Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence

Yıl 2024, Cilt: 8 Sayı: 1, 171 - 180, 31.01.2024

Cemile Bozdemir Özel Hazal Yakut Ozdemir

Öz

Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence

Öz

Background and Purpose: This study aimed to examine the level of awareness of physiotherapists about artificial intelligence (AI), readiness, anxiety level related to AI and barriers to AI use.
Method: 413 physiotherapists participated in an online and cross-sectional study. A custom-designed survey that focussed on the levels of knowledge, readiness, and anxiety of physiotherapists related to AI and factors limiting AI use.
Results: 61% of physiotherapists had knowledge of AI in physiotherapy and rehabilitation. Mobile-based applications were reported as the most preferred approach among AI-based applications, while the cost of AI-based technological therapy applications was stated as the factor most limiting use of AI-based technological therapy applications in rehabilitation. Total score of Medical Artificial Intelligence Readiness Scale was calculated as 74.19±14.25, and total score of the Artificial Intelligence Anxiety Scale was 50.72 ±22.76. The level of readiness was lower among those with a bachelor's degree level of education compared to those with postgraduate degrees (p<0.05).
Conclusion: Physiotherapists have low levels of AI-related anxiety and a high degree of readiness. Physiotherapists were seen to have a positive attitude and willingness to use AI-based applications in practice. Nevertheless, the level of readiness could be increased by including AI-based applications in the undergraduate curriculum.

Anahtar Kelimeler

Physical Therapist, Artificial intelligence, Perceptions, Readiness

Kaynakça

• Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc.J. 2019;6(2):94-98.
• Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS med. 2018;15(11):1-17.
• Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging Sci. Dent 2019;49(1):1-7.
• Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
• Rowe M, Nicholls DA, Shaw J. How to replace a physiotherapist: artificial intelligence and the redistribution of expertise. Physiother. Theory Pract. 2021:1-9.
• Tack C. Artificial intelligence and machine learning| applications in musculoskeletal physiotherapy. Musculoskelet. Sci. Pract. 2019;39:164-9.
• Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front. Artif. Intell. 2020;3:578983.
• Tajaldeen A, Alghamdi S. Evaluation of radiologist’s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study. Acta Radiol Open. 2020;9(7):1-6.
• Baser A, Altuntas S, Kolcu G, Ozceylans G. Artificial Intelligence Anxiety of Family Physicians in Turkey. Prog. Nutr. 2021;23(2):1-7.
• Abuzaid MM, Elshami W, Hegazy F, Aboelnasr EA, Tekin HO. The Impact of Artificial Intelligence (AI) in Physiotherapy Practice: A Study of Physiotherapist Willingness and Readiness. J. Hunan Univ. Nat. Sci. 2022;49(3):196-201.
• Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists’ Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J.Med. Inter. Res. 2022;24(10):1-17.
• Lemay DJ, Basnet RB, Doleck T. Fearing the robot apocalypse: Correlates of AI anxiety. Int. j. Learn. Anal. Artif. Intell. Educ.2020; 2(2):24-33.
• Eysenbach G. Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J.Med.Internet Res. 2004.;6(3)1-6.
• Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC Med. Ed. 2021;21:1-9.
• Wang Y-Y, Wang Y-S. Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interact.Learn. Environ. 2022;30(4):619-34.
• Terzi R. An Adaptation of Artificial Intelligence Anxiety Scale into Turkish: Reliability and Validity Study. Inter. Online J. Ed and Teach. 2020;7(4):1501-15.
• Pinto dos Santos D, Giese D, Brodehl S, Chon S-H, Staab W, Kleinert R, et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur. Radiol. 2019;29:1640-6.
• Srivastava TK, Waghmare L. Implications of artificial intelligence (AI) on dynamics of medical education and care: a perspective. J Clin Diagn Res. 2020;14(3):1-2.
• Noblin A, Shettian M, Cortelyou-Ward K, Schack Dugre J. Exploring physical therapists’ perceptions of mobile application usage utilizing the FITT framework. Informa Health Soc. Care. 2017;42(2):180-93.
• Forsberg A, Nilsagård Y, Boström K. Perceptions of using videogames in rehabilitation: a dual perspective of people with multiple sclerosis and physiotherapists. Disabil. rehabil. 2015;37(4):338-44.
• Park J-H, Kim Y, Lee K-J, Yoon Y-S, Kang SH, Kim H, et al. Artificial neural network learns clinical assessment of spasticity in modified Ashworth scale. Arch. Phys. Med. Rehab. 2019;100(10):1907-15.
• Alsobhi M, Sachdev HS, Chevidikunnan MF, Basuodan R, KU DK, Khan F. Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach. Int. J. of Environ Res.Public Health. 2022;19(23):1-21.
• Oh S, Kim JH, Choi S-W, Lee HJ, Hong J, Kwon SH. Physician confidence in artificial intelligence: an online mobile survey. J. Med.Internet Res. 2019;21(3):1-13.
• Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, et al. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front. Med. 2022;9:1-17.
• European Society of Radiology (ESR). Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging. 2019 Oct 31;10(1):1-11.
• Laï M-C, Brian M, Mamzer M-F. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J. Transl. Med. 2020;18(1):1-13.
• Parasuraman A. Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2000;2(4):307-20.
• Lamanna C, Byrne L. Should artificial intelligence augment medical decision making? The case for an autonomy algorithm. AMA J Ethics. 2018;20(9):902-10.
• Martinez-Martin N, Insel TR, Dagum P, Greely HT, Cho MK. Data mining for health: staking out the ethical territory of digital phenotyping. NPJ Digit. Med. 2018;1(1):1-5.
• Sindermann C, Yang H, Elhai JD, Yang S, Quan L, Li M, Montag C. Acceptance and fear of Artificial Intelligence: associations with personality in a German and a Chinese sample. Discover Psychology. 2022;2(1):1-8.