{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T13:27:32Z","timestamp":1794317252266,"version":"4.9.0"},"reference-count":48,"publisher":"American Academy of Pediatrics (AAP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,11,1]]},"abstract":"<jats:p>The integration of generative artificial intelligence (GenAI) into pediatric health care offers exciting opportunities alongside critical challenges. GenAI tools, like large language models (LLMs), show promise in several health care applications, including but not limited to clinical decision support, documentation, and medical education across a wide range of pediatric subspecialties. However, real-world validation remains limited, and concerns persist around accuracy, bias, reliability, sustainability, and durability. Studies indicate that LLMs, when applied to pediatrics, often underperform compared with adult medical specialties, raising questions about their readiness for use in the care of children and adolescents. Moreover, the risk of exacerbating health disparities among patients of various races, ethnicities, genders, languages, abilities, and socioeconomic statuses because of biased or nonrepresentative training data underscores the need for rigorous oversight and accountability.<\/jats:p>\n                  <jats:p>This policy outlines recommendations for the safe, equitable, and effective use of GenAI in pediatric settings. Developers should prioritize diverse pediatric data sets, proactively address bias, and implement strong data privacy and security safeguards. Health care institutions must establish rigorous validation protocols, ensure compliance with privacy regulations, and maintain clear human oversight. Relevant regulatory and oversight bodies should enforce pediatric-specific evaluations and require postmarket surveillance. Governance frameworks must support interdisciplinary collaboration, transparency, and education initiatives to equip pediatricians with GenAI literacy. Reasonable disclosure of GenAI involvement in patient care is essential to establish and strengthen trust. GenAI policies pertaining to the care of children and adolescents must remain adaptive to ensure ethical, equitable, and evidence-based implementation. Despite these numerous challenges, GenAI holds transformative potential to improve pediatric health outcomes, and this policy seeks to empower developers, researchers, and clinicians to responsibly build and harness this technology.<\/jats:p>","DOI":"10.1542\/peds.2026-079037","type":"journal-article","created":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T04:02:11Z","timestamp":1791000131000},"source":"Crossref","is-referenced-by-count":0,"title":["Recommendations for the Development and Implementation of Generative Artificial Intelligence Tools in Pediatric Clinical Care: Policy Statement"],"prefix":"10.1542","volume":"158","author":[{"given":"Srinivasan","family":"Suresh","sequence":"first","affiliation":[{"name":"1Department of Pediatrics, University of Pittsburgh School of Medicine; UPMC Children\u2019s Hospital of Pittsburgh, Pittsburgh, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. Brandon","family":"Hunter","sequence":"additional","affiliation":[{"name":"2Department of Pediatrics, Division of Pediatric Critical Care, Texas Children\u2019s Hospital, Baylor College of Medicine, Houston, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Periard","sequence":"additional","affiliation":[{"name":"3Private practice, Grand Rapids, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Espinoza","sequence":"additional","affiliation":[{"name":"4Stanley Manne Children\u2019s Research Institute, Ann & Robert H. Lurie Children\u2019s Hospital of Chicago, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Council on Clinical Information Technology","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eli M.","family":"Lourie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivasan","family":"Suresh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan D.","family":"Chaparro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn Kilpatrick","family":"Cheek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin R.","family":"Dufendach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Goldstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marvin B.","family":"Harper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Hess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandan P.","family":"Kennedy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed J.","family":"Rabbani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonja S.","family":"Short","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lindsay","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa S.","family":"Van Cain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Section on Innovation in Therapeutics and