TIP FAKÜLTESİ ÖĞRENCİLERİNİN TIBBİ YAPAY ZEKA HAZIR BULUNUŞLULUĞUNUN İNCELENMESİ

Yıl 2024, Cilt: 25 Sayı: 2, 143 – 149, 29.04.2024

Kerem Gencer , Gülcan Gencer

https://doi.org/10.18229/kocatepetip.1295779

Öz

AMAÇ: Bu çalışmada, yapay zekanın (YZ) sağlık alanında hayatımızın ayrılmaz bir parçası haline gelmesi ile birlikte tıp öğrencilerinin yapay zeka teknolojileri ve yapay zekanın tıptaki uygulamaları konusunda algıladıkları hazır bulunuşlukları araştırılmıştır.
GEREÇ VE YÖNTEM: Bu araştırma Afyonkarahisar Sağlık Bilimleri Üniversitesi’nde (AFSÜ) öğrenim gören 1-3. sınıf öğrencilerine uygulanmıştır. Çalışmaya katılmayı kabul eden 203 öğrenci örnekleme alınmıştır. Veriler araştırmacılar tarafından hazırlanan sosyodemografik form ve tıbbi yapay zeka hazır bulunuşluluk ölçeği ile toplanmıştır. Verilerin analizi R.4.3.2 ortamı kullanılarak yapılmıştır.
BULGULAR: Çalışmada yer alan 203 öğrencinin 121’i (% 59,6) kız öğrenci, 82’si (% 40,4) erkek öğrencidir. Tıbbi yapay zeka bilişsel hazır bulunuşluluğunun erkek öğrencilerde, kız öğrencilere göre daha fazla olduğu ve bunun istatistiksel olarak anlamlı olduğu görülürken, bilişsel, öngörü ve etik hazır bulunuşluluklarında kız ve erkek öğrenciler arasında anlamlı farklılık bulunmamıştır. Ayrıca, öğrencilerin tıbbi yapay zeka bulunuşlulukları öğrencilerin sınıflarına göre önemli bir farklılık göstermemektedir.
SONUÇ: Öğrenciler için yapay zeka teknolojileri ve uygulamaları konusunda algılanan hazır bulunuşluk düzeyleri değerlendirildiğinde, tıbbi yapay zeka hazır bulunuşluluk ölçeği’nin alt boyutlarında genel olarak puanların düşük olduğu görülmüştür. En düşük puana bilişsel alt boyut sahiptir. En yüksek puan ise etik hazır bulunuşlulukta görülmüştür. Sonuç olarak elde edilen bu puanlar, öğrenci ihtiyaçlarının değerlendirilmesinde ve tıp eğitiminde değerli bir müfredatın geliştirilmesi için bir araç olarak kullanılabilir.

Anahtar Kelimeler

Yapay zeka, Tıp, Tıp öğrencileri, Müfredat.

Kaynakça

• 1. Nilsson NJ, Nilsson NJ. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc. San Francisco, CA, United States, 1998.
• 2. Lee J, Wu AS, Li D, Kulasegaram K. Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Academic Medicine. 2021;96:62-70.
• 3. Imran N, Jawaid M. Artificial intelligence in medical education: Are we ready for it? Pak J Med Sci. 2020;36(5):857-9.
• 4. Han E-R, Yeo S, Kim M-J, Lee Y-H, Park K-H, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Medical Education. 2019;19(1):460.
• 5. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digital Medicine. 2018;27(1):54.
• 6. Karaca O, Çalışkan SA, Demir K. Tıp Eğitiminde Yapay Zeka İçinde Eğitimde Yapay Zeka Kuramdan Uygulamaya Bölümü. Ankara: Pegem Akademi. 2020:346-63.
• 7. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine. 2019;132(7):795-801.
• 8. Masters K. Artificial intelligence in medical education. Medical Teacher. 2019;41(9):976-80.
• 9. Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5(1):e13930.
• 10. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016;315(6):551-2.
• 11. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
• 12. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nature Medicine. 2020;26(6):900-8.
• 13. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
• 14. Lip GYH, Nieuwlaat R, Pisters R, Lane DA, Crijns HJGM. Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach: The Euro Heart Survey on Atrial Fibrillation. Chest. 2010;137(2):263-72.
• 15. O'Mahony C, Jichi F, Pavlou M, et al. A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD). European Heart Journal. 2013;35(30):2010-20.
• 16. Lu P, Abedi V, Mei Y, et al. Supervised learning methods in modeling of CD4+ T cell heterogeneity. BioData Mining. 2015;8:1-21.
• 17. Chen Y, Wang X, Jung Y, et al. Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost. Physiological Measurement. 2018;39(10):104006.
• 18. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digital Medicine. 2018;1(1):9.
• 19. Kagawa R, Kawazoe Y, Ida Y, et al. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach. Journal of Diabetes Science and Technology. 2016;11(4):791-9.
• 20. Bassaganya-Riera J, Hontecillas R. Introduction to Accelerated Path to Cures and Precision Medicine in Inflammatory Bowel Disease. In: Bassaganya-Riera, J. (eds) Accelerated Path to Cures. Springer, Cham. 2018:1-6.
• 21. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular Neurology. 2017;2(4):230-243.
• 22. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2015;13:8-17.
• 23. Houssein EH, Emam MM, Ali AA, et al. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications. 2021;167:114161.
• 24. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS- MS) – development, validity and reliability study. BMC Medical Educatian. 2021; 21:112-20.
• 25. Blease C, Kharko A, Bernstein M, et al. Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland. BMJ Health Care Inform. 2022;29(1):1-4.
• 26. Santomartino SM, Yi PH.Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Academic Radiology. 2022;29(11):1748-1756.
• 27. Reeder K, Lee H. Impact of artificial intelligence on US medical students' choice of radiology, Clinical Imaging. 2022;81:67-71.
• 28. Öcal EE, Atay E, Önsüz MF ve ark. Tıp fakültesi öğrencilerinin tıpta yapay zekâ ile ilgili düşünceleri. Türk Tıp Öğrencileri Araştırma Dergisi. 2020;2(1), 9-16.

