Perceptions Towards Artificial Intelligence Among Allied Health Sciences Students in Islamabad and Rawalpindi
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Background: Artificial intelligence is being incorporated into diagnostic, therapeutic, and rehabilitative workflows, yet evidence on how allied health trainees in low- and middle-income settings perceive these technologies remains limited. Objective: To assess perceptions of artificial intelligence in healthcare among undergraduate Allied Health Sciences students and to examine whether perception scores differed by gender, academic year, and academic discipline. Methods: A cross-sectional observational study was conducted over six months among 145 undergraduate Allied Health Sciences students aged 19 to 26 years recruited by convenience sampling from four institutions in Islamabad and Rawalpindi, Pakistan. Perceptions were measured using the 10-item Shinners Artificial Intelligence Perception scale (range 10–50; Cronbach's alpha 0.766). Analyses used independent-samples t-test, one-way analysis of variance, and chi-square test in IBM SPSS Statistics version 26.0, with significance at p < 0.05. Results: Moderate perception predominated (77.2%, n = 112), with 20.7% (n = 30) high and 2.1% (n = 3) low. Male students scored higher than female students (35.87 ± 6.21 versus 31.98 ± 5.18; mean difference 3.89, 95% CI 2.01–5.77; p < 0.001; Cohen's d = 0.69). Scores increased with academic year (F = 2.531; p = 0.043), rising from 32.12 to 35.67. Discipline was not associated with perception (p > 0.05). Conclusion: Perceptions were cautiously favourable but constrained by limited formal instruction, with differences by gender and academic seniority supporting structured integration of artificial intelligence education into Allied Health curricula
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1. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.
2. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–8.
3. Jindal A, Bansal M. Knowledge and education about artificial intelligence among medical students from teaching institutions of India: a brief survey. MedEdPublish. 2020;9:200.
4. Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: a review. Sensors. 2021;21(6):2146.
5. Aldhahi MI, Alorainy AI, Abuzaid MM, Gareeballah A, Alsubaie NF, Alshamary AS, et al. Adoption of artificial intelligence in rehabilitation: perceptions, knowledge, and challenges among healthcare providers. Healthcare (Basel). 2025;13(4).
6. Ng CT, Roslan SNA, Chng YH, Choong DAW, Chong AJL, Tay YX, et al. Singapore radiographers' perceptions and expectations of artificial intelligence — a qualitative study. J Med Imaging Radiat Sci. 2022;53(4):554–63.
7. Bozdemir Özel C, Yakut Ozdemir H. Levels of awareness, readiness, and anxiety of physiotherapists related to artificial intelligence. J Basic Clin Health Sci. 2024;8(1):171–80.
8. Rony MKK, Parvin MR, Wahiduzzaman M, Debnath M, Bala SD, Kayesh I. "I wonder if my years of training and expertise will be devalued by machines": concerns about the replacement of medical professionals by artificial intelligence. SAGE Open Nurs. 2024;10:23779608241245220.
9. Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11(1):5193.
10. Shishehgar S, Murray-Parahi P, Alsharaydeh E, Mills S, Liu X. Artificial intelligence in health education and practice: a systematic review of health students' and academics' knowledge, perceptions and experiences. Int Nurs Rev. 2025;72(2):e70045.
11. Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: a systematic review. Health Sci Rep. 2023;6(3):e1138.
12. Pupic N, Ghaffari-Zadeh A, Hu R, Singla R, Darras K, Karwowska A, et al. An evidence-based approach to artificial intelligence education for medical students: a systematic review. PLOS Digit Health. 2023;2(11):e0000255.
13. Weidener L, Fischer M. Artificial intelligence in medicine: cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ. 2024;10(1):e51247.
14. Chomutare T, Tejedor M, Svenning TO, Marco-Ruiz L, Tayefi M, Lind K, et al. Artificial intelligence implementation in healthcare: a theory-based scoping review of barriers and facilitators. Int J Environ Res Public Health. 2022;19(23):16359.
15. Kaihlanen AM, Laukka E, Nadav J, Närvänen J, Saukkonen P, Koivisto J, et al. The effects of digitalisation on health and social care work: a qualitative descriptive study of the perceptions of professionals and managers. BMC Health Serv Res. 2023;23(1):714.
16. 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 Internet Res. 2022;24(10):e39565.
17. Hoffman J, Hattingh L, Shinners L, Angus RL, Richards B, Hughes I, et al. Allied health professionals' perceptions of artificial intelligence in the clinical setting: cross-sectional survey. JMIR Form Res. 2024;8:e57204.
18. Habib MM, Hoodbhoy Z, Siddiqui MAR. Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS Digit Health. 2024;3(5):e0000443.
19. Maqbool T, Ishaq H, Shakeel S, Zaib un Nisa A, Rehman H, Kashif S, et al. Future pharmacy practitioners' insights towards integration of artificial intelligence in healthcare education: preliminary findings from Karachi, Pakistan. PLoS One. 2025;20(2):e0314045.
20. Fatima S, Mehtab M, Syed M, Hussain F, Ahmed S, Riaz N, et al. Assessing awareness, perception and application of artificial intelligence among healthcare professionals and medical students in Pakistan: a cross-sectional online study. J Health Wellness Community Res. 2025:e234.
21. Cresswell K, Sheikh A. Organizational issues in the implementation and adoption of health information technology innovations: an interpretative review. Int J Med Inform. 2013;82(5):e73–86.
22. Holzner D, Apfelbacher T, Rödle W, Schüttler C, Prokosch HU, Mikolajczyk R, et al. Attitudes and acceptance towards artificial intelligence in medical care. In: Challenges of trustable AI and added-value on health. Amsterdam: IOS Press; 2022. p. 68–72.
23. Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol. 2024;97(1156):763–9.
24. Tran C, Hryciw BN, Moore SW, Chaput A, Seely AJE. Perceptions and use of generative artificial intelligence in medical students: a multicenter survey. J Med Educ Curric Dev. 2025;12:23821205251391969.