Technology","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Molten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyssa M.","family":"Abo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gwenyth Anne","family":"Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janene Hilary","family":"Fuerch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen M.","family":"Kaplan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasum","family":"Peiris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"417","published-online":{"date-parts":[[2026,10,3]]},"reference":[{"key":"2026111008073123000_r1","article-title":"Artificial intelligence","author":"Dictionary.com"},{"key":"2026111008073123000_r2","article-title":"How is ChatGPT\u2019s behavior changing over time?","author":"Chen","year":"2023"},{"issue":"4","key":"2026111008073123000_r3","doi-asserted-by":"crossref","first-page":"e246565","DOI":"10.1001\/jamanetworkopen.2024.6565","article-title":"AI-Generated draft replies integrated into health records and physicians\u2019 electronic communication","volume":"7","author":"Tai-Seale","year":"2024","journal-title":"JAMA Netw Open."},{"issue":"4","key":"2026111008073123000_r4","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1093\/jamia\/ocae022","article-title":"The impact of nuance DAX ambient listening AI documentation: a cohort study","volume":"31","author":"Haberle","year":"2024","journal-title":"J Am Med Inform Assoc."},{"key":"2026111008073123000_r5","doi-asserted-by":"crossref","article-title":"Recent advances in generative AI and large language models: current status, challenges, and perspectives","author":"Hagos","DOI":"10.1109\/TAI.2024.3444742"},{"key":"2026111008073123000_r6","doi-asserted-by":"crossref","first-page":"1477898","DOI":"10.3389\/fmed.2024.1477898","article-title":"Large language models in patient education: a scoping review of applications in medicine","volume":"11","author":"Aydin","year":"2024","journal-title":"Front Med (Lausanne)."},{"issue":"8","key":"2026111008073123000_r7","doi-asserted-by":"crossref","first-page":"102887","DOI":"10.1016\/j.acap.2025.102887","article-title":"Caregiver and pediatric clinician perspectives on artificial intelligence for language services","volume":"25","author":"Ficerai-Garland","year":"2025","journal-title":"Acad Pediatr."},{"issue":"4","key":"2026111008073123000_r8","first-page":"757","article-title":"May artificial intelligence influence future pediatric research? The case of ChatGPT","volume":"10","author":"Corsello","year":"2023","journal-title":"Children (Basel)."},{"issue":"4","key":"2026111008073123000_r9","first-page":"e57795","article-title":"Redefining healthcare with artificial intelligence (AI): the contributions of ChatGPT, Gemini, and Co-pilot","volume":"16","author":"Alhur","year":"2024","journal-title":"Cureus."},{"issue":"11","key":"2026111008073123000_r10","doi-asserted-by":"crossref","first-page":"98","DOI":"10.3390\/bs8110098","article-title":"Factors related to physician burnout and its consequences: a review","volume":"8","author":"Patel","year":"2018","journal-title":"Behav Sci (Basel)."},{"key":"2026111008073123000_r11","article-title":"The AI Index 2024 Annual Report","author":"Maslej","year":"2024"},{"issue":"4","key":"2026111008073123000_r12","doi-asserted-by":"crossref","first-page":"e2024068509","DOI":"10.1542\/peds.2024-068509","article-title":"Special requirements of electronic health record systems in pediatrics: Clinical report","volume":"154","author":"Dufendach","year":"2024","journal-title":"Pediatrics."},{"issue":"1","key":"2026111008073123000_r13","doi-asserted-by":"crossref","first-page":"e20191359","DOI":"10.1542\/peds.2019-1359","article-title":"Electronic communication of the health record and information with pediatric patients and their guardians","volume":"144","author":"Webber","year":"2019","journal-title":"Pediatrics."},{"issue":"5","key":"2026111008073123000_r14","doi-asserted-by":"crossref","first-page":"e2024066326","DOI":"10.1542\/peds.2024-066326","article-title":"Confidentiality in the care of adolescents: Policy statement","volume":"153","author":"Chung","year":"2024","journal-title":"Pediatrics."},{"issue":"4","key":"2026111008073123000_r15","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1017\/jme.2024.15","article-title":"AI chatbots and challenges of HIPAA compliance for AI developers and vendors","volume":"51","author":"Rezaeikhonakdar","year":"2023","journal-title":"J Law Med Ethics."},{"issue":"1","key":"2026111008073123000_r16","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1186\/s13054-023-04393-x","article-title":"Large language models and the perils of their hallucinations","volume":"27","author":"Azamfirei","year":"2023","journal-title":"Crit Care."