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Erschienen in: World Journal of Pediatrics 2/2019

17.04.2019 | Editorial

Application of artificial intelligence in pediatrics: past, present and future

verfasst von: Li-Qi Shu, Yi-Kan Sun, Lin-Hua Tan, Qiang Shu, Anthony C. Chang

Erschienen in: World Journal of Pediatrics | Ausgabe 2/2019

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Excerpt

Artificial intelligence (AI) is a very active computer science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including medicine. Therefore, it is no surprise that innovation plays an important role in delivering better medical and health. …
Literatur
1.
Zurück zum Zitat Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8:303–20.CrossRefPubMed Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8:303–20.CrossRefPubMed
2.
Zurück zum Zitat Szolovits P. Artificial intelligence in medicine. Boulder: Westview Press Inc; 1982. Szolovits P. Artificial intelligence in medicine. Boulder: Westview Press Inc; 1982.
4.
Zurück zum Zitat Hanson CW 3rd, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med. 2001;29:427–35.CrossRefPubMed Hanson CW 3rd, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med. 2001;29:427–35.CrossRefPubMed
5.
Zurück zum Zitat Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform. 2016;Suppl 1:S103–16.PubMed Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform. 2016;Suppl 1:S103–16.PubMed
6.
Zurück zum Zitat Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR. Real alerts and artifact classification in archives multi-signal vital sign monitoring data: implication for mining big data. J Clin Monit Comput. 2016;30:875–88.CrossRefPubMed Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR. Real alerts and artifact classification in archives multi-signal vital sign monitoring data: implication for mining big data. J Clin Monit Comput. 2016;30:875–88.CrossRefPubMed
7.
Zurück zum Zitat Goel VV, Poole SF, Longhurst CA, Platchek TS, Pageler NM, Sharek PJ, et al. Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children. J Hosp Med. 2016;11:817–23.CrossRefPubMed Goel VV, Poole SF, Longhurst CA, Platchek TS, Pageler NM, Sharek PJ, et al. Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children. J Hosp Med. 2016;11:817–23.CrossRefPubMed
8.
Zurück zum Zitat Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035.CrossRefPubMedPubMedCentral Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035.CrossRefPubMedPubMedCentral
9.
Zurück zum Zitat Cascianelli S, Scialpi M, Amici S, Forini N, Minestrini M, Fravolini ML, et al. Role of artificial intelligence techniques (Automatic Classifiers) in molecular imaging modalities in neurodegenerative disease. Curr Alzheimer Res. 2017;14:198–207.CrossRefPubMed Cascianelli S, Scialpi M, Amici S, Forini N, Minestrini M, Fravolini ML, et al. Role of artificial intelligence techniques (Automatic Classifiers) in molecular imaging modalities in neurodegenerative disease. Curr Alzheimer Res. 2017;14:198–207.CrossRefPubMed
10.
Zurück zum Zitat Levy S, Duda M, Haber N, Wall DP. Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism. Mol Autism. 2017;8:65.CrossRefPubMedPubMedCentral Levy S, Duda M, Haber N, Wall DP. Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism. Mol Autism. 2017;8:65.CrossRefPubMedPubMedCentral
11.
Zurück zum Zitat Jalali A, Simpao AF, Gálvez JA, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia in neonates after cardiac surgery using machine learning algorithms. J Med Syst. 2018;42:177.CrossRefPubMed Jalali A, Simpao AF, Gálvez JA, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia in neonates after cardiac surgery using machine learning algorithms. J Med Syst. 2018;42:177.CrossRefPubMed
12.
Zurück zum Zitat Carass A, Cuzzocreo JL, Han S, Hernandez-Castillo CR, Rasser PE, Ganz M, et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage. 2018;183:150–72.CrossRefPubMed Carass A, Cuzzocreo JL, Han S, Hernandez-Castillo CR, Rasser PE, Ganz M, et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage. 2018;183:150–72.CrossRefPubMed
13.
14.
Zurück zum Zitat Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287:313–22.CrossRefPubMed Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287:313–22.CrossRefPubMed
15.
Zurück zum Zitat Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10.CrossRefPubMed Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10.CrossRefPubMed
