Artificial Intelligence Readiness Status of Medical Faculty Students

Abstract

Objective: This research aims to examine the knowledge level and awareness of Faculty of Medicine students about medical artificial intelligence technologies. Methods: In this study involving students studying at Medical Faculties in Turkey, descriptive questionnaire, and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) were used. The suitability of continuous variables for normal distribution was tested with the Shapiro-Wilk test. Descriptive statistics for continuous variables are presented as mean and standard deviation or median (Q1-Q3). Descriptive statistics for categorical variables are reported as frequencies and percentages. Homogeneity of variances was evaluated with the Levene test. Mann Whitney U test was used to compare the scale subdimension and total scores according to two independent groups; One-way Analysis of Variance or Kruskal Wallis test was used to compare the scale subdimensions and total scores according to more than two independent groups. Dunn-Bonferroni test was used for multiple comparisons if there was a significant difference between the groups. The relationship between MAIRS-MS subdimensions and MAIRS-MS score was evaluated with the Spearman correlation coefficient. MAIRS-MS reliability was determined by Cronbach alpha value. The value of p<0.05 was determined as the level of statistical significance. Results: MAIRS-MS scores of students who thought that artificial intelligence technologies would contribute to the development of the profession and reduce the workload were found to be higher (p=0.003; p<0.001). Conclusions: It is seen that the students' awareness level about medical artificial intelligence is high, and they have the ability to use artificial intelligence technologies.

Keywords

• Artificial intelligence
• Artificial intelligence applications in medicine
• Education
• MAIRS-MS
• Technology.

Tıp Fakültesi Öğrencilerinin Yapay Zekâ Hazırbulunuşluk Durumları

Abstract

Amaç: Bu araştırmada Tıp Fakültesi öğrencilerinin tıbbi yapay zekâ teknolojileri hakkındaki bilgi düzeyleri ve farkındalıklarının incelenmesi amaçlanmıştır. Gereç ve Yöntem: Türkiye’deki Tıp Fakültelerinde öğrenim gören öğrencilerin katıldığı bu çalışmada, tanımlayıcı bir anket ve Tıp Fakültesi öğrencileri için Tıbbi Yapay Zekâ Hazır Bulunuşluk ölçeği (Medical Artificial Intelligence Readiness Scale for Medical Students-MAIRS-MS) kullanılmıştır. Sürekli değişkenlerin normal dağılıma uygunluğu Shapiro-Wilk testi ile test edilmiştir. Sürekli değişkenler için betimleyici istatistikler ortalama ve standart sapma ya da medyan (Q1-Q3) olarak verilmiştir. Kategorik değişkenler için betimleyici istatistikler frekans ve yüzde olarak belirtilmiştir. Varyansların homojenliği Levene testi ile değerlendirilmiştir. Ölçek alt boyut ve toplam puanlarının iki bağımsız gruba göre karşılaştırılmasında Mann Whitney U testi, ikiden fazla bağımsız gruba göre karşılaştırılmasında Tek Yönlü Varyans Analizi ya da Kruskal Wallis testi, gruplar arasında önemli farklılık bulunması durumunda çoklu karşılaştırmalarda Dunn-Bonferroni test kullanılmıştır. MAIRS-MS alt boyutları ve MAIRS-MS puanı arasındaki ilişki Spearman korelasyon katsayısı ile değerlendirilmiştir. MAIRS-MS güvenilirliği Cronbach alpha değeri ile belirlenmiştir. p<0.05 değeri istatistiksel önemlilik düzeyi olarak belirlenmiştir. Bulgular: Yapay zekâ teknolojilerinin mesleğin gelişmesinde katkısı olacağını (p=0,003), iş yükünü azaltacağını (p<0,001) düşünen öğrencilerin, MAIRS-MS puan dağılımlarının daha yüksek olduğu bulunmuştur. Sonuç: Öğrencilerin tıbbi yapay zekâ konusundaki farkındalık düzeylerinin yüksek olduğu ve yapay zekâ teknolojilerini kullanma becerilerine sahip oldukları görülmüştür.

