Evidence map›Paper›PMID 40900839›Full record

ReviewWorld journal of methodology2025

Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.

İmran Metin, Öner Özdemir

Abstract readReview
In one paragraph

Review in World journal of methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

İmran MetinMedical Faculty, Sakarya University, Sakarya 54100, Türkiye.
Öner ÖzdemirDepartment of Pediatric Allergy and Immunology, Medical Faculty, Sakarya University, Sakarya Research and Training Hospital, Adapazarı 54100, Sakarya, Türkiye. ozdemir_oner@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, artificial intelligence (AI) usage and its benefits in medicine are reviewed in the oncology, radiology, and cardiology fields. The relevant literature was searched in PubMed and Google Scholar using the words "Artificial Intelligence", "Artificial Intelligence in Medicine", "Artificial Intelligence in Cardiology", "Artificial Intelligence in Oncology", and "Artificial Intelligence in Radiology" for the last 10 years. This article covers the AI's current implications in daily practice, discussing its advantages and disadvantages based on the findings. AI's effect in medicine for reducing workload, diagnosis, time management, and drug dosing is going to be reviewed especially in radiology, oncology, and cardiology fields as well as general usage of AI in addition to important highlights. Lastly, this minireview evaluates the current challenges of AI technology in medicine and how clinicians should work with this emerging technology.

Indexed as

Artificial intelligenceCardiologyDiagnosisManagementMedicineOncologyRadiology

Identifiers

PMID40900839
PMCPMC12400327

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.