Rationale and Objectives
Materials and Methods
Results
Conclusion
KEY WORDS
Abbreviations:
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Subscribe to Academic RadiologyREFERENCES
- Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists.PLoS Med. 2018; 15e1002686https://doi.org/10.1371/journal.pmed.1002686
- Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet.PLoS Med. 2018; 15e1002699https://doi.org/10.1371/journal.pmed.1002699
- Deep learning based on MR imaging for predicting outcome of uterine fibroid embolization.J Vasc Interv Radiol. 2020; 31 (e3): 1010-1017https://doi.org/10.1016/j.jvir.2019.11.032
- Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department.Radiol Artif Intell. 2021; 3e200098https://doi.org/10.1148/ryai.2020200098
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?.Skeletal Radiol. 2021; https://doi.org/10.1007/s00256-021-03876-8
- Artificial intelligence may cause a significant disruption to the radiology workforce.J Am Coll Radiol. 2019; 16: 1077-1082https://doi.org/10.1016/j.jacr.2019.01.026
- The end of radiology? Three threats to the future practice of radiology.J Am Coll Radiol. 2016; 13 (Pt A): 1415-1420https://doi.org/10.1016/j.jacr.2016.07.010
- Medical student perspectives on the impact of artificial intelligence on the practice of medicine.Curr Probl Diagn Radiol. 2021; 50: 614-619https://doi.org/10.1067/j.cpradiol.2020.06.011
- 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; 121108742
- Medical students’ attitude towards artificial intelligence: a multicentre survey.Eur Radiol. 2019; 29: 1640-1646https://doi.org/10.1007/s00330-018-5601-1
- Influence of artificial intelligence on canadian medical students’ preference for radiology specialty: a National survey study.Acad Radiol. 2019; 26: 566-577https://doi.org/10.1016/j.acra.2018.10.007
- The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program.J Am Coll Radiol. 2018; 15: 1753-1757https://doi.org/10.1016/j.jacr.2017.12.021
- Resident physicians’ perceptions of diagnostic radiology and the declining interest in the specialty.Acad Radiol. 2021; 28: 261-270https://doi.org/10.1016/j.acra.2020.01.016
- Attitudes toward artificial intelligence among radiologists, IT specialists, and industry.Acad Radiol. 2021; 28: 834-840https://doi.org/10.1016/j.acra.2020.04.011
- An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.Eur Radiol. 2021; 31: 7058-7066https://doi.org/10.1007/s00330-021-07781-5
abstrackr: home. http://abstrackr.cebm.brown.edu/. Accessed November 4, 2021.
- A qualitative analysis of the needs and experiences of hospital-based clinicians when accessing medical imaging.J Digit Imaging. 2021; 34: 385-396https://doi.org/10.1007/s10278-021-00446-1
- Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field.J Health Serv Res Policy. 2005; 10 (Suppl): 6-20https://doi.org/10.1258/1355819054308576
- Impact of the rise of artificial intelligence in radiology: what do radiologists think?.Diagn Interv Imaging. 2019; 100: 327-336https://doi.org/10.1016/j.diii.2019.03.015
- Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey.Insights Imaging. 2020; 11: 14https://doi.org/10.1186/s13244-019-0830-7
- The importance of introducing artificial intelligence to the medical curriculum - assessing practitioners’ perspectives.Croat Med J. 2020; 61: 457-464
- An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education.Eur Radiol. 2021; https://doi.org/10.1007/s00330-021-07782-4
- Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?.Insights Imaging. 2020; 11: 127https://doi.org/10.1186/s13244-020-00942-y
Brandes GIG, D'Ippolito G, Azzolini AG, et al. Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiol Bras. 2020;53:167–170. doi: 10.1590/0100-3984.2019.0101.
- Thoracic Radiologists’ Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology.J Thorac Imaging. 2020; 35: 255-259https://doi.org/10.1097/RTI.0000000000000453
- Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.Singapore Med J. 2021; 62: 126-134https://doi.org/10.11622/smedj.2019141
- A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.Sci Rep. 2021; 11: 5193https://doi.org/10.1038/s41598-021-84698-5
- Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?.BJR Open. 2020; 220200037https://doi.org/10.1259/bjro.20200037
- Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey.Radiol Med. 2021; 126: 63-71https://doi.org/10.1007/s11547-020-01205-y
- Automated critical test findings identification and online notification system using artificial intelligence in imaging.Radiology. 2017; 285: 923-931https://doi.org/10.1148/radiol.2017162664
- Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks.Radiology. 2017; 284: 574-582https://doi.org/10.1148/radiol.2017162326
- The RSNA pediatric bone age machine learning challenge.Radiology. 2019; 290: 498-503https://doi.org/10.1148/radiol.2018180736
- Artificial intelligence in radiology: resident recruitment help or hindrance?.Acad. Radiol. 2019; : 699-700https://doi.org/10.1016/j.acra.2019.01.005
- Artificial intelligence and the trainee experience in radiology.J Am Coll Radiol. 2020; 17: 1388-1393https://doi.org/10.1016/j.jacr.2020.09.028
- AI in Radiology: Medical Students’ Perspective. 2021; https://www.acr.org/Advocacy-and-Economics/Voice-of-Radiology-Blog/2021/07/22/AI-in-Radiology-Medical-Students-PerspectiveDate accessed: November 21, 2021
- Sin JM. AI-RADS: an artificial intelligence curriculum for residents.Acad Radiol. 2020; https://doi.org/10.1016/j.acra.2020.09.017
- A conference-friendly, hands-on introduction to deep learning for radiology trainees.J Digit Imaging. 2021; 34: 1026-1033https://doi.org/10.1007/s10278-021-00492-9
- Preparing radiologists to lead in the era of artificial intelligence: designing and implementing a focused data science pathway for senior radiology residents.Radiol Artif Intell. 2020; 2e200057https://doi.org/10.1148/ryai.2020200057
- RSNA Imaging AI Certificate. 2021; https://www.rsna.org/ai-certificateDate accessed: November 21, 2021
- Artificial intelligence: threat or boon to radiologists?.J Am Coll Radiol. 2017; 14: 1476-1480https://doi.org/10.1016/j.jacr.2017.07.007
- Artificial intelligence and deep learning – radiology's next frontier?.Clinical Imaging. 2018; : 87-88https://doi.org/10.1016/j.clinimag.2017.11.007
- Current applications and future impact of machine learning in radiology.Radiology. 2018; 288: 318-328https://doi.org/10.1148/radiol.2018171820
- Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology.J Am Coll Radiol. 2019; 16: 1239-1247https://doi.org/10.1016/j.jacr.2019.05.047
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.Eur Radiol Exp. 2018; 2: 35https://doi.org/10.1186/s41747-018-0061-6
- Artificial intelligence: who is responsible for the diagnosis?.Radiol Med. 2020; 125: 517-521https://doi.org/10.1007/s11547-020-01135-9
- The present and future of deep learning in radiology.Eur J Radiol. 2019; 114: 14-24https://doi.org/10.1016/j.ejrad.2019.02.038