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Attitudes and perceptions of Thai medical students towards artificial intelligence in radiology and medicine | BMC Medical Education


  • Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM et al. Clinical applications of artificial intelligence – An updated review. J Clin Med 2022, 11(8).

  • Lorkowski J, Grzegorowska O, Pokorski M. Artificial intelligence in healthcare: an overview. Adv Exp Med Biol. 2021;1335:1–10.

    Article Google Scholar

  • Clough RAJ, Sparkes WA, Clough OT, Sykes JT, Steventon AT, King K. Transforming healthcare documentation: harnessing the potential of AI to generate discharge summaries. BJGP opened 2024, 8(1):BJGPO.2023.0116.

  • Angkurawaranon S, Sanorsieng N, Unsrisong K, Inkeaw P, Sripan P, Khumrin P, Angkurawaranon C, Vaniyapong T, Chitapanarux I. A performance comparison between a resident deep learning model for localization and classification of intracranial hemorrhage. Sci Rep. 2023;13(1):9975.

    Article Google Scholar

  • Mello-Thoms C, Mello CAB. Clinical applications of artificial intelligence in radiology. Brother J Radiol. 2023;96(1150):20221031.

    Article Google Scholar

  • Khan FA, Majidulla A, Tavaziva G, Nazish A, Abidi SK, Benedetti A, Menzies D, Johnston JC, Khan AJ, Saeed S. Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed diseases. Lancet Digit Health. 2020;2(11):e573–81.

    Article Google Scholar

  • Murphy K, Habib SS, Zaidi SMA, Khowaja S, Khan A, Melendez J, Scholten ET, Amad F, Schalekamp S, Verhagen M, et al. Computer-aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system. Sci Rep. 2020;10(1):5492.

    Article Google Scholar

  • Philipsen RH, Sánchez CI, Maduskar P, Melendez J, Peters-Bax L, Peter JG, Dawson R, Theron G, Dheda K, van Ginneken B. Automated chest radiography as triage for Xpert testing in resource-limited settings: a prospective study of diagnostic accuracy and costs. (2045-2322 (electronic)).

  • Inkeaw P, Angkurawaranon S, Khumrin P, Inmutto N, Traisathit P, Chaijaruwanich J, Angkurawaranon C, Chitapanarux I. Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using a 3D deep learning model. (1879 – 0534 (electronic)).

  • Phaphuangwittayakul A, Guo Y, Ying FA-O, Dawod AY, Angkurawaranon S, Angkurawaranon C. An optimal deep learning framework for detection and quantification of multi-type hemorrhagic lesions in head CT images for traumatic brain injury. (1573-7497 (electronic)).

  • Khunte M, Chae A, Wang R, Jain R, Sun Y, Sollee JR, Jiao Z, Bai HX. Trends in clinical validation and use of US Food and Drug Administration-approved artificial intelligence algorithms for medical imaging. Clin Radiol. 2023;78(2):123–9.

    Article Google Scholar

  • Zarei M, Eftekhari Mamaghani H, Abbasi A, Hosseini MS. Application of Artificial Intelligence in Medical Education: An Overview of Benefits, Challenges, and Solutions. Med Clinica Practica. 2024;7(2):100422.

    Article Google Scholar

  • Kolachalama VB, Garg PS. Machine learning and medical education. Npj grade Med. 2018;1(1):54.

    Article Google Scholar

  • Li Q, Qin Y. AI in medical education: medical students’ perceptions, curriculum recommendations and design suggestions. BMC Medical education. 2023;23(1):852.

    Article Google Scholar

  • Pucchio A, Rathagirishnan R, Caton N, Gariscsak PJ, Del Papa J, Nabhen JJ, Vo V, Lee W, Moraes FY. Exploring Exposure to Artificial Intelligence in Medical Education: A Canadian Cross-Sectional Mixed Methods Study. BMC Medical education. 2022;22(1):815.

    Article Google Scholar

  • Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, et al. An exploratory review of artificial intelligence in medical education: BEME Guide 84. Med Teach. 2024;46(4):446–70.

    Article Google Scholar

  • Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baessler B. Medical students’ attitudes toward artificial intelligence: a multicenter study. Eur Radiol. 2019;29(4):1640–6.

    Article Google Scholar

  • van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H, Heverhagen J, Poellinger A. A survey of the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may be afraid that other disciplines will take over. Eur J Radiol. 2019;121:108742.

    Article Google Scholar

  • Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, Nicolaou S. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: ANational Survey Study. Acad Radiol. 2019;26(4):566–77.

    Article Google Scholar

  • Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of British medical students towards artificial intelligence and radiology: a multi-centre study. Insights into imaging. 2020;11(1):14.

