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Artificial intelligence in pediatric medicine: a call for rigorous reporting standards

Journal of Perinatology volume 45, pages 1031–1033 (2025)Cite this article

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Artificial intelligence (AI) research in pediatric medicine is expanding rapidly, but only a small fraction of models reach clinical use, exposing a critical gap in real-world implementation, evaluation, and reporting. A systematic review by Schouten et al. found that out of 262 pediatric AI studies, only one model (~0.38%) reached a technology readiness level of 8, meaning the model underwent clinical testing. They found that most AI models (87%) were at a level of <5, meaning they had not passed the prototype or developmental stage [1]. Similarly, research on AI in adult intensive care units has shown that fewer than 1 in 50 models progress beyond the prototype stage [2]. Despite these challenges, pediatric AI research continues to advance, though progress is often hindered by ineffective implementation approaches, inconsistent reporting standards, and a lack of pediatric-specific validation. Bridging this gap requires not only technical excellence and effective deployment and maintenance strategies, but also an unwavering commitment to transparency, equity, reproducibility, and ethical integrity.

This commentary provides practical guidance for researchers developing AI models and reviewers evaluating AI studies, offering key principles to ensure scientific rigor and clinical relevance in the rapidly evolving AI-landscape.

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References

  1. Schouten JS, Kalden MACM, van Twist E, Reiss IKM, Gommers DAMPJ, Genderen MEV, et al. From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit. Intensive Care Med. 2024;50:1767–77.

    Article  PubMed  PubMed Central  Google Scholar 

  2. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med. 2021;47:750–60.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Liu X, Cruz Rivera S, 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. Nat Med. 2020;26:1364–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ibrahim H, Liu X, Rivera SC, Moher D, Chen AW, Sydes MR, et al. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials. 2021;22:11.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11:e048008.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Gallifant J, Afshar M, Ameen S, Aphinyanaphongs Y, Chen S, Cacciamani G, et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med. 2025;31:60–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inf. 2021;28:e100385.

    Article  Google Scholar 

  9. 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;6:166.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards for interventions that use artificial intelligence (CHEERS-AI). Value Health. 2024;27:1196–205.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Shiferaw KB, Roloff M, Balaur I, Welter D, Waltemath D, Zeleke AA. Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review. JAMIA Open. 2025;8:ooae155.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28:924–33.

    Article  CAS  PubMed  Google Scholar 

  13. Jacob C, Brasier N, Laurenzi E, Heuss S, Mougiakakou SG, Coltekin A, et al. AI for IMPACTS framework for evaluating the long-term real-world impacts of AI-powered clinician tools: Systematic review and narrative synthesis. J Med Internet Res. 2025;27:e67485.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Olczak J, Pavlopoulos J, Prijs J, Ijpma FFA, Doornberg JN, Lundstrom C, et al. Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop. 2021;92:513–25.

    Article  PubMed  PubMed Central  Google Scholar 

  15. van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalke VR, Lane PJ. Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. J Am Med Inf Assoc. 2023;30:1503–15.

    Article  Google Scholar 

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Authors and Affiliations

  1. Section of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA

    James S. Barry

  2. Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Kristyn Beam

  3. Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Ryan M. McAdams

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  1. James S. Barry
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  2. Kristyn Beam
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Contributions

JB and RM conceptualized the contents of the commentary. JB, KB, and RM conducted the literature search, drafted the initial manuscript, and were involved with the revisions of the manuscript. All authors agreed to the final draft of the manuscript being submitted and final approval of the manuscript.

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Correspondence to James S. Barry.

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The authors declare no competing interests.

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Barry, J.S., Beam, K. & McAdams, R.M. Artificial intelligence in pediatric medicine: a call for rigorous reporting standards. J Perinatol 45, 1031–1033 (2025). https://doi.org/10.1038/s41372-025-02284-3

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