Evidence map›Paper›PMID 42572076›Full record

ArticleEuropean journal of pediatrics2026

Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.

Aditya Hemendra Bhatt, Somashekhar Marutirao Nimbalkar, Lalan Bharti

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in European journal of pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Aditya Hemendra BhattDepartment of Neonatology, Pramukhswami Medical College, Bhaikaka University, Karamsad, Gujarat, India. adityabhatt47@gmail.com.ORCID http://orcid.org/0000-0002-7284-2915
Somashekhar Marutirao NimbalkarDepartment of Pediatrics, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India.
Lalan BhartiDepartment of Pediatrics, JPCH, Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to map ethical, legal, social, professional, and implementation issues associated with artificial intelligence (AI) in neonatal intensive care units (NICUs). A JBI-informed scoping review using the population-concept-context framework was reported according to PRISMA-ScR. Peer-reviewed English-language articles published from 1 January 2016 to 31 May 2026 were eligible. Because the original exports and screening log were unavailable, a documented updated rerun was completed on 13 July 2026 using public PubMed-indexed bibliographic searching, supplementary publisher and bibliographic web searching, and backward and forward citation chaining. Exact strategies and record-level decisions are provided as online resources. The retrieval log contained 78 record captures. After removal of 21 duplicates, 57 unique records were screened; 45 full texts were assessed, 14 were excluded with documented reasons, and 31 sources were included. Eight recurring domains were identified: data governance; bias and fairness; transparency and explainability; human oversight; accountability and surveillance; parental engagement; professional readiness and workflow; and equitable implementation. Empirical evidence was concentrated on parent and nurse perceptions, pain assessment, counseling, and explainability, whereas consent processes, subgroup fairness, liability, and post-deployment safety remained under-studied.

conclusionEthically responsible NICU AI requires secure governance, local and subgroup validation, understandable communication, active clinician oversight, defined accountability, staff and family engagement, and prospective monitoring. Generative AI should remain supervised and should not replace clinician-family communication. WHAT IS KNOWN: • Artificial intelligence is increasingly being developed for neonatal outcome prediction, monitoring, pain assessment, clinical decision support, documentation, and family communication, but relatively few systems have progressed to validated routine NICU use. • The use of AI in neonatal care raises concerns regarding privacy, algorithmic bias, explainability, accountability, human oversight, parental trust, and equitable access. WHAT IS NEW: • This scoping review identifies eight recurring ethical and implementation domains for NICU AI: data governance, fairness, transparency, human oversight, accountability, parental engagement, professional readiness, and equitable implementation. • Current empirical evidence is concentrated on stakeholder perceptions, pain assessment, counseling, and explainability, while consent processes, subgroup fairness, legal responsibility, and post-deployment safety monitoring remain insufficiently studied.

Indexed as

Artificial IntelligenceIntensive Care Units, NeonatalHumansInfant, NewbornArtificial intelligenceClinical decision supportEthicsMachine learningNeonatal intensive careScoping review

Identifiers

What OpenQuestion holds

Textmetadata
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.