DayanI, RothHR, ZhongA, et al.Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med, 2021; 27(10):1735–1743; doi: 10.1038/s41591-021-01506-3
5.
NinoG, LinguraruMG. Developing artificial intelligence technology for pediatric pulmonology: Lessons from COVID-19. Pediatr Pulmonol, 2022; 57(7):1588–1589; doi: 10.1002/ppul.25901
6.
MorcosG, YiPH, JeudyJ. Applying artificial intelligence to pediatric chest imaging: Reliability of leveraging adult-based artificial intelligence models. J Am Coll Radiol, 2023; 20(8):742–747; doi: 10.1016/j.jacr.2023.07.004
7.
SammerMBK, AkbariYS, BarthRA, et al.Use of artificial intelligence in radiology: Impact on pediatric patients, a White Paper from the ACR Pediatric AI Workgroup. J Am Coll Radiol, 2023; 20(8):730–737; doi: 10.1016/j.jacr.2023.06.003
8.
JiangF, JiangY, ZhiH, et al.Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol, 2017; 2(4):230–243; doi: 10.1136/svn-2017-000101
9.
LiangH, TsuiBY, NiH, et al.Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med, 2019; 25(3):433–438; doi: 10.1038/s41591-018-0335-9
10.
MacMathD, ChenM, KhouryP. Artificial intelligence: Exploring the future of innovation in allergy immunology. Curr Allergy Asthma Rep, 2023; 23(6):351–362; doi: 10.1007/s11882-023-01084-z
11.
JoumaaH, SigogneR, MaravicM. Artificial intelligence to differentiate asthma from COPD in medico-administrative databases. BMC Pulm Med, 2022; 22(1):357; doi: 10.1186/s12890-022-02144-2
12.
MessingerAI, BuiN, WagnerBD, et al.Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma. Pediatr Pulmonol, 2019; 54(8):1149–1155; doi: 10.1002/ppul.24342
13.
GoldsteinCA, BerryRB, KentDT, et al.Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement. J Clin Sleep Med, 2020; 16(4):605–607; doi: 10.5664/jcsm.8288
14.
DournesG, HallCS, WillmeringMM, et al.Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators. Eur Respir J, 2022; 59(3):2100844; doi: 10.1183/13993003.00844-2021
15.
BarileJ, MargolisA, CasonG, et al.Diagnostic accuracy of a large language model in pediatric case studies. JAMA Pediatr; doi:10.1001/jamapediatrics.2023.5750
16.
HaugCJ, DrazenJM. Artificial intelligence and machine learning in clinical medicine. N Engl J Med, 2023; 388(13):1201–1208; doi: 10.1056/NEJMra2302038