INVESTIGATION OF MEDICAL ARTIFICIAL INTELLIGENCE READINESS OF MEDICAL FACULTY STUDENTS

Yıl 2024, Cilt: 25 Sayı: 2, 143 – 149, 29.04.2024

Kerem Gencer , Gülcan Gencer

https://doi.org/10.18229/kocatepetip.1295779

Öz

OBJECTIVE: In this study, with the fact that artificial intelligence (AI) has become an integral part of our lives in the field of health, medical students’ perceived readiness for artificial intelligence technologies and applications of artificial intelligence in medicine has been investigated.
MATERIAL AND METHODS: This research was conducted in the 1st-3rd grades studying at Afyonkarahisar Health Sciences University (AFSÜ). 203 students who agreed to participate in the study were included in the sample. Data were collected with the sociodemographic form and medical artificial intelligence readiness scale prepared by the researchers. Data analysis was done using the R 4.0.2 environment.
RESULTS: Of the 203 students included in the study, 121 (59.6%) were female students, and 82 (40.4%) male students. While it was observed that medical artificial intelligence cognitive readiness was higher in male students than in female students and this was statistically significant, there was no significant difference between male and female students in their cognitive, foresight, and ethical readiness. Additionally, students’ medical artificial intelligence presence does not differ significantly according to students’ grades.
CONCLUSIONS: When the students’ perceived readiness for artificial intelligence technologies and applications were evaluated, it was seen that the scores were generally low in the sub-dimensions of the medical artificial intelligence readiness scale. The cognitive sub-dimension has the lowest score. The highest score was seen in ethical readiness. These scores can be used as a tool to assess student needs and develop a valuable curriculum in medical education.

Anahtar Kelimeler

Artificial intelligence, Medicine, Medical students, Curriculum.

Kaynakça

• 1. Nilsson NJ, Nilsson NJ. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc. San Francisco, CA, United States, 1998.
• 2. Lee J, Wu AS, Li D, Kulasegaram K. Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Academic Medicine. 2021;96:62-70.
• 3. Imran N, Jawaid M. Artificial intelligence in medical education: Are we ready for it? Pak J Med Sci. 2020;36(5):857-9.
• 4. Han E-R, Yeo S, Kim M-J, Lee Y-H, Park K-H, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Medical Education. 2019;19(1):460.
• 5. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digital Medicine. 2018;27(1):54.
• 6. Karaca O, Çalışkan SA, Demir K. Tıp Eğitiminde Yapay Zeka İçinde Eğitimde Yapay Zeka Kuramdan Uygulamaya Bölümü. Ankara: Pegem Akademi. 2020:346-63.
• 7. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine. 2019;132(7):795-801.
• 8. Masters K. Artificial intelligence in medical education. Medical Teacher. 2019;41(9):976-80.
• 9. Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5(1):e13930.
• 10. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016;315(6):551-2.
• 11. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
• 12. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nature Medicine. 2020;26(6):900-8.
• 13. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
• 14. Lip GYH, Nieuwlaat R, Pisters R, Lane DA, Crijns HJGM. Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach: The Euro Heart Survey on Atrial Fibrillation. Chest. 2010;137(2):263-72.
• 15. O'Mahony C, Jichi F, Pavlou M, et al. A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD). European Heart Journal. 2013;35(30):2010-20.
• 16. Lu P, Abedi V, Mei Y, et al. Supervised learning methods in modeling of CD4+ T cell heterogeneity. BioData Mining. 2015;8:1-21.
• 17. Chen Y, Wang X, Jung Y, et al. Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost. Physiological Measurement. 2018;39(10):104006.
• 18. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digital Medicine. 2018;1(1):9.
• 19. Kagawa R, Kawazoe Y, Ida Y, et al. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach. Journal of Diabetes Science and Technology. 2016;11(4):791-9.
• 20. Bassaganya-Riera J, Hontecillas R. Introduction to Accelerated Path to Cures and Precision Medicine in Inflammatory Bowel Disease. In: Bassaganya-Riera, J. (eds) Accelerated Path to Cures. Springer, Cham. 2018:1-6.
• 21. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular Neurology. 2017;2(4):230-243.
• 22. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2015;13:8-17.
• 23. Houssein EH, Emam MM, Ali AA, et al. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications. 2021;167:114161.
• 24. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS- MS) – development, validity and reliability study. BMC Medical Educatian. 2021; 21:112-20.
• 25. Blease C, Kharko A, Bernstein M, et al. Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland. BMJ Health Care Inform. 2022;29(1):1-4.
• 26. Santomartino SM, Yi PH.Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Academic Radiology. 2022;29(11):1748-1756.
• 27. Reeder K, Lee H. Impact of artificial intelligence on US medical students' choice of radiology, Clinical Imaging. 2022;81:67-71.
• 28. Öcal EE, Atay E, Önsüz MF ve ark. Tıp fakültesi öğrencilerinin tıpta yapay zekâ ile ilgili düşünceleri. Türk Tıp Öğrencileri Araştırma Dergisi. 2020;2(1), 9-16.

Toplam 28 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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