Evidence map›Paper›PMID 40973564›Full record

ReviewSeminars in perinatology2025

Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.

Robert Barrett, Brooke Lawler, Star Liu, Woo Yeon Park, Marjan Davoodi, Ben Martin, Sai Manasa Kalyanam, Kartikeya Makker, Jordan R Kuiper, Khyzer B Aziz

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Seminars in perinatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

10 authors.

Robert BarrettBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Brooke LawlerBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Star LiuBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Woo Yeon ParkBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Marjan DavoodiBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Ben MartinBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States.
Sai Manasa KalyanamDepartment of Pediatrics, Johns Hopkins University, Baltimore, MD, United States.
Kartikeya MakkerDepartment of Pediatrics, Johns Hopkins University, Baltimore, MD, United States.
Jordan R KuiperDepartment of Environmental & Occupational Health, George Washington University, Washington, DC, United States.
Khyzer B AzizBiomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, United States; Department of Pediatrics, Johns Hopkins University, Baltimore, MD, United States. Electronic address: kaziz5@jhmi.edu.

Funding

Johns Hopkins Training Program in Biomedical Informatics and Data ScienceT15LM013979 · NLM · JOHNS HOPKINS UNIVERSITY · PI CHRISTOPHER G CHUTE, Hadi Kharrazi · 2022 to 2026
$2.2M
NLM NIH HHS T15 LM013979
6 · The paper itself

Abstract

Significant strides have been made in utilizing data, information, and knowledge to enhance neonatal outcomes. This review examines how data informatics, encompassing electronic health records (EHRs), data standards, and artificial intelligence (AI), has facilitated advancements in neonatal care and research. Vast amounts of data, structured and unstructured, have been produced from clinical care. In turn AI stands to improve patient care, safety, and quality improvement initiatives. Facilitated by AI, clinicians' interaction with neonatal informatic tools is transitioning from reactive to real-time, proactive care. Historically, necrotizing enterocolitis, sepsis, medical imaging, and neonatal mortality have been the targets of AI-integrated neonatal care. While much progress has been made in developing state-of-the-art AI tools, their development and implementation must consider optimization of patient care, clinical workflows, and aim to decrease clinician burnout. Employing a sociotechnical framework to assess both technical and human factors is key to effectively evaluating clinical utility, promoting adoption, and facilitating successful deployment. Beyond technical concerns, ethical considerations such as trust in AI, data security, and model transparency are critical to the responsible deployment of informatics tools. Ongoing advancements in neonatal care coupled with informatics, multi-omics, AI, and federated learning expands the possibilities of personalized care for neonates.

Indexed as

Artificial IntelligenceElectronic Health RecordsMedical InformaticsNeonatologyHumansInfant, NewbornQuality ImprovementArtificial intelligenceDataInformaticsLearning health systemNeonatalNetworksOutcomes

Identifiers

PMID40973564
PMCPMC13191498

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.