    Article Google Scholar

  • Yang L, Ene IC, Arabi Belaghi R, Koff D, Stein N, Santaguida PL. Stakeholder perspectives on the future of artificial intelligence in radiology: a scoping review. Eur Radiol. 2022;32(3):1477–95.

    Article Google Scholar

  • Pongtriang P, Rakhab A, Bian J, Guo Y, Maitree K. Challenges in Adopting Artificial Intelligence to Improve Healthcare Systems and Outcomes in Thailand. Health Inf Res. 2023;29(3):280–2.

    Article Google Scholar

  • Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical education. 2022;22(1):772.

    Article Google Scholar

  • The shortage of radiologists. and the potential of AI [https://www.aidoc.com/blog/is-radiologist-shortage-real/

  • Ooi SKG, Makmur A, Soon AYQ, Fook-Chong S, Liew C, Sia SY, Ting YH, Lim CY. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singap Med J. 2021;62(3):126–34.

    Article 

    Google Scholar 

  • Wood EA, Ange BL, Miller DD. Are we ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: students and Faculty Survey. J Med Educ Curric Dev. 2021;8:23821205211024078.

    Article 

    Google Scholar 

  • Process of Translation and Adaptation of Instruments. [http://wjp.int/substance_abuse/research_tools/translation/en/

  • Amiri H, Peiravi S, rezazadeh shojaee Ss, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ShojaeiBaghini M, Musaie F, Anvari MH, Asadi Anar M. Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ. 2024;24(1):412.

    Article 

    Google Scholar 

  • Hassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a Specialty in the era of Artificial Intelligence: a systematic review and Meta-analysis on medical students, Radiology trainees, and radiologists. Acad Radiol. 2024;31(1):306–21.

    Article 

    Google Scholar 

  • Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18.

    Article 

    Google Scholar 

  • Mondal H, Marndi G, Behera JK, Mondal S. ChatGPT for teachers: practical examples for utilizing Artificial Intelligence for Educational purposes. Indian J Vascular Endovascular Surg. 2023;10(3):200–5.

    Article 

    Google Scholar 

  • Tsang R. Practical applications of ChatGPT in Undergraduate Medical Education. J Med Educ Curric Dev. 2023;10:23821205231178449.

    Article 

    Google Scholar 

  • McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence? Npj Digit Med. 2020;3(1):86.

    Article 

    Google Scholar 

  • Lee J, Wu AS, Li D, Kulasegaram KM. Artificial Intelligence in Undergraduate Medical Education: a scoping review. Acad Med. 2021;96(11s):S62–70.

    Article 

    Google Scholar 

  • Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing Artificial Intelligence Training in Medical Education. JMIR Med Educ. 2019;5(2):e16048.

    Article 

    Google Scholar 

  • Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. Npj Digit Med. 2020;3(1):126.

    Article 

    Google Scholar 

  • Langlotz CP. Will Artificial Intelligence Replace radiologists? Radiology: Artif Intell. 2019;1(3):e190058.

    Google Scholar 

  • Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, Ong HY, Jagmohan P, Tan WC, et al. Improved Productivity using deep learning–assisted reporting for lumbar spine MRI. Radiology. 2022;305(1):160–6.

    Article 

    Google Scholar 

  • Rangarajan K, Muku S, Garg AK, Gabra P, Shankar SH, Nischal N, Soni KD, Bhalla AS, Mohan A, Tiwari P, et al. Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19. Eur Radiol. 2021;31(8):6039–48.

    Article 

    Google Scholar 

  • Clinical radiology UK workforce. census 2019 report [https://www.rcr.ac.uk/publication/clinical-radiology-uk-workforce-census-2019-report

  • Radiology Facing a Global Shortage. Specialty affected by COVID-19, aging population and demand for imaging [https://www.rsna.org/news/2022/may/global-radiologist-shortage

  • Morton SM, Bandara DK, Robinson EM, Carr PE. In the 21st Century, what is an acceptable response rate? Aust N Z J Public Health. 2012;36(2):106–8.

    Article 

    Google Scholar 

  • Alkhaaldi SMI, Kassab CH, Dimassi Z, Oyoun Alsoud L, Al Fahim M, Al Hageh C, Ibrahim H. Medical student experiences and perceptions of ChatGPT and Artificial Intelligence: cross-sectional study. JMIR Med Educ. 2023;9:e51302.

    Article 

    Google Scholar 

  • Bharatha A, Ojeh N, Fazle Rabbi AM, Campbell MH, Krishnamurthy K, Layne-Yarde RNA, Kumar A, Springer DCR, Connell KL, Majumder MAA. Comparing the performance of ChatGPT-4 and medical students on MCQs at varied levels of Bloom’s taxonomy. Adv Med Educ Pract. 2024;15(null):393–400.

    Article 

    Google Scholar 



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