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

Year 2024, Volume: 8 Issue: 1, 171 - 180, 31.01.2024

Abstract

References

  • 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.

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

Year 2024, Volume: 8 Issue: 1, 171 - 180, 31.01.2024

Abstract

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.

References

  • 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.
There are 30 citations in total.

Details

Primary Language English
Subjects Health Services and Systems (Other)
Journal Section Research Article
Authors

Cemile Bozdemir Özel 0000-0003-4918-9249

Hazal Yakut Ozdemir 0000-0001-7375-2519

Early Pub Date January 31, 2024
Publication Date January 31, 2024
Submission Date August 7, 2023
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

APA Bozdemir Özel, C., & Yakut Ozdemir, H. (2024). Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence. Journal of Basic and Clinical Health Sciences, 8(1), 171-180.
AMA Bozdemir Özel C, Yakut Ozdemir H. Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence. JBACHS. January 2024;8(1):171-180.
Chicago Bozdemir Özel, Cemile, and Hazal Yakut Ozdemir. “Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence”. Journal of Basic and Clinical Health Sciences 8, no. 1 (January 2024): 171-80.
EndNote Bozdemir Özel C, Yakut Ozdemir H (January 1, 2024) Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence. Journal of Basic and Clinical Health Sciences 8 1 171–180.
IEEE C. Bozdemir Özel and H. Yakut Ozdemir, “Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence”, JBACHS, vol. 8, no. 1, pp. 171–180, 2024.
ISNAD Bozdemir Özel, Cemile - Yakut Ozdemir, Hazal. “Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence”. Journal of Basic and Clinical Health Sciences 8/1 (January 2024), 171-180.
JAMA Bozdemir Özel C, Yakut Ozdemir H. Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence. JBACHS. 2024;8:171–180.
MLA Bozdemir Özel, Cemile and Hazal Yakut Ozdemir. “Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence”. Journal of Basic and Clinical Health Sciences, vol. 8, no. 1, 2024, pp. 171-80.
Vancouver Bozdemir Özel C, Yakut Ozdemir H. Levels of Awareness, Readiness, and Anxiety of Physiotherapists Related to Artificial Intelligence. JBACHS. 2024;8(1):171-80.