SAĞLIK BİLİMLERİ ÖĞRENCİLERİNİN YAPAY ZEKAYA KARŞI BEKLENTİ VE YAKLAŞIMLARI

Abstract

Amaç: İnsanlar yapay zekanın yaşama olumlu katkı sağlayacağının yanısıra olumsuz etkilerinin de olabileceği endişesini taşımaktadır. Bu çalışmada sağlık bilimleri bölümlerinde öğrenim gören üniversite öğrencilerinin, yapay zeka konusundaki beklentileri, endişeleri ve düşüncelerinin belirlenmesi amaçlanmıştır.
Gereç ve Yöntem: Çalışma kesitsel olarak planlanmıştır. Çalışmada Kahramanmaraş Sütçü İmam Üniversitesinde öğrenim gören Diş Hekimliği, Tıp, Hemşirelik, Sağlık Yönetimi ve Ebelik öğrencilerine yapay zekanın gelecekteki teknolojik, sosyolojik ve mesleki etkilerine ilişkin 55 soruluk bir anket uygulanmıştır. Araştırmaya toplam 550 öğrenci dahil edilmiştir. Araştırmada öğrencilerin yapay zeka hakkındaki düşüncelerinin yanısıra yapay zeka teknolojilerinin kullanım sıklıkları da araştırılmıştır.
Bulgular: Çalışma bulgularına göre öğrenciler, yapay zeka teknolojilerini çoğunlukla kullanmaktadırlar. Öğrenciler çoğunlukla, yapay zekanın gelecekte sosyolojik açıdan olumsuz etkileri olacağını düşünmektedirler. Öğrenciler yapay zekanın, sağlık ve tıp alanında olumlu katkılar sağlayacağını düşünmektedir. Öğrenciler yapay zekanın tedavideki başarı oranını artıracağını düşünmektedir. Öğrenciler yapay zekanın gelecekte işsizliğe neden olacağını düşünmektedirler (p<0.05).
Sonuç: Öğrenciler yapay zekanın teknoloji ve sağlık alanında olumlu etkileri olacağını düşünmekte iken, sosyolojik açıdan ve işsizlik konusunda olumsuz etkileri olacağını düşünmektedirler. Üniversite öğrencilerine yapay zeka konusunda eğitim verilmelidir. Yapay zekanın gelecekte yaşama olumlu etkilerinin artırılması ve olumsuz etkilerinin azaltılması için araştırmalar yapılmalıdır.

Keywords

• Yapay Zeka
• Gelecek
• Üniversite Öğrencileri
• Sağlık bilimleri

THE APPROACHES AND EXPECTATIONS OF THE HEALTH SCIENCES STUDENTS TOWARDS ARTIFICIAL INTELLIGENCE

Abstract

Objective: People also think that artificial intelligence can have negative impacts as well as it has positive contributions to life. In this study, it has been aimed to determine the expectations, concerns and thoughts of university students studying in health sciences departments about artificial intelligence.
Materials and Methods: The study is planned as a cross-sectional research. In the study, a questionnaire consisting of 55 questions on the future technological, sociological and professional effects of artificial intelligence has been applied to the students. A total of 550 students have been included in the study. In the study, knowledge about the use of artificial intelligence technologies has been also analyzed in addition to the students' thoughts about artificial intelligence.
Results: Most of the students can use artificial intelligence technologies according to the findings of the study. Students mostly think that artificial intelligence will have negative sociological effects in the future. Students assume that artificial intelligence will bring positive contributions to the field of health and medicine. Students think that artificial intelligence will increase the success rate in treatment. Students also think that artificial intelligence will cause unemployment in the future (p<0.05).
Conclusion: Whereas students think that artificial intelligence will have positive effects on technology and health, they assume that it will have negative effects on the subjects of sociology and unemployment. It is supported that medical students ought to get an education on artificial intelligence. Research should be done to increase the positive effects of artificial intelligence on life in the future and to reduce its negative effects.

