Current issue
Archive
Manuscripts accepted
About the Journal
Editorial office
Editorial board
Section Editors
Abstracting and indexing
Subscription
Contact
Ethical standards and procedures
Most read articles
Instructions for authors
Article Processing Charge (APC)
Regulations of paying article processing charge (APC)
RESEARCH LETTER
Assessment of the perception and use of artificial intelligence among physicians, medical students, and other healthcare professionals
1
Poznan University of Medical Sciences, Poznan, Poland
2
Department of Public Health, Poznan University of Medical Sciences, Poznan, Poland
Submission date: 2026-04-25
Final revision date: 2026-06-12
Acceptance date: 2026-07-04
Online publication date: 2026-08-21
Corresponding author
TOPICS
REFERENCES (15)
1.
Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, New York 2019.
2.
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med 2019; 25: 24-9.
3.
Oh S, Kim JH, Choi SW, et al. Physician confidence in artificial intelligence. J Korean Med Sci 2019; 34: 16.
4.
Pinto dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence. Eur Radiol 2019; 29: 1640-6.
5.
Choudhury A, Shamszare H. Human factors influencing trust in healthcare artificial intelligence: systematic literature review. IISE Trans Occup Ergon Hum Factors 2026; 14: 116-31.
6.
Mertz M, Toskovich K, Shields G, et al. Exploring trust factors in AI-healthcare integration: a rapid review. Front Artif Intell 2025; 8: 1658510.
7.
Zhu L, Wang X, Zhang Y. From assistance to reliance: development and validation of the large language model dependence scale. Int J Inf Manage 2025; 82: 102888.
8.
Henzler D, Schmidt S, Koçar A, et al. Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors. BMC Health Serv Res 2025; 25: 633.
9.
Yerköy Ateş N. Assessing healthcare professionals’ attitudes toward artificial intelligence in Turkey: a cross-sectional study. Public Health Nurs 2025; 42: e70080.
10.
Li X, Chen Y, Wang H. Gender differences in artificial intelligence: the role of artificial intelligence anxiety. Front Psychol 2025; 16: 1559457.
11.
Triola MM, Rodman A. Integrating generative artificial intelligence into medical education: curriculum, policy, and governance strategies. Acad Med 2025; 100: 413-8.
12.
Huang S, Ma J, Dai H, Luo L. A new in-hospital mortality prediction nomogram for intensive care unit patients with acute pancreatitis. Arch Med Sci 2024; 20: 61-70.
13.
Li GM, Tian JZ, Zhang HB, et al. A review on an interactive framework for digital therapeutics in proactive health management based on multimodal large language models. J Pract Med 2024; 41: 3977-83.
14.
Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 2020; 26: 1351-63.
15.
Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020; 2: e537-48.
| eISSN: | 1896-9151 |
| ISSN: | 1734-1922 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
