ارزیابی سواد هوش مصنوعی در دانشگاه علوم پزشکی مشهد

ضیایی, ثریا

doi:10.30473/mrs.2025.74569.1630

ارزیابی سواد هوش مصنوعی در دانشگاه علوم پزشکی مشهد

نوع مقاله : مقاله پژوهشی

نویسنده

ثریا ضیایی

دانشیار، گروه علم اطلاعات و دانش‌شناسی، دانشگاه پیام نور، تهران، ایران

10.30473/mrs.2025.74569.1630

چکیده

با توجه به پیشرفت‌های چشمگیر در حوزه هوش مصنوعی این فناوری به‌سرعت در حال تبدیل‌شدن به یکی از ابزارهای اصلی در بهبود کارایی، دقت در تشخیص و درمان و ارتقاء کیفیت خدمات سلامت است. از آنجایی که پزشکان و دستیاران پزشکی در فرآیندهای بالینی و درمانی نقش کلیدی دارند، برخورداری آن‌ها از سواد هوش مصنوعی برای استفاده مؤثر و ایمن از این فناوری‌ها ضروری است. بنابراین، این پژوهش با هدف بررسی میزان سواد هوش مصنوعی در میان دستیاران پزشکی دانشگاه علوم پزشکی مشهد انجام شد. این مطالعه به‌صورت توصیفی–همبستگی و با رویکرد کاربردی طراحی گردید. از روش نمونه‌گیری در دسترس استفاده شد و ۲۶۰ نفر از دستیاران رشته‌های مختلف پزشکی در این پژوهش مشارکت داشتند. داده‌ها با استفاده از پرسشنامه محقق‌ساخته شامل پنج مؤلفه سواد هوش مصنوعی:درک الگوریتم‌ها، تحلیل داده‌ها، استفاده از نرم‌افزارهای هوش مصنوعی، آگاهی از چالش‌ها و محدودیت‌ها، و آموزش مداوم) جمع‌آوری شد. روایی پرسشنامه به‌صورت صوری و محتوایی تأیید شد و پایایی آن با ضریب آلفای کرونباخ 0/93 محاسبه گردید. نتایج نشان داد که از شرکت‌کنندگان، ۷/۵۷ درصد مرد و ۳/۴۲ درصد زن بودند. میانگین نمرات در مؤلفه‌های مختلف به‌ترتیب عبارت بود از: درک الگوریتم‌ها (4/05)، تحلیل داده‌ها (3/98)، استفاده از نرم‌افزارها (3/95)، آگاهی از چالش‌ها (3/66) و آموزش مداوم (3/85). همچنین، دستیارانی که دوره‌های آموزشی هوش مصنوعی را گذرانده بودند، سطح سواد بالاتری در این حوزه داشتند. در نهایت گنجاندن دوره‌های آموزشی هدفمند در زمینه هوش مصنوعی در برنامه‌های آموزشی می‌تواند به بهبود آمادگی حرفه‌ای و کاربرد مؤثرتر این فناوری در نظام سلامت منجر شود.

کلیدواژه‌ها

موضوعات

سواد دیجیتالی

عنوان مقاله [English]

Evaluation of Artificial Intelligence Literacy at Mashhad University of Medical Sciences

نویسنده [English]

soraya ziaei

. AssociateProfessor, Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran.

چکیده [English]

Artificial intelligence (AI) is becoming increasingly common in healthcare, with many experts pointing to its potential to improve diagnostics, streamline clinical tasks, and ultimately support better patient care. Physicians and residents are at the center of these changes, and their role in applying AI effectively is critical. However, simply having access to new tools isn’t enough. For clinicians today, understanding the basics of how AI functions - what it can do, and where its limits lie - is quickly becoming an essential skill rather than a bonus. With that in mind, this study looked at how familiar medical residents at Mashhad University of Medical Sciences are with AI-related concepts and tools. We surveyed 260 residents across a range of specialties, using a convenience sampling method. The questionnaire we developed focused on five key areas: knowledge of algorithms, ability to work with data, experience using AI in clinical settings, awareness of potential risks or limitations, and openness to continued learning in this area. To ensure the questionnaire was solid, we had it reviewed by experts, and internal reliability was confirmed with a Cronbach’s alpha of 0.93. Of those surveyed 57.7 were men and 42.3 were women. Average scores by category were: algorithm understanding (4.05), data skills (3.98), AI tool usage (3.95), awareness of limitations (3.66), and interest in ongoing learning (3.85). Notably, residents who had previously attended AI-related training generally performed better across the board. The findings suggest that while many residents have a decent baseline understanding of AI, there’s still a gap - especially when it comes to deeper knowledge or critical awareness of its limitations. As these technologies become more embedded in everyday clinical work, it makes sense to introduce more formal training during residency. Doing so could not only help future doctors feel more confident using AI, but also ensure these tools are applied in ways that genuinely benefit patients.