Keywords

• Artificial Intelligence
• Future
• University Students
• Health Sciences

References

1. 1. Kutsurelis JE. Forecasting financial markets using neural networks: An analysis of methods and accuracy. Naval Postgraduate School Monterey CA; 1998.
2. 2. Staub S, Karaman E, Kaya S, et al. Artificial Neural Network and Agility. Procedia - Soc Behav Sci. 2015; 195: 1477-1485.
3. 3. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019; 28(2): 73-81.
4. 4. Bahrammirzaee A. A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Comput Appl. 2010; 19(8): 1165-1195.
5. 5. Jha K, Doshi A, Patel P, et al. A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric. 2019; 2: 1-12.
6. 6. Hengstler M, Enkel E, Duelli S. Applied artificial intelligence and trust-The case of autonomous vehicles and medical assistance devices. Technol Forecast Soc Change. 2016; 105: 105-120.
7. 7. Pinto dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019; 29(4): 1640-1646.
8. 8. Sit C, Srinivasan R, Amlani A, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020; 11: 14.

9. 9. Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021; 85(1): 60-68.
10. 10. Cho SI, Han B, Hur K, et al. Perceptions and attitudes of medical students regarding artificial intelligence in dermatology. J Eur Acad Dermatol Venereol. 2021; 35: 72-73.
11. 11. Yun D, Xiang Y, Liu Z, et al. Attitudes towards medical artificial intelligence talent cultivation: an online survey study. Ann Transl Med. 2020; 8(11): 708-719.
12. 12. Sur J, Bose S, Khan F, et al. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: A survey. Imaging Sci Dent. 2020; 50(3): 193-198.
13. 13. Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Reports. 2020; 1: 100014.
14. 14. Nunnally JC. Phychometric theory. (2nd edit.). New York. 1978.
15. 15. Huang MH, Rust RT. Artificial Intelligence in Service. J Serv Res. 2018; 21(2): 155-172.
16. 16. Wuest T, Weimer D, Irgens C, et al. Machine learning in manufacturing: Advantages, challenges, and applications. Prod Manuf Res. 2016; 4(1): 23-45.
17. 17. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017; 2: 230-243.
18. 18. Bruun EPG, Duka A. Artificial intelligence, jobs and the future of work: racing with the machines. Basic Income Stud. 2018; 13(2): 1-15.
19. 19. Makridakis S. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures. 2017; 90: 46-60.
20. 20. Bloss CS, Wineinger NE, Peters M, et al. A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors. PeerJ. 2016; 4: e1554.
21. 21. Cho J, Quinlan MM, Park D, et al. Determinants of adoption of smartphone health apps among college students. Am J Health Behav. 2014; 38(6): 860-870.
22. 22. Körmendi A, Czki ZB, Végh BP, et al. Smartphone use can be addictive? A case report. J Behav Addict. 2016; 5(3): 548-552.
23. 23. Becker A. Artificial intelligence in medicine: What is it doing for us today? Health Policy Technol. 2019; 8: 198-205.
24. 24. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019; 25: 30-36.
25. 25. Visvikis D, Cheze Le Rest C, Jaouen V, et al. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nuc Med Mol Imag. 2019: 46; 2630-2637.
26. 26. Haug S, Paz Castro R, Kwon M, et al. Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict. 2015; 4(4): 299-307.
27. 27. King RC, Dong S. The impact of smartphone on young adults. Bus Manag Rev. 2017; 8(4): 342.
28. 28. Zhong ZJ, Yao MZ. Gaming motivations, avatar-self identification and symptoms of online game addiction. Asian J Commun. 2013; 23(5): 555-573.
29. 29. Ford MR. The rise of the robots: technology and the threat of mass unemployment. Oneworld Publ. 2017.
30. 30. Liang Y, Lee SA. Fear of Autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. Int J Soc Robot. 2017; 9(3): 379-384.
31. 31. Shneiderman B. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. Int J Hum Comput Interact. 2020; 36(6): 495-504.
32. 32. 32.Dos Santos DP, Giese D, Brodehl S, et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur radiol. 2019; 29(4): 1640-1646.