Comparison of awareness and attitudes toward artificial intelligence among Russian- and English-speaking students at Orenburg State Medical University

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Abstract

BACKGROUND: Artificial Intelligence (AI) is actively implemented in medicine. Since medical students are future physicians, an assessment of their awareness and attitudes toward AI is important.

AIM: To compare the degree of awareness and attitudes toward AI among Russian-speaking students of the Orenburg State Medical University (OrSMU) from the Russian Federation and English-speaking students from the Republic of India.

METHODS: From March 12 to 25, 2023, a voluntary anonymous survey (28 questions) was offered to OrSMU students using the Google Forms platform. An English-language version was prepared for foreign students. All responses were analyzed statistically (calculation of mean values using Likert scale, Student’s t-test, and Pearson’s chi-square test).

RESULTS: A total of 331 students participated in the survey, including 214 Russian-speaking and 117 English-speaking participants (127 males, 202 females, and two did not indicate gender). All participants were divided into 2 subgroups: junior (1–3 years, 200 participants) and senior year (4–6 years, 131 participants) students. The vast majority of respondents (92.3%) knew what AI is, with a higher percentage (p<0.001) among Russian-speaking students (95.8%) versus English-speaking students (84.6%). Only 34.1% of Russian-speaking and 46.2% of English-speaking students (p=0.032) were aware of the possibility of using AI in medicine. A total of 28.5% and 23.4% of Russian-speaking and 44.4% and 38.5% of English-speaking respondents, respectively, were aware of the use of AI in diagnostic radiology and pathological anatomy (p=0.004). Students expressed the greatest agreement with the statement that AI will play a significant role in the development and support of medicine in the future (mean Likert scale value of 4.23). Students were the least likely to agree with the statement that AI’s diagnostic abilities are superior to the human physician’s clinical experience (mean of 2.84). In the case of a disagreement between the AI and the physician, 76.7% of respondents would trust the latter to make the final decision. Most respondents considered diagnostic radiology, electrocardiogram analysis, and pathological anatomy to be promising areas for the use of AI (91.3%, 71.3%, and 70.4%, respectively). For the rest of the statements about attitudes toward AI, the average values ranged from 3.63 to 4.33. Among the disadvantages of using AI, the threat of data leakage was reported. The advantages were quick data analysis and assistance in diagnosis.

CONCLUSIONS: English-speaking students were more aware of the use of AI in medicine, whereas students from Russia showed a more positive attitude toward AI. However, in the case of a disagreement between the physician and the AI, both groups of respondents would trust the physician to make the decision.

Full Text

BACKGROUND: Artificial Intelligence (AI) is actively implemented in medicine. Since medical students are future physicians, an assessment of their awareness and attitudes toward AI is important.

AIM: To compare the degree of awareness and attitudes toward AI among Russian-speaking students of the Orenburg State Medical University (OrSMU) from the Russian Federation and English-speaking students from the Republic of India.

METHODS: From March 12 to 25, 2023, a voluntary anonymous survey (28 questions) was offered to OrSMU students using the Google Forms platform. An English-language version was prepared for foreign students. All responses were analyzed statistically (calculation of mean values using Likert scale, Student’s t-test, and Pearson’s chi-square test).

RESULTS: A total of 331 students participated in the survey, including 214 Russian-speaking and 117 English-speaking participants (127 males, 202 females, and two did not indicate gender). All participants were divided into 2 subgroups: junior (1–3 years, 200 participants) and senior year (4–6 years, 131 participants) students. The vast majority of respondents (92.3%) knew what AI is, with a higher percentage (p<0.001) among Russian-speaking students (95.8%) versus English-speaking students (84.6%). Only 34.1% of Russian-speaking and 46.2% of English-speaking students (p=0.032) were aware of the possibility of using AI in medicine. A total of 28.5% and 23.4% of Russian-speaking and 44.4% and 38.5% of English-speaking respondents, respectively, were aware of the use of AI in diagnostic radiology and pathological anatomy (p=0.004). Students expressed the greatest agreement with the statement that AI will play a significant role in the development and support of medicine in the future (mean Likert scale value of 4.23). Students were the least likely to agree with the statement that AI’s diagnostic abilities are superior to the human physician’s clinical experience (mean of 2.84). In the case of a disagreement between the AI and the physician, 76.7% of respondents would trust the latter to make the final decision. Most respondents considered diagnostic radiology, electrocardiogram analysis, and pathological anatomy to be promising areas for the use of AI (91.3%, 71.3%, and 70.4%, respectively). For the rest of the statements about attitudes toward AI, the average values ranged from 3.63 to 4.33. Among the disadvantages of using AI, the threat of data leakage was reported. The advantages were quick data analysis and assistance in diagnosis.

