286 related articles for article (PubMed ID: 35753935)
1. Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why?
Velez-Florez MC; Ghosh A; Patton D; Sze R; Reid JR; Sotardi S
Acad Radiol; 2023 Feb; 30(2):349-358. PubMed ID: 35753935
[TBL] [Abstract][Full Text] [Related]
2. Job-Readiness After Pediatric Neuroradiology Training: Defining Trainee Needs.
Gokli A; Shekdar KV; Reid JR
Acad Radiol; 2021 Dec; 28(12):1792-1798. PubMed ID: 33618941
[TBL] [Abstract][Full Text] [Related]
3. Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States.
Salastekar NV; Maxfield C; Hanna TN; Krupinski EA; Heitkamp D; Grimm LJ
Acad Radiol; 2023 Jul; 30(7):1481-1487. PubMed ID: 36710101
[TBL] [Abstract][Full Text] [Related]
4. Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development.
Alkhulaifat D; Rafful P; Khalkhali V; Welsh M; Sotardi ST
J Am Coll Radiol; 2023 Aug; 20(8):724-729. PubMed ID: 37352995
[TBL] [Abstract][Full Text] [Related]
5. A needs assessment of musculoskeletal fellowship training: a survey of practicing musculoskeletal radiologists.
Yablon CM; Wu JS; Newman LR; Downie BK; Hochman MG; Eisenberg RL
AJR Am J Roentgenol; 2013 Apr; 200(4):732-40. PubMed ID: 23521440
[TBL] [Abstract][Full Text] [Related]
6. Artificial intelligence-based decision support system (AI-DSS) implementation in radiology residency: Introducing residents to AI in the clinical setting.
Shiang T; Garwood E; Debenedectis CM
Clin Imaging; 2022 Dec; 92():32-37. PubMed ID: 36183619
[TBL] [Abstract][Full Text] [Related]
7. A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist.
van Kooten MJ; Tan CO; Hofmeijer EIS; van Ooijen PMA; Noordzij W; Lamers MJ; Kwee TC; Vliegenthart R; Yakar D
Insights Imaging; 2024 Jan; 15(1):15. PubMed ID: 38228800
[TBL] [Abstract][Full Text] [Related]
8. AI-RADS: An Artificial Intelligence Curriculum for Residents.
Lindqwister AL; Hassanpour S; Lewis PJ; Sin JM
Acad Radiol; 2021 Dec; 28(12):1810-1816. PubMed ID: 33071185
[TBL] [Abstract][Full Text] [Related]
9. Systematic Review of Radiology Residency Artificial Intelligence Curricula: Preparing Future Radiologists for the Artificial Intelligence Era.
Garin SP; Zhang V; Jeudy J; Parekh VS; Yi PH
J Am Coll Radiol; 2023 Jun; 20(6):561-569. PubMed ID: 37127217
[TBL] [Abstract][Full Text] [Related]
10. Artificial intelligence for precision education in radiology.
Duong MT; Rauschecker AM; Rudie JD; Chen PH; Cook TS; Bryan RN; Mohan S
Br J Radiol; 2019 Nov; 92(1103):20190389. PubMed ID: 31322909
[TBL] [Abstract][Full Text] [Related]
11. Artificial intelligence in radiology: trainees want more.
Hashmi OU; Chan N; de Vries CF; Gangi A; Jehanli L; Lip G
Clin Radiol; 2023 Apr; 78(4):e336-e341. PubMed ID: 36746724
[TBL] [Abstract][Full Text] [Related]
12. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.
Ooi SKG; Makmur A; Soon AYQ; Fook-Chong S; Liew C; Sia SY; Ting YH; Lim CY
Singapore Med J; 2021 Mar; 62(3):126-134. PubMed ID: 31680181
[TBL] [Abstract][Full Text] [Related]
13. The state of emergency radiology fellowships in North America and the development of a standardized curriculum.
Chahine AH; Hanna TN; Myers L; Kumaravel M; Herr KD
Emerg Radiol; 2019 Feb; 26(1):53-58. PubMed ID: 30498926
[TBL] [Abstract][Full Text] [Related]
14. How and how well do pediatric radiology fellows learn ultrasound skills? A national survey.
Back SJ; Gurian MS; Reid JR; Darge K
Pediatr Radiol; 2014 Sep; 44(9):1058-64. PubMed ID: 24733295
[TBL] [Abstract][Full Text] [Related]
15. AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats.
Lindqwister AL; Hassanpour S; Levy J; Sin JM
Front Med Technol; 2022; 4():1007708. PubMed ID: 36688145
[TBL] [Abstract][Full Text] [Related]
16. A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.
Agrawal A; Khatri GD; Khurana B; Sodickson AD; Liang Y; Dreizin D
Emerg Radiol; 2023 Jun; 30(3):267-277. PubMed ID: 36913061
[TBL] [Abstract][Full Text] [Related]
17. Training opportunities of artificial intelligence (AI) in radiology: a systematic review.
Schuur F; Rezazade Mehrizi MH; Ranschaert E
Eur Radiol; 2021 Aug; 31(8):6021-6029. PubMed ID: 33587154
[TBL] [Abstract][Full Text] [Related]
18. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists.
Hassankhani A; Amoukhteh M; Valizadeh P; Jannatdoust P; Sabeghi P; Gholamrezanezhad A
Acad Radiol; 2024 Jan; 31(1):306-321. PubMed ID: 37349157
[TBL] [Abstract][Full Text] [Related]
19. Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.
Yang L; Ene IC; Arabi Belaghi R; Koff D; Stein N; Santaguida PL
Eur Radiol; 2022 Mar; 32(3):1477-1495. PubMed ID: 34545445
[TBL] [Abstract][Full Text] [Related]
20. Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology.
Santomartino SM; Yi PH
Acad Radiol; 2022 Nov; 29(11):1748-1756. PubMed ID: 35105524
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
