Download PDF
Appl Clin Inform 2025; 16(03): 477-487
DOI: 10.1055/a-2521-1508
Review Article

Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review

Swaminathan Kandaswamy
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
,
Lindsey A. Knake
2   Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, Iowa, United States
,
Adam C. Dziorny
3   Department of Pediatrics, University of Rochester, Rochester, New York, United States
,
Sean M. Hernandez
4   Primary Care, Miami Veteran's Affairs, Miami, Florida, United States
,
Allison B. McCoy
5   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Lauren M. Hess
6   Pediatric Hospital Medicine, Texas Children's Hospital, Houston, Texas, United States
7   Division of Pediatric Hospital Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
,
Evan Orenstein
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Mia S. White
9   Woodruff Health Sciences Center Library, Emory University, Atlanta, Georgia, United States
,
Eric S. Kirkendall
10   Department of Pediatrics, Wake Forest University School of Medicine, Center for Healthcare Innovation, Winston-Salem, North Carolina, United States
,
Matthew J. Molloy
11   Department of Pediatrics, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
,
Philip A. Hagedorn
11   Department of Pediatrics, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
,
Naveen Muthu
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Avinash Murugan
12   Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, United States
,
Jonathan M. Beus
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Mark Mai
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Brooke Luo
13   Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
14   Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States
,
Juan D. Chaparro
15   Division of Clinical Informatics, Department of Pediatrics, Nationwide Children's Hospital/Ohio State University College of Medicine, Columbus, Ohio, United States
› Author Affiliations
Funding None.
› Further Information
Also available at
   Permissions and Reprints

Abstract

Objective

To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.

Methods

We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE, and Web of Science with controlled vocabulary. Inclusion criteria: AI intervention in a pediatric clinical setting that learns from data (i.e., data-driven, as opposed to rule-based) and takes actions to make patient-specific recommendations; published between 01/2010 and 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in the background). We extracted study characteristics, target users, implementation setting, time span, and performance measures.

Results

Of 126 articles reviewed as full text, 17 met inclusion criteria. Eight studies (47%) reported both clinical outcomes and process measures, six (35%) reported only process measures and two (12%) reported only clinical outcomes. Five studies (30%) reported no difference in clinical outcomes with AI, four (24%) reported improvement in clinical outcomes compared with controls, two (12%) reported positive effects on clinical outcomes with use of AI but had no formal comparison or controls, and one (6%) reported poor clinical outcomes with AI. Twelve studies (71%) reported improvement in process measures, while two (12%) reported no improvement. Five (30%) studies reported on at least 1 human performance measure.

Conclusion

While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.

Keywords

artificial intelligence - pediatrics - evaluation

Protection of Human and Animal Subjects

None.


