawab Abdel Qader,Hammad Alaa,Alasmari Fawaz,Alazab Badi’ah,Husein Daoud Abu,Alazab Jumana,Al-Beool Saed
Abstract
Abstract
Introduction
The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions’ students in Jordan concerning AI, providing insights into their preparedness and perceptions.
Methods
An online questionnaire was distributed to 483 Jordanian health professions’ students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores.
Results
Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps.
Conclusions
This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI’s potential for improved patient care and training.
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
Reference25 articles.
1. Wang F, Preininger A. AI in Health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28:16–26.
2. Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: a national survey study. Front Artif Intell. 2022;5:1015418.
3. Londhe VY, Bhasin B. Artificial intelligence and its potential in oncology. Drug Discov Today. 2019;24:228–32.
4. Alagappan M, Brown JRG, Mori Y, Berzin TM. Artificial intelligence in gastrointestinal endoscopy: the future is almost here. World J Gastrointest Endosc. 2018;10:239–49.
5. Khumrin P, Ryan A, Judd T, Verspoor K. Diagnostic machine learning models for Acute Abdominal Pain: towards an e-Learning Tool for Medical Students. Stud Health Technol Inform. 2017;245:447–51.