16.
Zurück zum Zitat Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T. Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med. 2014;62:105–18.CrossRefPubMed Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T. Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med. 2014;62:105–18.CrossRefPubMed
17.
Zurück zum Zitat Farley T, Kiefer J, Lee P, Von Hoff D, Trent JM, Colbourn C, et al. The BioIntelligence Framework: a new computational platform for biomedical knowledge computing. J Am Med Inform Assoc. 2013;20:128–33.CrossRefPubMedPubMedCentral Farley T, Kiefer J, Lee P, Von Hoff D, Trent JM, Colbourn C, et al. The BioIntelligence Framework: a new computational platform for biomedical knowledge computing. J Am Med Inform Assoc. 2013;20:128–33.CrossRefPubMedPubMedCentral
18.
Zurück zum Zitat Su H, Shen Y, Xing F, Qi X, Hirshfield KM, Yang L, et al. Robust automatic breast cancer staging using a combination of functional genomics and image-omics. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7226–9.PubMedCentral Su H, Shen Y, Xing F, Qi X, Hirshfield KM, Yang L, et al. Robust automatic breast cancer staging using a combination of functional genomics and image-omics. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7226–9.PubMedCentral
20.
Zurück zum Zitat Griebel L, Prokosch HU, Kopcke F, Toddenroth D, Christoph J, Leb I, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak. 2015;15:17.CrossRefPubMedPubMedCentral Griebel L, Prokosch HU, Kopcke F, Toddenroth D, Christoph J, Leb I, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak. 2015;15:17.CrossRefPubMedPubMedCentral
21.
Zurück zum Zitat Tang H, Jiang X, Wang X, Wang S, Sofia H, Fox D, et al. Protecting genomic data analytics in the cloud: state of the art and opportunities. BMC Med Genom. 2016;9:63.CrossRef Tang H, Jiang X, Wang X, Wang S, Sofia H, Fox D, et al. Protecting genomic data analytics in the cloud: state of the art and opportunities. BMC Med Genom. 2016;9:63.CrossRef
22.
Zurück zum Zitat Shatil AS, Younas S, Pourreza H, Figley CR. Heads in the cloud: a primer on neuroimaging applications of the high performance computing. Magn Reson Insights. 2016;8(Suppl 1):69–80.PubMedPubMedCentral Shatil AS, Younas S, Pourreza H, Figley CR. Heads in the cloud: a primer on neuroimaging applications of the high performance computing. Magn Reson Insights. 2016;8(Suppl 1):69–80.PubMedPubMedCentral
23.
Zurück zum Zitat Steinhubl SR, Topol EJ. Moving from digitalization to digitization in cardiovascular care: why is it important, and what could it mean for patients and providers? J Am Coll Cardiol. 2015;66:1489–96.CrossRefPubMedPubMedCentral Steinhubl SR, Topol EJ. Moving from digitalization to digitization in cardiovascular care: why is it important, and what could it mean for patients and providers? J Am Coll Cardiol. 2015;66:1489–96.CrossRefPubMedPubMedCentral
24.
Zurück zum Zitat Kubota KJ, Chen JA, Little MA. Machine learning for large-scale wearable sensor data in Parkinson’s disease: concepts, promises, pitfalls, and features. Mov Disord. 2016;31:1314–26.CrossRefPubMed Kubota KJ, Chen JA, Little MA. Machine learning for large-scale wearable sensor data in Parkinson’s disease: concepts, promises, pitfalls, and features. Mov Disord. 2016;31:1314–26.CrossRefPubMed
25.
Zurück zum Zitat Russell S, Hauert S, Altman R, Veloso M. Robotics: ethics of artificial intelligence. Nature. 2015;521:415–8.CrossRefPubMed Russell S, Hauert S, Altman R, Veloso M. Robotics: ethics of artificial intelligence. Nature. 2015;521:415–8.CrossRefPubMed
26.
Zurück zum Zitat Costescu CA, Vanderborght B, David DO. Reversal learning task in children with autism spectrum disorder: a robot-based approach. J Autism Dev Disord. 2015;45:3715–25.CrossRefPubMed Costescu CA, Vanderborght B, David DO. Reversal learning task in children with autism spectrum disorder: a robot-based approach. J Autism Dev Disord. 2015;45:3715–25.CrossRefPubMed
Metadaten
Titel
Application of artificial intelligence in pediatrics: past, present and future
verfasst von
Li-Qi Shu
Yi-Kan Sun
Lin-Hua Tan
Qiang Shu
Anthony C. Chang
Publikationsdatum
17.04.2019
Verlag
Childrens Hospital, Zhejiang University School of Medicine
Erschienen in
World Journal of Pediatrics / Ausgabe 2/2019
Print ISSN: 1708-8569
Elektronische ISSN: 1867-0687
DOI
https://doi.org/10.1007/s12519-019-00255-1

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