ArticleJAMA network open2026
FDA-Regulated AI-Enabled Medical Devices With Pediatric Indications.
Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Accelerating Pediatric Oncology Drug Development: Advances in Clinical Pharmacology, Trial Design, Non-Clinical Evidence, and Regulatory Science.Journal of clinical pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Importance: Artificial intelligence (AI)-based technologies hold promise for faster and more accurate devices in health care; however, little is known about their availability to pediatric patients. A comprehensive analysis of US Food and Drug Administration (FDA) regulatory submissions is necessary to identify technologies with pediatric labeling. Objective: To characterize AI devices marketed in the US, identify those with pediatric indications, and provide clues on AI-specific barriers to pediatric device innovation. Design, Setting, and Participants: This retrospective descriptive cross-sectional study was conducted using public data from the FDA's AI-Enabled Medical Device List. Marketing submissions reviewed by the FDA between November 1995 and June 2024 were analyzed. Main Outcomes and Measures: Prevalence of pediatric AI devices and their main characteristics (eg, clinical area, review time, and year of FDA marketing decision). Results: Among 952 submissions, 42 (4.4%) included pediatric age ranges (0-17 years). The first pediatric-inclusive device was cleared in 2015, and 5 exclusively pediatric technologies were introduced between 2020 and 2024. Of 18 clinical areas, radiology comprised 723 of all devices (75.9%) but only 18 of the devices specifically labeled for pediatrics (42.9%), whereas neurology comprised 34 devices overall (3.6%) vs 13 pediatric devices (31.0%); 10 clinical areas (55.6%) were missing among pediatric devices. The median (IQR) FDA review time was significantly longer for pediatric than for nonpediatric devices (162 [114-228] days; 95% CI, 151-212 days vs 134 [87-214] days; 95% CI, 149-162 days; 2-sided Mann-Whitney U test P = .049). Based on National Clinical Trial Identifiers in FDA summaries, clinical trial registration was noted in 6 pediatric (14.3%) vs 20 of 906 nonpediatric (2.2%) submissions. Conclusions and Relevance: In this study, pediatric devices were rare, emerged recently, and had longer review times and a higher proportion of registered clinical trials compared with nonpediatric devices, suggesting expectations for more pediatric-specific evidence despite unchanged statutory standards. To address gaps in pediatric device development, the FDA should standardize age labeling and validation requirements for AI-enabled technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.