6.
Civaner M, Uncu Y, Bulut F, Giounous Chalil E, Tatli A
. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022; 22(1):772.
PMC: 9646274.
DOI: 10.1186/s12909-022-03852-3.
View
7.
Bing D, Ying J, Miao J, Lan L, Wang D, Zhao L
. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol. 2018; 43(3):868-874.
DOI: 10.1111/coa.13068.
View
8.
Valikodath N, Al-Khaled T, Cole E, Ting D, Tu E, Campbell J
. Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology. J AAPOS. 2021; 25(3):164.e1-164.e5.
PMC: 8328946.
DOI: 10.1016/j.jaapos.2021.01.011.
View
9.
Formeister E, Baum R, Knott P, Seth R, Ha P, Ryan W
. Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer. Laryngoscope. 2020; 130(12):E843-E849.
DOI: 10.1002/lary.28508.
View
10.
Lau K, Wilkinson J, Moorthy R
. A web-based prediction score for head and neck cancer referrals. Clin Otolaryngol. 2018; 43(4):1043-1049.
DOI: 10.1111/coa.13098.
View
11.
Wartman S, Combs C
. Medical Education Must Move From the Information Age to the Age of Artificial Intelligence. Acad Med. 2017; 93(8):1107-1109.
DOI: 10.1097/ACM.0000000000002044.
View
12.
Crowson M, Ranisau J, Eskander A, Babier A, Xu B, Kahmke R
. A contemporary review of machine learning in otolaryngology-head and neck surgery. Laryngoscope. 2019; 130(1):45-51.
DOI: 10.1002/lary.27850.
View
13.
Wu Y, Ho H, Hsiao S, Brummet R, Chipara O
. Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL). Int J Audiol. 2016; 55(5):285-94.
PMC: 4823154.
DOI: 10.3109/14992027.2015.1120892.
View
14.
Nam Y, Choo O, Lee Y, Choung Y, Shin H
. Cascade recurring deep networks for audible range prediction. BMC Med Inform Decis Mak. 2017; 17(Suppl 1):56.
PMC: 5444043.
DOI: 10.1186/s12911-017-0452-2.
View
15.
Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer H, Janda M
. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021; 11(1):5193.
PMC: 7933437.
DOI: 10.1038/s41598-021-84698-5.
View
16.
Pfeifer C
. A progressive three-phase innovation to medical education in the United States. Med Educ Online. 2018; 23(1):1427988.
PMC: 5795774.
DOI: 10.1080/10872981.2018.1427988.
View
17.
Webster C
. Artificial intelligence and the adoption of new technology in medical education. Med Educ. 2020; 55(1):6-7.
DOI: 10.1111/medu.14409.
View
18.
Bramhall N, McMillan G, Kujawa S, Konrad-Martin D
. Use of non-invasive measures to predict cochlear synapse counts. Hear Res. 2018; 370:113-119.
PMC: 7161203.
DOI: 10.1016/j.heares.2018.10.006.
View
19.
Nasca T, Philibert I, Brigham T, Flynn T
. The next GME accreditation system--rationale and benefits. N Engl J Med. 2012; 366(11):1051-6.
DOI: 10.1056/NEJMsr1200117.
View
20.
Altuwaijri M, Bahanshal A, Almehaid M
. Implementation of computerized physician order entry in National Guard Hospitals: assessment of critical success factors. J Family Community Med. 2011; 18(3):143-51.
PMC: 3237203.
DOI: 10.4103/2230-8229.90014.
View