Evidence map›Paper›PMID 42583651›Full record

ArticleAnnals of medicine and surgery (2012)2026

Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.

Pegah Rashidian, Saisree Reddy Adla Jala, Kavya Priya Somu, Abinash Mahapatro, Nikitha Chellapuram, Seyyed Mohammad Hashemi, Abdulhadi Jameel Alotaibi, Omar Hasan Hasan, Amir Nasrollahizadeh, Negin Letafatkar and 3 more

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Article in Annals of medicine and surgery (2012), 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

13 authors.

Pegah RashidianVali-e-Asr Reproductive Health Research Center, Family Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Saisree Reddy Adla JalaMission Hospital, Asheville, NC, USA.
Kavya Priya SomuCentinela Hospital Medical Center, Inglewood, CA, USA.
Abinash MahapatroSIU School of Medicine, Springfield, IL, USA.
Nikitha ChellapuramCooper University Hospital, Camden, NJ, USA.
Seyyed Mohammad HashemiCardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Abdulhadi Jameel AlotaibiDepartment of Medicine, Vision Colleges, Riyadh, Saudi Arabia.
Omar Hasan HasanDepartment of Medicine, Vision Colleges, Riyadh, Saudi Arabia.
Amir NasrollahizadehTehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Negin LetafatkarGastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Maryam JafariGastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Nimra ShafiDepartment of Medicine, Arnot Ogden Medical Center, Elmira, NY, USA.
Ehsan Amini-SalehiGastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure (HF) is a complex syndrome with high morbidity and mortality. Despite advancements in treatment, its management remains a challenge. The objective of this study was to map the scientific landscape of artificial intelligence (AI) applications in HF management through a bibliometric analysis. Data were retrieved from the Web of Science Core Collection. Keywords related to AI and HF were used to identify relevant research articles. Various bibliometric tools, such as Biblioshiny, VOS viewer, and CiteSpace, were used for quantitative trends, collaboration networks, and thematic areas, which were assessed. A total of 1332 studies were included in the final analysis. Publication trends show a sharp increase in AI research related to HF from 2016 onward, with 317 studies published in 2025. The most frequent keywords in the field were

Indexed as

artificial intelligencecardiovascular systemsheart failuremachine learningrisk stratification

Identifiers

PMID42583651
PMCPMC13461045

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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.