},{"key":"2026111008073123000_r17","article-title":"Executive Summary for the Digital Health Advisory Committee Meeting","author":"US Food and Drug Administration","year":"2024"},{"key":"2026111008073123000_r18","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.18653\/v1\/2021.emnlp-main.98","article-title":"Documenting Large Webtext Corpora: a case study on the Colossal Clean Crawled Corpus","author":"Dodge","year":"2021","journal-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing."},{"issue":"7","key":"2026111008073123000_r19","doi-asserted-by":"crossref","first-page":"964","DOI":"10.7326\/M24-0146","article-title":"Artificial Intelligence in the Provision of Health Care: An American College of Physicians Policy Position Paper","volume":"177","author":"Daneshvar","year":"2024","journal-title":"Ann Intern Med."},{"key":"2026111008073123000_r20","article-title":"Artificial Intelligence Resources","author":"American Academy of Neurology"},{"issue":"3","key":"2026111008073123000_r21","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1007\/s11606-024-09102-0","article-title":"Recommendations for clinicians, technologists, and healthcare organizations on the use of generative artificial intelligence in medicine: a position statement from the Society of General Internal Medicine","volume":"40","author":"Crowe","year":"2025","journal-title":"J Gen Intern Med."},{"key":"2026111008073123000_r22","article-title":"Ethical Application of Artificial Intelligence in Family Medicine","author":"American Academy of Family Physicians"},{"issue":"8","key":"2026111008073123000_r23","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.jacr.2023.06.003","article-title":"Use of artificial intelligence in radiology: impact on pediatric patients, a white paper from the ACR Pediatric AI Workgroup","volume":"20","author":"Sammer","year":"2023","journal-title":"J Am Coll Radiol."},{"key":"2026111008073123000_r24","article-title":"Augmented Intelligence Development, Deployment, and Use in Health Care","author":"American Medical Association"},{"key":"2026111008073123000_r25","article-title":"Good Machine Learning Practice for Medical Device Development: Guiding Principles","author":"US Food and Drug Administration"},{"key":"2026111008073123000_r26","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.pediatrneurol.2023.08.035","article-title":"Leveraging ChatGPT in the pediatric neurology clinic: practical considerations for use to improve efficiency and outcomes","volume":"148","author":"Karakas","year":"2023","journal-title":"Pediatr Neurol."},{"issue":"2S","key":"2026111008073123000_r27","doi-asserted-by":"crossref","first-page":"S52","DOI":"10.1016\/j.jadohealth.2020.02.021","article-title":"Artificial intelligence for personalized preventive adolescent healthcare","volume":"67","author":"Rowe","year":"2020","journal-title":"J Adolesc Health."},{"issue":"6464","key":"2026111008073123000_r28","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science."},{"issue":"1","key":"2026111008073123000_r29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41746-023-00939-z","article-title":"Large language models propagate race-based medicine","volume":"6","author":"Omiye","year":"2023","journal-title":"NPJ Digit Med."},{"issue":"4","key":"2026111008073123000_r30","first-page":"356","article-title":"Gender and ethnicity bias of text-to-image generative artificial intelligence in medical imaging, part 1: preliminary evaluation","volume":"52","author":"Currie","year":"2024","journal-title":"J Nucl Med Technol."},{"issue":"1","key":"2026111008073123000_r31","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s43856-022-00214-4","article-title":"Mitigating the impact of biased artificial intelligence in emergency decision-making","volume":"2","author":"Adam","year":"2022","journal-title":"Commun Med (Lond)."},{"key":"2026111008073123000_r32","first-page":"220","article-title":"A Comparison Study Using a Domain Specific Large Language Model","author":"Parra","year":"2023"},{"issue":"3","key":"2026111008073123000_r33","doi-asserted-by":"crossref","first-page":"e2023064683","DOI":"10.1542\/peds.2023-064683","article-title":"Large language models in pediatric education: current uses and future potential","volume":"154","author":"Suresh","year":"2024","journal-title":"Pediatrics."},{"key":"2026111008073123000_r34","doi-asserted-by":"crossref","first-page":"e50945","DOI":"10.2196\/50945","article-title":"The role of large language models in medical education: applications and implications","volume":"9","author":"Safranek","year":"2023","journal-title":"JMIR Med Educ."},{"key":"2026111008073123000_r35","doi-asserted-by":"crossref","first-page":"23821205241263475","DOI":"10.1177\/23821205241263475","article-title":"Artificial intelligence and objective structured clinical examinations: using ChatGPT to revolutionize clinical skills assessment in medical education","volume":"11","author":"Misra","year":"2024","journal-title":"J Med Educ Curric Dev."