CONCLUSIONS: English-speaking students were more aware of the use of AI in medicine, whereas students from Russia showed a more positive attitude toward AI. However, in the case of a disagreement between the physician and the AI, both groups of respondents would trust the physician to make the decision.

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About the authors

Mariia L. Kalinina

Orenburg State Medical University

Email: maria.kalinina1990@gmail.com
ORCID iD: 0009-0009-1293-8243
Russian Federation, Orenburg

Aleksei P. Svitachev

Orenburg State Medical University

Email: alekseismed@gmail.com
ORCID iD: 0009-0006-8539-1267
Russian Federation, Orenburg

Diganta Biswas

Orenburg State Medical University

Email: digantabiswas143@gmail.com
ORCID iD: 0009-0003-6706-0649
Russian Federation, Orenburg

Pandey Vishnu

Orenburg State Medical University

Author for correspondence.
Email: manipaljaipur068@gmail.com
ORCID iD: 0009-0007-2317-3296
Russian Federation, Orenburg

References

  1. Vikhrov IP, Ashirbaev ShP. The Attitude of Teachers and Students of Medical Universities to Artificial Intelligence Technologies in Uzbekistan. Perspektivy razvitiya vysshego obrazovaniya. 2021;(10):19–39. (In Russ). Available from: https://cyberleninka.ru/article/n/otnoshenie-prepodavateley-i-studentov-meditsinskih-vuzov-k-tehnologiyam-iskusstvennogo-intellekta-v-uzbekistane.
  2. Kvon GM, Vaks VB, Pozdeeva OG. Using the Likert scale in the study of students’ motivational factors. Koncept. 2018;(11):1039–1051. (In Russ). Available from: https://cyberleninka.ru/article/n/ispolzovanie-shkaly-laykerta-pri-issledovanii-motivatsionnyh-faktorov-obuchayuschihsya.
  3. Strelnikov SS, Vokhmintsev AP, Katkova AL, Ushakova OM. Artificial intelligence in medicine: correspondence of everyday and professional understanding. Problemy sovremennogo obrazovaniya. 2022;(6):55–69. Available from: https://cyberleninka.ru/article/n/iskusstvennyy-intellekt-v-meditsine-sootnoshenie-obydennogo-i-professionalnogo-ponimaniya.
  4. European Society of Radiology (ESR). Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging. 2019;10(1):105. doi: 10.1186/s13244-019-0798-3
  5. Fan W, Liu J, Zhu S et al. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann Oper Res. 2020;294:567–592. doi: 10.1007/s10479-018-2818-y
  6. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) — development, validity and reliability study. BMC Med Educ. 2021;21:112. doi: 10.1186/s12909-021-02546-6
  7. Oh S, Kim JH, Choi SW, et al. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J Med Internet Res. 2019;21(3):e12422. doi: 10.2196/12422
  8. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2:35. doi: 10.1186/s41747-018-0061-6
  9. 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(1):14. doi: 10.1186/s13244-019-0830-7
  10. Wong SH, Al-Hasani H, Alam Z, et al. Artificial intelligence in radiology: how will we be affected? Eur Radiol. 2019;29:141–143. doi: 10.1007/s00330-018-5644-3
  11. Worley P. Open thinking, closed questioning: Two kinds of open and closed question. Journal of Philosophy in Schools. 2015;2(2). doi: 10.21913/JPS.v2i2.1269

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