Evidence map›Paper›PMID 40926431›Full record

ReviewComputer methods and programs in biomedicine2025

The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.

Oluwatunmise Akinniyi, Jose Dixon, Joseph Aina, Francesca Weaks, Gehad A Saleh, Md Mahmudur Rahman, Timothy Meeker, Hari Trivedi, Judy Wawira Gichoya, Fahmi Khalifa

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computer methods and programs in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Oluwatunmise AkinniyiElectrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA.
Jose DixonElectrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA.
Joseph AinaElectrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA.
Francesca WeaksMaryland Center for Health Equity, University of Maryland, College Park, MD, 20742, USA.
Gehad A SalehDepartment of Diagnostic and Interventional Radiology, Mansoura University, Mansoura, 35516, Egypt.
Md Mahmudur RahmanDepartment of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, 21251, USA.
Timothy MeekerBiology Department, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, 21251, USA.
Hari TrivediDepartment of Radiology, Emory University, Atlanta, GA, 21251, USA.
Judy Wawira GichoyaDepartment of Radiology, Emory University, Atlanta, GA, 21251, USA.
Fahmi KhalifaElectrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA. Electronic address: fahmi.khalifa@morgan.edu.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
NIH HHS OT2 OD032581
6 · The paper itself

Abstract

Breast Cancer (BC) remains a leading cause of morbidity and mortality among women globally, accounting for 30% of all new cancer cases (with approximately 44,000 women dying), according to recent American Cancer Society reports. Therefore, accurate BC screening, diagnosis, and classification are crucial for timely interventions and improved patient outcomes. The main goal of this paper is to provide a comprehensive review of the latest advancements in BC detection, focusing on diagnostic BC imaging, Artificial Intelligence (AI) driven analysis, and health disparity considerations. We first examine diverse imaging techniques such as Mammography, Ultrasound, and Dynamic Contrast-Enhanced Magnetic Resonance Imaging, and provide an overview of their pros and cons. Then, we provided an intensive review of the State-of-the-Art (SOTA) literature on the role of AI in BC classification and segmentation. Lastly, we examined the role of AI in BC health disparities. A key contribution of this work lies in its integrative approach, consolidating insights from multiple research areas, imaging methods, AI-driven methodologies, and health disparities in a single resource. This paper evaluates the effectiveness of modern AI-based tools in enhancing diagnostic accuracy and discusses their potential to address biases in BC diagnosis, thus promoting equitable healthcare access. By integrating clinical, technical, and equity perspectives, this review aims to inform real-world decision-making, supporting the development of bias-aware AI tools, guiding equitable screening policy, and enhancing clinical practice in breast cancer care. Additionally, our critical analysis and discussion of recent SOTA highlights the strengths, limitations, and knowledge gaps for future directions of AI roles in BC. In total, these findings and future venue suggestions serve as a practical reference for researchers, clinicians, and policymakers, underscoring the need for interdisciplinary collaboration to harness AI's full potential in BC diagnosis and reduce global health disparities.

Indexed as

Artificial IntelligenceBreast NeoplasmsHealthcare DisparitiesEarly Detection of CancerFemaleHumansMagnetic Resonance ImagingMammographyBreast cancerHealth disparityMammogramsMulti-modal

Identifiers

PMID40926431
PMCPMC12462113

What Socratic 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 Socratic graph.