Investigation of the Knowledge Levels and Expectations of Medical Faculty Students on the Use of Artificial Intelligence in Prescribing
Year 2024,
Volume: 46 Issue: 6, 842 - 851, 07.11.2024
Metin Deniz Karakoç
,
Hatice Durusoy
Eren Özdemir
,
Emine Bilek
Abstract
Artificial Intelligence (AI) is utilized in various fields of medicine and is currently a significant focus of research in treatment planning. Our study aims to investigate the knowledge levels and expectations of medical students, the future physicians, regarding the role of AI in the prescription process. The research was conducted through a 15-question survey with participation from a total of 341 students from all classes of the medical faculty. The study found that male students currently use AI significantly more than female students (p<0.0001). Among the participants, 90.3% indicated that they might prefer to use AI when writing prescriptions, while 87.7% believed that the use of AI would expedite and simplify the prescription process. The majority of participants stated that AI could contribute to rational drug use by reducing prescription errors and the average cost of prescriptions. Additionally, 93.8% of the students expressed that they would consult AI for quicker results or as a suggestion tool but would rely on it only with their own supervision. Conversely, 46.3% of the participants felt that the use of AI could harm the doctor-patient relationship in the future. When asked whether AI could eventually replace physicians, 90.6% of the participants responded negatively. Among these, 81.55% believed that AI could be an important auxiliary tool used by physicians. It was observed that a significantly higher number of female students compared to male students held this opinion (p<0.0001). Furthermore, 78% of the students indicated that comprehensive education on the use of AI in medicine should be provided in medical schools.
References
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- 2. Osmani F, Arab-Zozani M, Shahali Z, Lotfi F. Evaluation of the effectiveness of electronic prescription in reducing medical and medical errors (systematic review study). Ann Pharm Fr. 2023;81(3):433-45.
- 3. Baysari MT, Raban MZ. The safety of computerised prescribing in hospitals. Aust Prescr. 2019;42(4):136-8.
- 4. Gates PJ, Hardie RA, Raban MZ, Li L, Westbrook JI. How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis. J Am Med Inform Assoc. 2021;28(1):167-76.
- 5. Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans. 2023;51(5):1871-9.
- 6. Li L, Zheng NN, Wang FY. On the crossroad of artificial intelligence: A revisit to Alan Turing and Norbert Wiener. IEEE Trans Cybern. 2019;49(10):3618-26.
- 7. Muthukrishnan N, Maleki F, Ovens K, Reinhold C, Forghani B, Forghani R. Brief history of artificial intelligence. Neuroimaging Clin N Am. 2020;30(4):393-9.
- 8. Shortliffe EH. Artificial intelligence in medicine: Weighing the accomplishments, hype, and promise. Yearb Med Inform. 2019;28(1):257-62.
- 9. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81.
- 10. Chong JH, Abdulkareem M, Petersen SE, Khanji MY. Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients. Curr Probl Cardiol. 2022;47(12):101330.
- 11. Amin D, Garzόn-Orjuela N, Garcia Pereira A, Parveen S, Vornhagen H, Vellinga A. Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review. Antibiotics (Basel). 2023;12(8):1293. Published 2023 Aug 6. doi:10.3390/antibiotics12081293
- 12. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7.
- 13. Huang Z, George MM, Tan YR, Natarajan K, Devasagayam E, Tay E, Manesh A, Varghese GM, Abraham OC, Zachariah A, Yap P, Lall D, Chow A. Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore. J Glob Antimicrob Resist. 2023;35:76-85.
- 14. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, Longhurst CA, Hogarth M, Smith DM. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 2023;183(6):589-596.
- 15. Savulescu J, Giubilini A, Vandersluis R, Mishra A. Ethics of artificial intelligence in medicine. Singapore Med J. 2024;65(3):150-158.
- 16. Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baeßler B. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640-1646.
- 17. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459-8486.
- 18. Ruksakulpiwat S, Kumar A, Ajibade A. Using ChatGPT in medical research: current status and future directions. J Multidiscip Healthc. 2023;16:1513-1520.
- 19. Driesnack S, Rücker F, Dietze-Jergus N, Bondarenko A, Pletz MW, Viehweger A. A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning. JAC Antimicrob Resist. 2024;6(4):dlae099.