کلیدواژه‌ها [English]

Artificial Intelligence Literacy
Medical Education
Medical Residents

مراجع

Abdolahi Shahvali, E., Arizavi, Z., Hemmatipour, A., & Jahahgirimehr, A. (2024).. Investigating the level of knowledge, attitude, and performance of students regarding the applications of artificial intelligence in nursing Jundishapur Scientific Medical Journal (JSMJ), 23(2), 134–142. )In Persia) https://Doi. 10.32592/JSMJ.23.2.134org/10.32592/JSMJ.23.2.134

Ahmed, Z., Bhinder, K. K., Tariq, A., Tahir, M. J., Mehmood, Q., Tabassum, M. S., ... & Yousaf, Z. (2022). Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey. Annals of Medicine and Surgery, 76, 103493. DOI: 10.1016/j.amsu.2022.103493

Alelyani, M., Alamri, S., Alqahtani, M. S., Musa, A., Almater, H., Alqahtani, N., Alshahrani, F., & Alelyani, S. (2021). Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology. Healthcare, 9(7), 834. https://doi.org/10.3390/healthcare9070834

Chowdhury, R. H. (2024). Intelligent systems for healthcare diagnostics and treatment. World Journal of Advanced Research and Reviews, 23(1), 007-015.‏ https://doi.org/10.30574/wjarr.2024.23.1.2015

Fitzek, S., & Choi, K. E. A. (2024). Shaping future practices: German-speaking medical and dental students’ perceptions of artificial intelligence in healthcare. BMC Medical Education, 24(1), 844.‏ DOI: 10.1186/s12909-024-05826-z

Hamamoto, R. (2021). Application of artificial intelligence for medical research. Biomolecules, 11(1), 90. doi: 10.3390/biom11010090

Hatem, R., Simmons, B., & Thornton, J. E. (2023). A call to address AI “hallucinations” and how healthcare professionals can mitigate their risks. Cureus, 15(9), e44720. https://doi.org/10.7759/cureus.44720

Kimiafar, K., Sarbaz, M., Tabatabaei, S. M., Ghaddaripouri, K., Mousavi, A. S., Raei Mehneh, M., & Mousavi Baigi, S. F. (2023). Artificial intelligence literacy among healthcare professionals and students: A systematic review. Frontiers in Health Informatics, 12, 168. https://doi.org/10.30699/fhi.v12i0.524

Koohi-Moghadam, M., & Bae, K. T. (2023). Generative AI in medical imaging: Applications, challenges, and ethics. Journal of Medical Systems, 47(1), 94. https://doi.org/10.1007/s10916-023-01987-4

Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24(1), 401.‏. https://doi.org/10.1186/s12909-024-05400-7

Lérias, E., Guerra, C., & Ferreira, P. (2024). Literacy in artificial intelligence as a challenge for teaching in higher education: A case study at portalegre polytechnic university. Information, 15(4), 205.‏ https://doi.org/10.3390/info15040205

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727

Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Zhao, M., Chow, A. K., Ikemura, K., Kim, A., Pouli, D., Patel, A., Soliman, A., Chen, C., Ding, T., Wang, J. J., Gerber, G., Liang, I., Le, L. P., Parwani, A. V., Weishaupt, L. L., & Mahmood, F. (2024). A multimodal generative AI copilot for human pathology. Nature, 634(8033), 466–473. https://doi.org/10.1038/s41586-024-07618-3

Ma, Y., Song, Y., Balch, J. A., Ren, Y., Vellanki, D., Hu, Z., ... & Shickel, B. (2024). Promoting AI competencies for medical students: a scoping review on frameworks, programs, and tools. arXiv preprint arXiv: 2407.18939.‏ https:t//arxiv.org/abs/2407.18939

Mennella, C., Maniscalco, U., Pietro, G. D., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297

Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6, e1138. https://doi.org/10.1002/hsr2.1138

Nelson, G. S. (2019). Bias in artificial intelligence. North Carolina Medical Journal, 80(4), 220–222. https://doi.org/10.18043/ncm.80.4.220

