Brandes GIG, D’Ippolito G, Azzolini AG, Meirelles G. Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiol Bras. 2020;53(3):167–70. https://doi.org/10.1590/0100-3984.2019.0101.
Article PubMed PubMed Central Google Scholar
Gampala S, Vankeshwaram V, Gadula SSP. Is artificial intelligence the new friend for radiologists? A review article. Cureus. 2020;12(10): e11137. https://doi.org/10.7759/cureus.11137.
Article PubMed PubMed Central Google Scholar
Yang L, Ene IC, ArabiBelaghi R, Koff D, Stein N, Santaguida PL. Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review. EurRadiol. 2022;32(3):1477–95. https://doi.org/10.1007/s00330-021-08214-z.
Rodrigues JA, Krois J, Schwendicke F. Demystifying artificial intelligence and deep learning in dentistry. Braz Oral Res. 2021;35: e094. https://doi.org/10.1590/1807-3107bor-2021.vol35.0094.
Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. DentomaxillofacRadiol. 2020;49(1):20190107. https://doi.org/10.1259/dmfr.20190107.
Heo MS, Kim JE, Hwang JJ, Han SS, Kim JS, Yi WJ, Park IW. Artificial intelligence in oral and maxillofacial radiology: what is currently possible? Dentomaxillofac Radiol. 2021;50(3):20200375. https://doi.org/10.1259/dmfr.20200375.
Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. DentomaxillofacRadiol. 2021;50(5):20200461. https://doi.org/10.1259/dmfr.20200461.
Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85(1):60–8. https://doi.org/10.1002/jdd.12385.
Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto de Santos D, Coppola F, Morozov S, Zins M, Bohyn C, Koç U, Wu J, Veean S, Fleischmann D, Leiner T, Willemink MJ. An international survey on AI in radiology in 1041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. EurRadiol. 2021;31(9):7058–66. https://doi.org/10.1007/s00330-021-07781-5.
Grayev A. Artificial Intelligence in radiology: resident recruitment help or hindrance? Acad Radial. 2019;26(5):699–700. https://doi.org/10.1016/j.acra.2019.01.005.
Pinto Dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. EurRadiol. 2019;29(4):1640–6. https://doi.org/10.1007/s00330-018-5601-1.
Coppola F, Faggioni L, Regge D, et al. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med. 2021;126(1):63–71. https://doi.org/10.1007/s11547-020-01205-y.
Tajmir SH, Alkasab TK. Toward augmented radiologists: changes in radiology education in the era of machine learning and artificial intelligence. AcadRadiol. 2018;25(6):747–50. https://doi.org/10.1016/j.acra.2018.03.007.
Rubin DL. Artificial intelligence in imaging: the radiologist’s role. J Am Coll Radiol. 2019;16:1309–17. https://doi.org/10.1016/j.jacr.2019.05.036.
Article PubMed PubMed Central Google Scholar
Bin Dahmash A, Alabdulkareem M, Alfutais A, Kamel AM, Alkholaiwi F, Alshehri S, Al Zahrani Y, Almoaiqel M. Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career? BJR Open. 2020;2(1):20200037. https://doi.org/10.1259/bjro.20200037.
Article PubMed PubMed Central Google Scholar
Lennartz S, Dratsch T, Zopfs D, Persigehl T, Maintz D, GroßeHokamp N, Pinto Dos Santos D. Use and control of artificial intelligence in patients across the medical workflow: single-center questionnaire study of patient perspectives. J Med Internet Res. 2021;23(2):e24221. https://doi.org/10.2196/24221.
Article PubMed PubMed Central Google Scholar
Jungmann F, Jorg T, Hahn F, Pinto Dos Santos D, Jungmann SM, Düber C, Mildenberger P, Kloeckner R. Attitudes toward artificial intelligence among radiologists, it specialists, and industry. AcadRadiol. 2021;28(6):834–40. https://doi.org/10.1016/j.acra.2020.04.011.
Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91. https://doi.org/10.3758/bf03193146.
Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates; 1988.
van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H, Heverhagen J, Poellinger A. 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.
Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):14. https://doi.org/10.1186/s13244-019-0830-7.
Article PubMed PubMed Central Google Scholar
Botwe BO, Antwi WK, Arkoh S, Akudjedu TN. Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: the Ghana study. J Med Radiat Sci. 2021;68(3):260–8. https://doi.org/10.1002/jmrs.460.
Article PubMed PubMed Central Google Scholar
Gong B, Nugent JP, Guest W, et al. Influence of artificial intelligence on canadian medical students’ preference for radiology specialty: a national survey study. AcadRadiol. 2019;26(4):566–77. https://doi.org/10.1016/j.acra.2018.10.007.
Park CJ, Yi PH, Siegel EL. Medical student perspectives on the impact of artificial intelligence on the practice of medicine. CurrProblDiagn Radiol. 2021;50(5):614–9. https://doi.org/10.1067/j.cpradiol.2020.06.011.
Hermida PM, Araújo IE. Elaboration and validation of the nursing interview. RevBrasEnferm. 2006;59(3):314–20. https://doi.org/10.1590/s0034-71672006000300012.
da Costa ED, Pinelli C, da Silva Tagliaferro EP, Corrente JE, Ambrosano GMB. Development and validation of a questionnaire to evaluate infection control in oral radiology. DentomaxillofacRadiol. 2017;46(4):20160338. https://doi.org/10.1259/dmfr.20160338.
Rattray J, Jones MC. Essential elements of questionnaire design and development. J Clin Nurs. 2007;16(2):234–43. https://doi.org/10.1111/j.1365-2702.2006.01573.x.
Chambers S, Humphris G, Freeman R. The parental dental concerns scale (PDCS): its development and initial psychometric properties. Community Dent Oral Epidemiol. 2013;41(6):541–50. https://doi.org/10.1111/cdoe.12046.
Eltorai AEM, Bratt AK, Guo HH. Thoracic radiologists’ versus computer scientists’ perspectives on the future of artificial intelligence in radiology. J Thorac Imaging. 2020;35(4):255–9. https://doi.org/10.1097/RTI.0000000000000453.
Kim CS, Samaniego CS, Sousa Melo SL, Brachvogel WA, Baskaran K, Rulli D. Artificial intelligence (A.I.) in dental curricula: ethics and responsible integration. J Dent Educ. 2023;87(11):1570–3. https://doi.org/10.1002/jdd.13337.
Jeong H, Han SS, Kim KE, Park IS, Choi Y, Jeon KJ. Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: an online survey. J Dent Educ. 2023;87(6):804–12. https://doi.org/10.1002/jdd.13189.
Gorospe-Sarasúa L, Muñoz-Olmedo JM, Sendra-Portero F, de Luis-García R. Challenges of radiology education in the era of artificial intelligence. Radiologia (Engl Ed). 2022;64(1):54–9. https://doi.org/10.1016/j.rxeng.2020.10.012.
Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto de Santos D, Coppola F, Morozov S, Zins M, Bohyn C, Koç U, Wu J, Veean S, Fleischmann D, Leiner T, Willemink MJ. An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation and education. EurRadiol. 2021;31(11):8797–806.
Comments (0)