},{"issue":"9","key":"2026111008073123000_r36","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1001\/jamapediatrics.2023.2373","article-title":"Performance of a large language model on practice questions for the neonatal board examination","volume":"177","author":"Beam","year":"2023","journal-title":"JAMA Pediatr."},{"issue":"1","key":"2026111008073123000_r37","doi-asserted-by":"crossref","first-page":"26.e1","DOI":"10.1016\/j.jpurol.2023.08.003","article-title":"Evaluating the performance of ChatGPT in answering questions related to pediatric urology","volume":"20","author":"Caglar","year":"2024","journal-title":"J Pediatr Urol."},{"issue":"9","key":"2026111008073123000_r38","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1038\/s41591-024-03097-1","article-title":"Evaluation and mitigation of the limitations of large language models in clinical decision-making","volume":"30","author":"Hager","year":"2024","journal-title":"Nat Med."},{"issue":"1","key":"2026111008073123000_r39","doi-asserted-by":"crossref","first-page":"8236","DOI":"10.1038\/s41467-024-52415-1","article-title":"Evaluating the use of large language models to provide clinical recommendations in the Emergency Department","volume":"15","author":"Williams","year":"2024","journal-title":"Nat Commun."},{"issue":"4","key":"2026111008073123000_r40","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1001\/jama.2024.21700","article-title":"Testing and evaluation of health care applications of large language models: a systematic review","volume":"333","author":"Bedi","year":"2025","journal-title":"JAMA."},{"key":"2026111008073123000_r41","article-title":"Generative methods for pediatric genetics education [preprint August 2, 2023]","author":"Waikel","journal-title":"medRxiv."},{"key":"2026111008073123000_r42","doi-asserted-by":"crossref","first-page":"e49022","DOI":"10.2196\/49022","article-title":"Promises, pitfalls, and clinical applications of artificial intelligence in pediatrics","volume":"26","author":"Bhargava","year":"2024","journal-title":"J Med Internet Res."},{"issue":"2","key":"2026111008073123000_r43","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1038\/s41390-022-02226-1","article-title":"Artificial intelligence-based clinical decision support in pediatrics","volume":"93","author":"Ramgopal","year":"2023","journal-title":"Pediatr Res."},{"issue":"5","key":"2026111008073123000_r44","doi-asserted-by":"crossref","DOI":"10.1056\/AIdbp2300192","article-title":"GPT versus resident physicians\u2014a benchmark based on official board scores","volume":"1","author":"Katz","year":"2024","journal-title":"NEJM AI."},{"issue":"1","key":"2026111008073123000_r45","doi-asserted-by":"crossref","first-page":"e230513","DOI":"10.1148\/ryai.230513","article-title":"Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR and RSNA","volume":"6","author":"Brady","year":"2024","journal-title":"Radiol Artif Intell."},{"key":"2026111008073123000_r46","volume-title":"Artificial Intelligence in Health Professions Education: Proceedings of a Workshop","author":"National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Global Health; Global Forum on Innovation in Health Professional Education","year":"2023"},{"key":"2026111008073123000_r47","doi-asserted-by":"crossref","first-page":"1020592","DOI":"10.3389\/frai.2023.1020592","article-title":"The assessment list for trustworthy artificial intelligence: A review and recommendations","volume":"6","author":"Radclyffe","year":"2023","journal-title":"Front Artif Intell."},{"issue":"1","key":"2026111008073123000_r48","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1186\/s12913-023-10462-2","article-title":"The application of artificial intelligence in health policy: a scoping review","volume":"23","author":"Ramezani","year":"2023","journal-title":"BMC Health Serv Res."}],"container-title":["Pediatrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/publications.aap.org\/pediatrics\/article-pdf\/doi\/10.1542\/peds.2026-079037\/1975708\/pediatrics.2026079037.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/publications.aap.org\/pediatrics\/article-pdf\/doi\/10.1542\/peds.2026-079037\/1975708\/pediatrics.2026079037.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T13:07:41Z","timestamp":1794316061000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.aap.org\/pediatrics\/article\/158\/5\/e2026079037\/210626\/Recommendations-for-the-Development-and"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,3]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,10,3]]},"published-print":{"date-parts":[[2026,11,1]]}},"URL":"https:\/\/doi.org\/10.1542\/peds.2026-079037","relation":{},"ISSN":["0031-4005","1098-4275"],"issn-type":[{"value":"0031-4005","type":"print"},{"value":"1098-4275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,3]]},"article-number":"e2026079037"}}