Supplementary Material



Publication History

Received: 09 September 2024

Accepted: 20 January 2025

Accepted Manuscript online:
21 January 2025

Article published online:
28 May 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Hoodbhoy Z, Masroor Jeelani S, Aziz A. et al. Machine learning for child and adolescent health: a systematic review. Pediatrics 2021; 147 (01) e2020011833
    Search in Google Scholar
  • 2 Kwok TC, Henry C, Saffaran S. et al. Application and potential of artificial intelligence in neonatal medicine. Semin Fetal Neonatal Med 2022; 27 (05) 101346
    Search in Google Scholar
  • 3 Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial intelligence applications in health care practice: scoping review. J Med Internet Res 2022; 24 (10) e40238
    Search in Google Scholar
  • 4 Iancu A, Leb I, Prokosch HU, Rödle W. Machine learning in medication prescription: a systematic review. Int J Med Inform 2023; 180: 105241
    Search in Google Scholar
  • 5 Alsabri M, Aderinto N, Mourid MR. et al. Artificial intelligence for pediatric emergency medicine. J Med Surg Public Health 2024; 3: 100137
    Search in Google Scholar
  • 6 Walczak S, Scorpio RJ. Predicting pediatric length of stay and acuity of care in the first ten minutes with artificial neural networks. Pediatr Crit Care Med 2000; 1 (01) 42-47
    Search in Google Scholar
  • 7 Hogan AH, Brimacombe M, Mosha M, Flores G. Comparing artificial intelligence and traditional methods to identify factors associated with pediatric asthma readmission. Acad Pediatr 2022; 22 (01) 55-61
    Search in Google Scholar
  • 8 Carlin CS, Ho LV, Ledbetter DR, Aczon MD, Wetzel RC. Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit. J Am Med Inform Assoc 2018; 25 (12) 1600-1607
    Search in Google Scholar
  • 9 Aczon MD, Ledbetter DR, Laksana E, Ho LV, Wetzel RC. Continuous prediction of mortality in the PICU: a recurrent neural network model in a single-center dataset. Pediatr Crit Care Med 2021; 22 (06) 519-529
    Search in Google Scholar
  • 10 Lamping F, Jack T, Rübsamen N. et al. Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms. BMC Pediatr 2018; 18 (01) 112
    Search in Google Scholar
  • 11 Le S, Hoffman J, Barton C. et al. Pediatric severe sepsis prediction using machine learning. Front Pediatr 2019; 7: 413
    Search in Google Scholar
  • 12 Mayampurath A, Sanchez-Pinto LN, Hegermiller E. et al. Development and external validation of a machine learning model for prediction of potential transfer to the PICU. Pediatr Crit Care Med 2022; 23 (07) 514-523
    Search in Google Scholar
  • 13 Pollack MM, Holubkov R, Berg RA. et al. Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network (CPCCRN). Predicting cardiac arrests in pediatric intensive care units. Resuscitation 2018; 133: 25-32
    Search in Google Scholar
  • 14 McInnis C, Garcia MJS, Widjaja E. et al. Magnetic resonance imaging findings are associated with long-term global neurological function or death after traumatic brain injury in critically ill children. J Neurotrauma 2021; 38 (17) 2407-2418
    Search in Google Scholar
  • 15 Tunthanathip T, Oearsakul T. Application of machine learning to predict the outcome of pediatric traumatic brain injury. Chin J Traumatol 2021; 24 (06) 350-355
    Search in Google Scholar
  • 16 Goto Y, Maeda T, Nakatsu-Goto Y. Decision tree model for predicting long-term outcomes in children with out-of-hospital cardiac arrest: a nationwide, population-based observational study. Crit Care 2014; 18 (03) R133
    Search in Google Scholar
  • 17 Fung FW, Topjian AA, Xiao R, Abend NS. Early EEG features for outcome prediction after cardiac arrest in children. J Clin Neurophysiol 2019; 36 (05) 349-357
    Search in Google Scholar
  • 18 Winter MC, Day TE, Ledbetter DR. et al. Machine learning to predict cardiac death within 1 hour after terminal extubation. Pediatr Crit Care Med 2021; 22 (02) 161-171
    Search in Google Scholar
  • 19 Sujan M, Pool R, Salmon P. Eight human factors and ergonomics principles for healthcare artificial intelligence. BMJ Health Care Inform 2022; 29 (01) 100516
    Search in Google Scholar
  • 20 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372 (71) n71
    Search in Google Scholar
  • 21 Niel O, Bastard P, Boussard C, Hogan J, Kwon T, Deschênes G. Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis. Pediatr Nephrol 2018; 33 (10) 1799-1803
    Search in Google Scholar
  • 22 Amrit C, Paauw T, Aly R, Lavric M. Identifying child abuse through text mining and machine learning. Expert systems with applications. Expert Syst Appl 2017; 88: 402-418
    Search in Google Scholar
  • 23 Achten NB, Dorigo-Zetsma JW, van der Linden PD, van Brakel M, Plötz FB. Sepsis calculator implementation reduces empiric antibiotics for suspected early-onset sepsis. Eur J Pediatr 2018; 177 (05) 741-746
    Search in Google Scholar
