Evidence map›Paper›PMID 39664363›Full record

ArticlePulmonary medicine2024

Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.

Javier De La Hoz-M, Karime Montes-Escobar, Viorkis Pérez-Ortiz

Abstract read
In one paragraph

Article in Pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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.

Javier De La Hoz-MFaculty of Engineering, Universidad del Magdalena, Santa Marta, Colombia.ORCID https://orcid.org/0000-0001-7779-0803
Karime Montes-EscobarDepartamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador.ORCID https://orcid.org/0000-0002-9555-0392
Viorkis Pérez-OrtizFacultad Ciencias de la Salud, Carrera de Medicina, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador.ORCID https://orcid.org/0000-0002-5921-2484

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence (AI) has begun to play a crucial role in improving diagnostic and treatment methods. It is crucial to understand how AI has integrated into LC research and to identify the main areas of focus. The aim of the study was to provide an updated insight into the role of AI in LC research, analyzing evolving topics, geographical distribution, and contributions to journals. The study explores research trends in AI applied to LC through a novel approach combining latent Dirichlet allocation (LDA) topic modeling with the HJ-Biplot statistical technique. A growing interest in AI applications in LC oncology was observed, reflected in a significant increase in publications, especially after 2017, coinciding with the availability of computing resources.

Indexed as

Artificial IntelligenceLung NeoplasmsBiomedical ResearchHumanslung cancermachine learningtopic modeling

Identifiers

PMID39664363
PMCPMC11634404

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Registered trials

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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.