Neittaanmäki, N. (2025, February). Swedish medical students’ attitudes toward artificial intelligence and effects on career plans: a survey. In Frontiers in Education (Vol. 10, p. 1517116). Frontiers Media SA.‏ https://doi.org/10.3389/feduc.2025.1517116

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041.‏ https://doi.org/10.1016/j.caeai.2021.100041

Pesapane, F., Cuocolo, R., & Sardanelli, F. (2024). The Picasso’s skepticism on computer science and the dawn of generative AI: Questions after the answers to keep “machines-in-the-loop”. European radiology experimental, 8(1), 81.‏ https://doi.org/10.1186/s41747-024-00485-7

Prabhod, K. J. (2024). The role of artificial intelligence in reducing healthcare costs and improving operational efficiency. Quarterly Journal of Emerging Technologies and Innovations, 9(2), 47-59.‏

Rahimi Esbo, S., Ghaemi-Amiri, M., & Mostafazadeh-Bora, M. (2024) Assessment of Medical Students' Acceptance, Knowledge, Attitudes, and Readiness toward Artificial Intelligence. J Mazandaran Univ Med Sci, 34(239), 88-95. (In Persian)

Rana, M. S., & Shuford, J. (2024). AI in healthcare: Transforming patient care through predictive analytics and decision support systems. Journal of Artificial Intelligence General Science, 1(1), 1–4. https://doi.org/10.60087/jaigs.v1i1.30

Rao, S. J., Isath, A., Krishnan, P., Tangsrivimol, J. A., Virk, H. U. H., Wang, Z., ... & Balouch, F. (2024). ChatGPT: A conceptual review of applications and utility in the field of medicine. Journal of Medical Systems, 48, 59. https://doi.org/10.1007/s10916-024-02075-x

Sakai, T. (2023). Literacy for appropriate use of medical big data and artificial intelligence. Yakugaku Zasshi: Journal of the Pharmaceutical Society of Japan, 143(6), 491–495. 143(6), 491–495. https://doi.org/10.1248/yakushi.22-00179-2

Sarrafzadeh, S., & Aboutaleb, E. (2023). The Importance of Ethics in Using of Artificial Intelligence in Medical Education. RME , 15(2), 1- 4. (In Persian)

Singh, Y., Hathaway, Q. A., & Erickson, B. J. (2024). Generative AI in oncological imaging: revolutionizing cancer detection and diagnosis. Oncotarget, 15, 607.‏ Singh, Y., Hathaway, Q. A., & Erickson, B. J. (2024). Generative AI in oncological imaging: Revolutionizing cancer detection and diagnosis. Oncotarget, 15, 607–608. https://doi.org/10.18632/oncotarget.28640

Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020). Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into imaging, 11(1), 14.‏Sit, C., Srinivasan, R., Amlani, A., et al. (2020). Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: A multicentre survey. Insights into Imaging, 11, 14. https://doi.org/10.1186/s13244-019-0830-7

Sorkhi, A., & Pourasghar, F. (2024). Artificial intelligence application in healthcare management: A review of challenges. International Journal of Healthcare Technology and Management, 21(1), 79–88. https://doi.org/10.1504/IJHTM.2024.136547

Subaveerapandiyan, A., Mvula, D., Ahmad, N., Taj, A., & Ahmed, M. G. (2024). Assessing AI literacy and attitudes among medical students: Implications for integration into healthcare practice. Journal of Health Organization and Management. Advance online publication. https://doi.org/10.1108/JHOM-04-2024-0154

Syed, W., & Al-Rawi, M. B. (2023). Assessment of awareness, perceptions, and opinions towards artificial intelligence among healthcare students in Riyadh, Saudi Arabia. Medicina, 59(5), 828. https://doi.org/10.3390/medicina59050828

Tavares, J. (2024). Application of Artificial Intelligence in Healthcare: The Need for More Interpretable Artificial Intelligence. Acta Médica Portuguesa, 37(6), 411–414. https://doi.org/10.20344/amp.20469

Wiljer, D., & Hakim, Z. (2019). Developing an Artificial Intelligence–Enabled Health Care Practice: Rewiring Health Care Professions for Better Care. Journal of Medical Imaging and Radiation Sciences, 50(4), S8–S14. https://doi.org/10.1016/j.jmir.2019.09.010

Wood, E. A., Ange, B. L., & Miller, D. D. (2021). Are we Ready to Integrate Artificial Intelligence Literacy Intomedical School Curriculum: Students and Faculty Survey. Journal of Medical Education and Curricular Development, 8, 23821205211024078.‏ https://doi.org/10.1177/23821205211024078