- 20. Eker A, Çalışkan AA, Zorali A, Kaynak B, Derin ME. Medical Students’ Knowledge and Attitudes about Artificial Intelligence: A Cross-Sectional Survey. TED. 2023;22(68):41-5.
- 21. Öcal EE, Atay E, Önsüz MF, Algın F, Çokyiğit FK, Kılınç S, Köse ÖS, Yiğit FN. Tıp Fakültesi Öğrencilerinin Tıpta Yapay Zeka ile İlgili Düşünceleri. TÖAD. 2020;2(1):9-16.
- 22. Hathaway QA, Hogg JP, Lakhani DA. Need for medical student education in emerging technologies and artificial intelligence: fostering enthusiasm, rather than flight, from specialties most affected by emerging technologies. Acad Radiol. 2023;30(8):1770-1771.
- 23. Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, Sonoo T, Ogura T, Nakamura K, Goto T. Acceptance, barriers, and facilitators to implementing artificial intelligence-based decision support systems in emergency departments: quantitative and qualitative evaluation. JMIR Form Res. 2022;6(6):e36501.
- 24. Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci. 2019;40(9):624-635.
- 25. Salas M, Petracek J, Yalamanchili P, et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceut Med. 2022;36(5):295-306.
- 26. Dallari V, Sacchetto A, Saetti R, Calabrese L, Vittadello F, Gazzini L. Is artificial intelligence ready to replace specialist doctors entirely? ENT specialists vs ChatGPT: 1-0, ball at the center. Eur Arch Otorhinolaryngol. 2024;281(2):995-1023.
- 27. Anderson JM, Tejani I, Jarmain T, Kellett L, Moy RL. Artificial intelligence vs medical providers in the dermoscopic diagnosis of melanoma. Cutis. 2023;111(5):254-258.
- 28. Cohen M, Puntonet J, Sanchez J, Kierszbaum E, Crema M, Soyer P, Dion E. Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol. 2023;33(6):3974-3983.
- 29. Goodman RS, Patrinely JR, Stone CA Jr, Zimmerman E, Donald RR, Chang SS, Berkowitz ST, Finn AP, Jahangir E, Scoville EA, Reese TS, Friedman DL, Bastarache JA, van der Heijden YF, Wright JJ, Ye F, Carter N, Alexander MR, Choe JH, Chastain CA, et al. Accuracy and reliability of chatbot responses to physician questions. JAMA Netw Open. 2023;6(10):e2336483.
- 30. Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. J Am Med Inform Assoc. 2021;28(7):1574-1577.
- 31. Tamori H, Yamashina H, Mukai M, Morii Y, Suzuki T, Ogasawara K. Acceptance of the use of artificial intelligence in medicine among Japan's doctors and the public: a questionnaire survey. JMIR Hum Factors. 2022;9(1):e24680.
- 32. Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the ethical enigma: artificial intelligence in healthcare. Cureus. 2023;15(8):e43262.
- 33. Schukow C, Smith SC, Landgrebe E, Parasuraman S, Folaranmi OO, Paner GP, Amin MB. Application of ChatGPT in routine diagnostic pathology: promises, pitfalls, and potential future directions. Adv Anat Pathol. 2024;31(1):15-21.