Keywords

• Yapay zekâ
• Tıpta yapay zekâ uygulamaları
• Eğitim
• MAIRS-MS
• Teknoloji

References

1. 1. Isler B, Kılıç M. Eğitimde yapay zekâ kullanımı ve gelişimi. Yeni Medya Elektronik Dergisi. 2021;5(1):1-11.
2. 2. Hinton G. Deep learning a technology with the potential to transform health care. Jama. 2018;320(11):1101-2.
3. 3. Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review. 2019;61(4):5-14.
4. 4. Onder HH. Uzaktan Egitimde Bilgisayar Kullanimi ve Uzman Sistemler. TOJET: The Turkish Online Journal of Educational Technology. 2003;2(3).
5. 5. Doğaç A. MYCIN I- Uzman Sistemler. Elektrik Mühendisliği. 2015;7(7):87-91.
6. 6. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal endoscopy. 2020;92(4):807-12.
7. 7. Ishak WHW, Siraj F. Artificial intelligence in medical application: An exploration. Health Informatics Europe Journal. 2002;16.
8. 8. Kulikowski CA. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art–with reflections on present aim challenges. Yearbook of medical informatics. 2019;28(01):249-56.

9. 9. Atlan F, Pence I. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. Acta Infologica, 2021;5(1):207-30.
10. 10. Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019;8(7):2328.
11. 11. Xing L, Giger ML, Min JK (Eds.). Artificial intelligence in medicine: technical basis and clinical applications. Academic Press; 2020.
12. 12. Russak AJ, Chaudhry F, De Freitas JK, Baron G, Chaudhry FF, Bienstock S, et al. Machine learning in cardiology ensuring clinical impact lives up to the hype. Journal of Cardiovascular Pharmacology and Therapeutics. 2020;25(5):379-90.
13. 13. Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR medical education. 2019;5(2):e16048.
14. 14. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ digital medicine. 2018;1(1):54.
15. 15. Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? Journal of educational evaluation for health professions, 2019;16.
16. 16. Ocal EE, Emrah AT, Onsuz MF, Algın F, Cokyigit FK, Kılınc S, Kose OS, Yigit FN. Tıp fakültesi öğrencilerinin tıpta yapay zekâ ile ilgili düşünceleri. Türk Tıp Öğrencileri Araştırma Dergisi. 2020;2(1):9-16.
17. 17. Guo J, Li B. The application of medical artificial intelligence technology in rural areas of developing countries. Health equity. 2018;2(1):174-81.
18. 18. Caliskan SA, Demir K, Karaca O. Sağlık Çalışanları Yapay Zekaya Hazır Mı? Are Healthcare Workers Ready for Artificial Intelligence? Journal of Artificial Intelligence in Health Sciences. 2021;1(1):35-5. 19. Karaca O, Caliskan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)-development, validity and reliability study. BMC medical education. 2021; 21:1-9. 20. Xuan PY, Fahumida MIF, Al Nazir Hussain MI, Jayathilake NT, Khobragade S, Soe HHK, Moe S, Htay MNN. Readiness towards artificial intelligence among undergraduate medical students in Malaysia. Education in Medicine Journal. 2023;15(2):49–60.
19. 21. Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into Imaging. 2020;11(1):14.
20. 22. Gherheș V, Obrad C. Technical and humanities students’ perspectives on the development and sustainability of artificial intelligence (AI). Sustainability. 2018;10(9):3066.
21. 23. Moodi GA, Moghadasin M, Emadzadeh A, Mastour H. Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). BMC Medical Education, 2023; 23(1):577.
22. 24. Abid S, Awan B, Ismail T, Sarwar N, Sarwar G, Tariq M, et al. Artificial intelligence: medical student s attitude in district Peshawar Pakistan. Pakistan J Public Health. 2019;9(1):19–21.
23. 25. Hamd Z, Elshami W, Al Kawas S, Aljuaid H, Abuzaid MM. A closer look at the current knowledge and prospects of artificial intelligence integration in dentistry practice: a cross-sectional study. Heliyon. 2023;9(6):e17089.
24. 26. Boillat T, Nawaz FA, Rivas H. Readiness to Embrace Artificial intelligence among medical doctors and students: questionnaire-based study. JMIR Med Educ. 2022;8(2):e34973. 27. Hashimoto DA, Johnson KB. The Use of Artificial Intelligence Tools to prepare Medical School Applications. Acad Med. 2023.