Evidence map›Paper›PMID 41463617›Full record

ReviewBioengineering (Basel, Switzerland)2025

Integration of Artificial Intelligence and Wearable Devices in Pediatric Clinical Care: A Review.

Huili Zheng, Pragya Sharma, Matthew Johnson, Matteo Danieletto, Eugenia Alleva, Alexander W Charney, Girish N Nadkarni, Chethan Sarabu, Bjoern M Eskofier, Yuri Ahuja and 6 more

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Huili ZhengWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Pragya SharmaWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-2750-0147
Matthew JohnsonWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0001-7056-2824
Matteo DanielettoWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0002-5178-9182
Eugenia AllevaWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-3523-693X
Alexander W CharneyWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0001-8135-6858
Girish N NadkarniWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Chethan SarabuHealth Tech Hub, Cornell Tech, Weill Cornell Medicine, New York, NY 10044, USA.
Bjoern M EskofierInstitute of AI in Medicine, LMU Munich & LMU Hospital, 85152 Munich, Germany.ORCID 0000-0002-0417-0336
Yuri AhujaWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Florian RichterArtemisAI Labs, Inc., San Francisco, CA 94102, USA.ORCID 0000-0002-7669-1923
Eyal KlangWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Sandeep GangadharanThe Center for AI in Children's Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Felix RichterWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-3429-9621
Emma HolmesWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0009-0001-3639-7631
Benjamin S GlicksbergWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-4515-8090

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care.

Indexed as

artificial intelligencebiosignalshealthcarepediatricswearable deviceswearables

Identifiers

PMID41463617
PMCPMC12729400

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.