  • 24 van de Maat J, van der Ven M, Driessen G. et al. Cost study of a cluster randomized trial on a clinical decision rule guiding antibiotic treatment in children with suspected lower respiratory tract infections in the emergency department. Pediatr Infect Dis J 2020; 39 (11) 1026-1031
    Search in Google Scholar
  • 25 Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med 2019; 9 (03) 440-447
    Search in Google Scholar
  • 26 Clark MM, Hildreth A, Batalov S. et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci Transl Med 2019; 11 (489) eaat6177
    Search in Google Scholar
  • 27 Smith JC, Spann A, McCoy AB. et al. Natural language processing and machine learning to enable clinical decision support for treatment of pediatric pneumonia. AMIA Annu Symp Proc 2021; 2020: 1130-1139
    Search in Google Scholar
  • 28 De La Vega FM, Chowdhury S, Moore B. et al. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Med 2021; 13 (01) 153
    Search in Google Scholar
  • 29 van de Maat JS, Peeters D, Nieboer D. et al. Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands: a stepped-wedge cluster randomised trial. PLoS Med 2020; 17 (01) e1003034
    Search in Google Scholar
  • 30 Frymoyer A, Schwenk HT, Zorn Y. et al. Model-informed precision dosing of vancomycin in hospitalized children: implementation and adoption at an academic children's hospital. Front Pharmacol 2020; 11: 551
    Search in Google Scholar
  • 31 Ni Y, Bermudez M, Kennebeck S, Liddy-Hicks S, Dexheimer J. A real-time automated patient screening system for clinical trials eligibility in an emergency department: design and evaluation. JMIR Med Inform 2019; 7 (03) e14185
    Search in Google Scholar
  • 32 Li X, Tian D, Li W. et al. Artificial intelligence-assisted reduction in patients' waiting time for outpatient process: a retrospective cohort study. BMC Health Serv Res 2021; 21 (01) 237
    Search in Google Scholar
  • 33 Dexheimer JW, Abramo TJ, Arnold DH. et al. Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: a randomized clinical trial. Int J Med Inform 2014; 83 (11) 805-813
    Search in Google Scholar
  • 34 Seol HY, Shrestha P, Muth JF. et al. Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. PLoS One 2021; 16 (08) e0255261
    Search in Google Scholar
  • 35 de Vos-Kerkhof E, Nijman RG, Vergouwe Y. et al. Impact of a clinical decision model for febrile children at risk for serious bacterial infections at the emergency department: a randomized controlled trial. PLoS One 2015; 10 (05) e0127620
    Search in Google Scholar
  • 36 Lin H, Li R, Liu Z. et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine 2019; 9: 52-59
    Search in Google Scholar
  • 37 Nimri R, Battelino T, Laffel LM. et al. NextDREAM Consortium. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med 2020; 26 (09) 1380-1384
    Search in Google Scholar
  • 38 Longhurst CA, Singh K, Chopra A, Atreja A, Brownstein JS. A call for artificial intelligence implementation science centers to evaluate clinical effectiveness. NEJM AI 2024; 1 (08) AIp2400223
    Search in Google Scholar
  • 39 Bright TJ, Wong A, Dhurjati R. et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (01) 29-43
    Search in Google Scholar
  • 40 Dziorny AC, Heneghan JA, Bhat MA. et al. Pediatric Data Science and Analytics (PEDAL) Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network. Clinical decision support in the PICU: implications for design and evaluation. Pediatr Crit Care Med 2022; 23 (08) e392-e396
    Search in Google Scholar
  • 41 Lundberg S, Lee SI. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems [Internet]. pp. 4766–4775. Accessed 2017 at: http://arxiv.org/abs/1705.07874
  • 42 Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019; 393 (10181): 1577-1579
    Search in Google Scholar
  • 43 Sounderajah V, Ashrafian H, Aggarwal R. et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI steering group. Nat Med 2020; 26 (06) 807-808
    Search in Google Scholar
  • 44 Vasey B, Nagendran M, Campbell B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine 2022 28:5 [Internet];28(5):924–33. Accessed May 18, 2022 at: https://www.nature.com/articles/s41591-022-01772-9
  • 45 Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ 2020; 370: m3164
    Search in Google Scholar
  • 46 Muralidharan V, Burgart A, Daneshjou R, Rose S. Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI. npj. Digit Med 2023; 6 (01) 1-6
    Search in Google Scholar
  • 47 Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform 2021; 153: 104510
    Search in Google Scholar
  • 48 Tu C, Russo A, Zhang Y. A Checklist for the Usability Evaluation of Artificial Intelligence (AI) mHealth Applications Graphical User Interface. In: Design, User Experience, and Usability: 13th International Conference, DUXU 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA. June 29–July 4, 2024, Proceedings, Part I [Internet]. Berlin, Heidelberg: Springer-Verlag. p. 324–337. Accessed November 4, 2024 at: https://doi.org/10.1007/978-3-031-61351-7_23