Reçete Yazımında Yapay Zekâ Kullanımı Konusunda Tıp Fakültesi Öğrencilerinin Bilgi Düzeyleri ve Beklentilerinin Araştırılması
Year 2024,
Volume: 46 Issue: 6, 842 - 851, 07.11.2024
Metin Deniz Karakoç
,
Hatice Durusoy
Eren Özdemir
,
Emine Bilek
Abstract
Yapay Zekâ (YZ), tıbbın pek çok alanında kullanılmakta olup, günümüzde tedavi planlaması konusunda da yoğun olarak araştırılmaktadır. Çalışmamızda geleceğin hekimleri olan tıp fakültesi öğrencilerinin YZ ve reçete yazımı sürecinde üstleneceği rol konusundaki bilgi düzeyleri ve beklentilerinin araştırılması amaçlanmıştır. Araştırma, 15 soruluk bir anket formu aracılığı ile tıp fakültesinin her sınıfından toplam 341 öğrencinin katılımı ile gerçekleştirilmiştir. Çalışmada erkek öğrencilerin kızlara kıyasla hali hazırda YZ’yı önemli oranda daha fazla kullanmakta olduğu belirlenmiştir (p˂0,0001). Katılımcıların %90,3’ü reçete yazarken YZ kullanmayı tercih edebileceklerini belirtirken; %87,7’si YZ kullanımının reçete yazma sürecinde işlerini hızlandıracağını ve kolaylaştıracağını düşündüklerini ifade etmiştir. Katılımcıların çoğunluğu YZ kullanımının reçete hatalarını ve reçete ortalama maliyetlerini azaltarak akılcı ilaç kullanımına katkı sağlayabileceğini belirtmiştir. Öğrencilerin %93,8’i YZ’ya daha hızlı olmak ya da bir fikir vermesi amacıyla başvurabileceğini ve ancak kendi kontrolünü yapmak kaydıyla güvenebileceğini beyan etmiştir. Diğer yandan katılımcıların %46,3’ü YZ kullanımının gelecekte hasta-hekim ilişkisine zarar vereceğini belirtmiştir. YZ’nın, zamanla hekimin yerini alıp alamayacağı sorusuna katılımcıların %90,6’sı alamayacağı yönünde yanıt vermiştir. Bu öğrencilerin %81,55’i YZ’nın ancak hekimin kullandığı önemli bir yardımcı enstrüman olabileceğini beyan etmişlerdir. Bu şekilde düşünen kız öğrencilerin sayısının erkek öğrenci sayısına göre önemli derecede fazla olduğu saptanmıştır (p<0,0001). Öğrencilerin %78’i tıp fakültelerinde YZ’nin tıpta kullanımı konusunda kapsamlı bir eğitim verilmesi gerektiğini belirtmiştir. Hekim adaylarındaki genel istek, YZ’nın tıpta hangi sürecin içerisinde kullanılırsa kullanılsın son kararı verenin yine bir hekim olması gerektiği yönündedir. Ancak ivedi olarak mevzuat düzenlemelerine ve tıp fakültelerinde bu konuda kapsamlı eğitimler verilmesine ihtiyaç bulunmaktadır.
Ethical Statement
Çalışma için Pamukkale Üniversitesi Girişimsel Olmayan Klinik Çalışmalar Etik Kurulundan izin (29.01.2024-E.482845) alınmıştır.
Supporting Institution
Çalışma için hiçbir kişi ya da kurumdan herhangi bir destek alınmamıştır.
References
- 1. Karakoç MD, Özer Ö. Bir tıbbi onkoloji kliniğindeki parenteral ilaç uygulama hatalarının incelenmesi. Pamukkale Tıp Dergisi. 2022;15(4):720-727.
- 2. Osmani F, Arab-Zozani M, Shahali Z, Lotfi F. Evaluation of the effectiveness of electronic prescription in reducing medical and medical errors (systematic review study). Ann Pharm Fr. 2023;81(3):433-45.
- 3. Baysari MT, Raban MZ. The safety of computerised prescribing in hospitals. Aust Prescr. 2019;42(4):136-8.
- 4. Gates PJ, Hardie RA, Raban MZ, Li L, Westbrook JI. How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis. J Am Med Inform Assoc. 2021;28(1):167-76.
- 5. Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans. 2023;51(5):1871-9.
- 6. Li L, Zheng NN, Wang FY. On the crossroad of artificial intelligence: A revisit to Alan Turing and Norbert Wiener. IEEE Trans Cybern. 2019;49(10):3618-26.
- 7. Muthukrishnan N, Maleki F, Ovens K, Reinhold C, Forghani B, Forghani R. Brief history of artificial intelligence. Neuroimaging Clin N Am. 2020;30(4):393-9.
- 8. Shortliffe EH. Artificial intelligence in medicine: Weighing the accomplishments, hype, and promise. Yearb Med Inform. 2019;28(1):257-62.
- 9. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81.
- 10. Chong JH, Abdulkareem M, Petersen SE, Khanji MY. Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients. Curr Probl Cardiol. 2022;47(12):101330.
- 11. Amin D, Garzόn-Orjuela N, Garcia Pereira A, Parveen S, Vornhagen H, Vellinga A. Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review. Antibiotics (Basel). 2023;12(8):1293. Published 2023 Aug 6. doi:10.3390/antibiotics12081293
- 12. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7.
