Evidence map›Paper›PMID 40207818›Full record

ReviewCurrent pharmaceutical design2025

The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.

Matineh Behzadi, Anahita Azinfar, Hawraa Ibrahim Alshakarchi, Yeganeh Khazaei, Ibrahim Saeed Gataa, Gordon A Ferns, Hamid Naderi, Amir Avan, Hamid Fiuji, Masoud Pezeshki Rad

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current pharmaceutical design, 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

10 authors.

Matineh BehzadiMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Anahita AzinfarMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Hawraa Ibrahim AlshakarchiMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Yeganeh KhazaeiMedical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Ibrahim Saeed GataaCollege of Medicine, University of Warith Al-Anbiyaa, Karbala, 56001, Iraq.
Gordon A FernsDepartment of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, Sussex BN1 9PH, United Kingdom.
Hamid NaderiMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Amir AvanMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Hamid FiujiDepartment of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU, University Medical Center (VUMC), Amsterdam, The Netherlands.
Masoud Pezeshki RadDepartment of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.

Indexed as

Artificial IntelligenceBreast NeoplasmsFemaleHumansNeural Networks, Computerartificial intelligenceBreast cancerclinical diagnosisconvolutional neural networks.histopathological datamammographic data

Identifiers

PMID40207818

What OpenQuestion holds

Textmetadata
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.