Call for Papers on Artificial Intelligence Applied to Pediatric Care

Published in JAMA Pediatrics. Here is a link to the article.

Regenstrief Institute author: Aaron E. Carroll, M.D., M.S.

Artificial intelligence (AI) has long been discussed as a transformative tool in health care, promising to revolutionize all aspects of patient care and even our broader health care systems. Nowhere is this potential more profound than in the realm of pediatrics, where early detection and intervention can significantly impact life trajectories.

AI is evolving rapidly, with potential to transform health care diagnosis and treatment for children and adolescents.¹ It can improve outcomes while making many facets of the health care ecosystem more efficient.² It can also assist with imaging analysis, research, documentation, workflow efficiency, and more.³

In recent years, we have witnessed AI transition from a theoretical promise to a practical tool. Machine learning algorithms have been used in diagnosing skin cancer with comparable accuracy to dermatologists. AI’s predictive modeling capabilities have been tapped into to forecast patient readmission rates,⁴ disease progression, and even to anticipate epidemic outbreaks.⁵ Deep learning techniques are enhancing the interpretation of medical imaging, and natural language processing is streamlining the review and analysis of electronic health records.

However, many challenges and ethical issues also exist that need to be addressed when applying AI to the care of pediatric populations.⁶ These include ensuring the quality, validity, safety, and fairness of AI systems; protecting the privacy, security, and consent of patients and health care professionals; balancing the human and machine roles and responsibilities; and evaluating the impact of AI on health care costs, access, equity, and outcomes.⁷ As AI systems are only as good as the inputs they rely on, there is the potential that they could increase rather than decrease disparities.

JAMA Pediatrics now invites submissions of original research articles on the topic of AI applied to pediatric health. We are interested in high-quality studies and viewpoints that explore the benefits, risks, limitations, and implications of AI for pediatric care across various settings and domains.

We welcome submissions that address any aspect of AI applied to the health of children, including:

• Development and validation of AI models or tools for pediatric diagnosis, prognosis, screening, prevention, intervention, or monitoring⁸
• Evaluation of the clinical effectiveness, cost-effectiveness, safety, usability, or acceptability of AI interventions or systems in pediatric care⁹
• Comparison of AI-based approaches with conventional methods or human experts for pediatric care¹⁰
• Identification and mitigation of potential biases, errors, or harms of AI for pediatric populations¹¹
• Ethical, legal, social, or policy issues related to the use of AI for pediatric care¹²
• Patient, family, or clinician perspectives on the use of AI for pediatric care¹³
• Implementation and dissemination strategies for AI interventions or systems in pediatric setting¹⁴

We encourage submissions that use rigorous methods, report transparent and reproducible results, and adhere to relevant reporting standards and guidelines. We also encourage submissions that involve multidisciplinary collaboration and stakeholder engagement.

All submissions will undergo the journal’s usual rigorous peer review to ensure quality and relevance. Accepted papers will be published on a rolling basis and will be promoted extensively to reach a wide audience globally. Guidelines for manuscript preparation and submission can be found on in our Instructions for Authors.¹⁵ Please indicate in your cover letter that your manuscript is intended for this call for papers.

We look forward to your valuable contributions as we embark on this exciting exploration of AI’s potential within pediatric health. AI will undoubtedly play a large role in pediatric health and health care in the future, and we seek to publish the best available science to guide it.

References:

1. Liang H, Tsui BY, Ni  H,  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

2.Ramgopal  S, Sanchez-Pinto  LN, Horvat  CM, Carroll  MS, Luo  Y, Florin  TA.  Artificial intelligence-based clinical decision support in pediatrics.   Pediatr Res. 2023;93(2):334-341. doi:10.1038/s41390-022-02226-1

3.Shah  N, Arshad  A, Mazer  MB, Carroll  CL, Shein  SL, Remy  KE.  The use of machine learning and artificial intelligence within pediatric critical care.   Pediatr Res. 2023;93(2):405-412. doi:10.1038/s41390-022-02380-6

4.Romero-Brufau  S, Wyatt  KD, Boyum  P, Mickelson  M, Moore  M, Cognetta-Rieke  C.  Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital.   Appl Clin Inform. 2020;11(4):570-577. doi:10.1055/s-0040-1715827

5.Malik  YS, Sircar  S, Bhat  S,  et al.  How artificial intelligence may help the Covid-19 pandemic: pitfalls and lessons for the future.   Rev Med Virol. 2021;31(5):1-11. doi:10.1002/rmv.2205

6.Mittelstadt  BD, Floridi  L.  The ethics of big data: current and foreseeable issues in biomedical contexts.   Sci Eng Ethics. 2016;22(2):303-341. doi:10.1007/s11948-015-9652-2

7.Artificial Intelligence in Health Care.  Benefits and Challenges of Technologies to Augment Patient Care (710920). Government Accountability Office; 2020.

8.Rajkomar  A, Dean  J, Kohane  I.  Machine learning in medicine: reply.   N Engl J Med. 2019;380(26):2589-2590.

9.Wilkinson  J, Arnold  KF, Murray  EJ,  et al.  Time to reality check the promises of machine learning-powered precision medicine.   Lancet Digit Health. 2020;2(12):e677-e680. doi:10.1016/S2589-7500(20)30200-4

10.Ayers  JW, Poliak  A, Dredze  M,  et al.  Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum.   JAMA Intern Med. 2023;e231838. doi:10.1001/jamainternmed.2023.1838

11.Tschandl  P.  Risk of Bias and Error From Data Sets Used for Dermatologic Artificial Intelligence.   JAMA Dermatol. 2021;157(11):1271-1273. doi:10.1001/jamadermatol.2021.3128

12.Fiske  A, Henningsen  P, Buyx  A.  Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy.   J Med Internet Res. 2019;21(5):e13216. doi:10.2196/13216

13.Chen  YW, Stanley  K, Att  W.  Artificial intelligence in dentistry: current applications and future perspectives.   Quintessence Int. 2020;51(3):248-257.

14.Svedberg  P, Reed  J, Nilsen  P, Barlow  J, Macrae  C, Nygren  J.  Toward successful implementation of artificial intelligence in health care practice: protocol for a research program.   JMIR Res Protoc. 2022;11(3):e34920. doi:10.2196/34920

15.Instructions for authors. JAMA Pediatrics. Updated September 28, 2022. Accessed May 16, 2023. https://jamanetwork.com/journals/jamapediatrics/pages/instructions-for-authors

Authors and Affiliations:

Aaron E. Carroll, M.D., M.S.^1,2, Dimitri A. Christakis, M.D., MPH^3,4

¹Center for Pediatric and Adolescent Comparative Effectiveness Research, Indiana University School of Medicine, Indianapolis

²Web and Social Media Editor, JAMA Pediatrics

³Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, Washington

⁴Editor, JAMA Pediatrics