- 13. Huang Z, George MM, Tan YR, Natarajan K, Devasagayam E, Tay E, Manesh A, Varghese GM, Abraham OC, Zachariah A, Yap P, Lall D, Chow A. Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore. J Glob Antimicrob Resist. 2023;35:76-85.
- 14. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, Longhurst CA, Hogarth M, Smith DM. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 2023;183(6):589-596.
- 15. Savulescu J, Giubilini A, Vandersluis R, Mishra A. Ethics of artificial intelligence in medicine. Singapore Med J. 2024;65(3):150-158.
- 16. Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baeßler B. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640-1646.
- 17. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459-8486.
- 18. Ruksakulpiwat S, Kumar A, Ajibade A. Using ChatGPT in medical research: current status and future directions. J Multidiscip Healthc. 2023;16:1513-1520.
- 19. Driesnack S, Rücker F, Dietze-Jergus N, Bondarenko A, Pletz MW, Viehweger A. A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning. JAC Antimicrob Resist. 2024;6(4):dlae099.
- 20. Eker A, Çalışkan AA, Zorali A, Kaynak B, Derin ME. Medical Students’ Knowledge and Attitudes about Artificial Intelligence: A Cross-Sectional Survey. TED. 2023;22(68):41-5.
- 21. Öcal EE, Atay E, Önsüz MF, Algın F, Çokyiğit FK, Kılınç S, Köse ÖS, Yiğit FN. Tıp Fakültesi Öğrencilerinin Tıpta Yapay Zeka ile İlgili Düşünceleri. TÖAD. 2020;2(1):9-16.
- 22. Hathaway QA, Hogg JP, Lakhani DA. Need for medical student education in emerging technologies and artificial intelligence: fostering enthusiasm, rather than flight, from specialties most affected by emerging technologies. Acad Radiol. 2023;30(8):1770-1771.
- 23. Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, Sonoo T, Ogura T, Nakamura K, Goto T. Acceptance, barriers, and facilitators to implementing artificial intelligence-based decision support systems in emergency departments: quantitative and qualitative evaluation. JMIR Form Res. 2022;6(6):e36501.
- 24. Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci. 2019;40(9):624-635.
- 25. Salas M, Petracek J, Yalamanchili P, et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceut Med. 2022;36(5):295-306.
- 26. Dallari V, Sacchetto A, Saetti R, Calabrese L, Vittadello F, Gazzini L. Is artificial intelligence ready to replace specialist doctors entirely? ENT specialists vs ChatGPT: 1-0, ball at the center. Eur Arch Otorhinolaryngol. 2024;281(2):995-1023.
- 27. Anderson JM, Tejani I, Jarmain T, Kellett L, Moy RL. Artificial intelligence vs medical providers in the dermoscopic diagnosis of melanoma. Cutis. 2023;111(5):254-258.
- 28. Cohen M, Puntonet J, Sanchez J, Kierszbaum E, Crema M, Soyer P, Dion E. Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol. 2023;33(6):3974-3983.
- 29. Goodman RS, Patrinely JR, Stone CA Jr, Zimmerman E, Donald RR, Chang SS, Berkowitz ST, Finn AP, Jahangir E, Scoville EA, Reese TS, Friedman DL, Bastarache JA, van der Heijden YF, Wright JJ, Ye F, Carter N, Alexander MR, Choe JH, Chastain CA, et al. Accuracy and reliability of chatbot responses to physician questions. JAMA Netw Open. 2023;6(10):e2336483.
- 30. Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. J Am Med Inform Assoc. 2021;28(7):1574-1577.
- 31. Tamori H, Yamashina H, Mukai M, Morii Y, Suzuki T, Ogasawara K. Acceptance of the use of artificial intelligence in medicine among Japan's doctors and the public: a questionnaire survey. JMIR Hum Factors. 2022;9(1):e24680.
- 32. Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the ethical enigma: artificial intelligence in healthcare. Cureus. 2023;15(8):e43262.
- 33. Schukow C, Smith SC, Landgrebe E, Parasuraman S, Folaranmi OO, Paner GP, Amin MB. Application of ChatGPT in routine diagnostic pathology: promises, pitfalls, and potential future directions. Adv Anat Pathol. 2024;31(1):15-21.