Evidence map›Paper›PMID 40881074›Full record

ReviewWorld journal of clinical pediatrics2025

Role of artificial intelligence in congenital heart disease.

Subhrashis Guha Niyogi, Deb Sanjay Nag, Mandar Mahavir Shah, Amlan Swain, Chandrima Naskar, Preeti Srivastava, Ravi Kant

Abstract readReview
In one paragraph

Review in World journal of clinical pediatrics, 2025. 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

7 authors.

Subhrashis Guha NiyogiDepartment of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.
Deb Sanjay NagDepartment of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India. ds.nag@tatasteel.com.
Mandar Mahavir ShahDepartment of Cardiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.
Amlan SwainDepartment of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.
Chandrima NaskarDepartment of Psychiatry, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.
Preeti SrivastavaDepartment of Paediatrics, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.
Ravi KantDepartment of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient's life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

Indexed as

Artificial intelligenceCongenital abnormalitiesDiagnosisEchocardiographyHeart diseases

Identifiers

PMID40881074
PMCPMC12304924

What OpenQuestion holds

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LicenceCC BY-NC
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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.