Evidence map›Paper›PMID 39763487›Full record

ReviewFrontiers in sports and active living2024

Transforming tennis with artificial intelligence: a bibliometric review.

Tatiana Sampaio, João P Oliveira, Daniel A Marinho, Henrique P Neiva, Jorge E Morais

Abstract readReview
In one paragraph

Review in Frontiers in sports and active living, 2024. 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

5 authors.

Tatiana SampaioDepartment of Sports Sciences, University of Beira Interior, Covilhã, Portugal.
João P OliveiraDepartment of Sports Sciences, University of Beira Interior, Covilhã, Portugal.
Daniel A MarinhoDepartment of Sports Sciences, University of Beira Interior, Covilhã, Portugal.
Henrique P NeivaDepartment of Sports Sciences, University of Beira Interior, Covilhã, Portugal.
Jorge E MoraisResearch Centre for Active Living and WellBeing (LiveWell), Bragança, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to conduct a scoping and bibliometric review of articles using artificial intelligence (AI) in tennis. The analysis covered various aspects of tennis, including performance, health, match results, physiological data, tennis expenditure, and prize amounts. Articles on AI in tennis published until 2024 were retrieved from the Web of Science database. A total of 389 records were screened, and 108 articles were retained for analysis. The analysis identified intermittent gaps in publication output during certain intervals, notably in the years 2007-2008 and 2012-2013. From 2012 onward, there was a clear upward trend in publications and citations, peaking in 2022. The theme was led by China, the United States, and Australia. These countries maintain their status as the top contributors in terms of publications. The analysis of author collaborations revealed multiple clusters, with notable contributions from researchers in China, Australia, Japan, and the United States. This bibliometric review has elucidated the evolution of AI research in tennis, highlighting the countries and authors that have significantly contributed to this field over the years. The prediction model suggests that the number of articles and citations on this topic will continue to increase over the next decade (until 2034).

Indexed as

artificial intelligencedeep learningmachine learningsporttennis

Identifiers

PMID39763487
PMCPMC11701037

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.