Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation (Camb). 2021;2(4): 100179. https://doi.org/10.1016/j.xinn.2021.100179.
Mariani MM, Perez-Vega R, Wirtz J. AI in marketing, consumer research and psychology: a systematic literature review and research agenda. Psychol Mark. 2022;39(4):755–76. https://doi.org/10.1002/mar.21619.
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artif Intell Healthcare. 2020. https://doi.org/10.1016/B978-0-12-818438-7.00002-2.
Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020;111(5):1452–60. https://doi.org/10.1111/cas.14377.
Article CAS PubMed PubMed Central Google Scholar
Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021;41(11):1100–15. https://doi.org/10.1002/cac2.12215.
Hinton G. Deep learning-a technology with the potential to transform health care. JAMA J Am Med Assoc. 2018;320(11):1101–2. https://doi.org/10.1001/jama.2018.11100.
Yu H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–31. https://doi.org/10.1038/s41551-018-0305-z.
Blease C, Kaptchuk TJ, Bernstein MH, Mandl KD, Halamka JD, Desroches CM. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. J Med Internet Res. 2019. https://doi.org/10.2196/12802.
Article PubMed PubMed Central Google Scholar
Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res. 2019. https://doi.org/10.2196/12422.
Article PubMed PubMed Central Google Scholar
Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: ınsights from a global physician survey. Artif Intell Med. 2020. https://doi.org/10.1016/j.artmed.2019.101753.
van Hoek J, et al. A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol. 2019. https://doi.org/10.1016/j.ejrad.2019.108742.
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. https://doi.org/10.1186/s13244-019-0798-3.
Sarwar S, Dent A, Faust K, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. npj Digit Med. 2019;2:28. https://doi.org/10.1038/s41746-019-0106-0.
Article PubMed PubMed Central Google Scholar
Ara Shaikh A, Kumar A, Jani K, Mitra S, García-Tadeo DA, Devarajan A. The role of machine learning and artificial ıntelligence for making a digital classroom and its sustainable ımpact on education during Covid-19. Mater Today Proc. 2022;56:3211–5. https://doi.org/10.1016/j.matpr.2021.09.368.
Article CAS PubMed Google Scholar
Pecqueux M, et al. The use and future perspective of Artificial Intelligence—a survey among German surgeons. Front Public Health. 2022. https://doi.org/10.3389/fpubh.2022.982335.
Article PubMed PubMed Central Google Scholar
Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11(1):5193. https://doi.org/10.1038/s41598-021-84698-5.
Article CAS PubMed PubMed Central Google Scholar
O’Shaughnessey J, Collins ML. Radiation therapist perceptions on how artificial intelligence may affect their role and practice. J Med Radiat Sci. 2023;70(S2):6–14. https://doi.org/10.1002/jmrs.638.
Frank MR, et al. Toward understanding the impact of artificial intelligence on labor. Proc Natl Acad Sci USA. 2019;116(14):6531–9. https://doi.org/10.1073/pnas.1900949116.
Article CAS PubMed PubMed Central Google Scholar
Zheng B, Wu M-n, Zhu S-j, et al. Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey. BMC Health Serv Res. 2021;21:1067. https://doi.org/10.1186/s12913-021-07044-5.
Article PubMed PubMed Central Google Scholar
Gong B, et al. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol. 2019;26(4):566–77. https://doi.org/10.1016/j.acra.2018.10.007.
Collado-Mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol. 2018;15(12):1753–7. https://doi.org/10.1016/j.jacr.2017.12.021.
Kust D, Murgic J, Vukovic P, Kruljac I, Prpic M, Zilic A, et al. Oncologist burnout syndrome in eastern Europe: results of the multinational survey. JCO Oncol Pract. 2020;16(4):e366–76. https://doi.org/10.1200/JOP.19.00470.
Coiera E. The fate of medicine in the time of AI. Lancet. 2018;392(10162):2331–2. https://doi.org/10.1016/S0140-6736(18)31925-1.
Kohane IS, Drazen JM, Campion EW. A glimpse of the next 100 years in medicine. N Engl J Med. 2012;367(26):2538–9. https://doi.org/10.1056/nejme1213371.
Article CAS PubMed Google Scholar
Beam AL, Kohane IS. Translating artificial intelligence into clinical care. JAMA J Am Med Assoc. 2016;316(22):2368–9. https://doi.org/10.1001/jama.2016.17217.
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