Artificial Intelligence Anxiety of Family Physicians in Turkey

Main Article Content

Aysel Baser https://orcid.org/0000-0001-8067-0677
Sibel Baktır Altuntaş https://orcid.org/0000-0003-1392-9014
Giray Kolcu https://orcid.org/0000-0001-8406-5941
Gökmen Özceylan https://orcid.org/0000-0002-2388-4158

Keywords

Family medicine, Family physicians, Artificial intelligence, Anxiety

Abstract

Study Objectives: Artificial intelligence (AI) is a computer system or a robot under the control of this system performing tasks similar to humans. In this study, it was aimed to evaluate the artificial intelligence anxiety of family physicians in Turkey. Method: Artificial Intelligence Anxiety Scale, which was developed by Wang, which Turkish validity and reliability study was conducted by Terzi, was preferred as the data collection tool. The universe was identified as Family Physician in Turkey (N=23,992). The sample size was calculated as 378 (n=378), regional populations were stratified according to gender and age groups. Results:  Within the scope of the study, Family Physicians / Family Medicine Specialists were included in the study (n.402). In evaluating the scale scores, the mean total score was 76.30±27.87, the learning sub-dimension average score was 24.83±11.46, the job change sub-dimension mean score was 21.51±8.68, and the sociotechnical subscale mean score was 18.95±6.44. The mean score of the artificial intelligence configuration sub-dimension was 6.44, and 10.99 ± 5.96. Conclusions: Since most physicians have not received training on AI applications and their anxiety levels are low, we believe that structured training programs and artificial intelligence applications in family medicine can be integrated into decision support systems and contribute to patient safety.

Abstract 689 | PDF Downloads 424

References

1. Kueper JK, Terry AL, Zwarenstein M, Lizotte DJ. Artificial Intelligence and Primary Care Research: A Scoping Review. Ann Fam Med. 2020;18(3):250–8.
2. Huang MZ, Gibson CJ, Terry AL. Measuring Electronic Health Record Use in Primary Care: A Scoping Review. Appl Clin Inform. 2018/01/10. 2018;9(1):15–33.
3. Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976–80.
4. Moore SF, Hamilton W, Llewellyn DJ. Harnessing the power of intelligent machines to enhance primary care. Br J Gen Pract. 2018;68(666):6 – 7.
5. Mary K P. Artificial intelligence in primary care. Med Econ. 2018;95(15):19–30.
6. Terzi R. an Adaptation of Artificial Intelligence Anxiety Scale Into Turkish: Reliability and Validity Study1. Int Online J Educ Teach. 2020;7:1501–15.
7. Wang YY, Wang YS. Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interact Learn Environ. 2019;0(0):1–16.
8. Randhawa GK, Jackson M. The role of artificial intelligence in learning and professional development for healthcare professionals. Healthc Manag Forum. 2020;33(1):19–24.
9. Guo J, Li B. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Heal equity. 2018;2(1):174–81.
10. Johnson DG, Verdicchio M. AI Anxiety. J Assoc Inf Sci Technol. 2017 Sep;68(9):2267–70.
11. Blease C, Kaptchuk TJ, Bernstein MH, Mandl KD, Halamka JD, DesRoches CM. Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views. J Med Internet Res. 2019;21(3):e12802.
12. Öcal EE, Atay E, Önsüz MF, Altın F, Çokyiğit FK, Kılınç S, et al. Tıp fakültesi öğrencilerinin tıpta yapay zeka ile ilgili düşünceleri. Türk Tıp Öğrencileri Araştırma Derg. 2020;2(1):9–16.
13. Lemay DJ, Basnet RB, Doleck T. Fearing the Robot Apocalypse: Correlates of AI Anxiety. Int J Learn Anal Artif Intell Educ. 2020;2(2):24.
14. Kim A, Cho M, Ahn J SY. Effects of Gender and Relationship Type on the Response to Artificial Intelligence. Cyberpsychology, Behav Soc Netw. 2019;22(4):249–53.