Evidence map›Paper›PMID 39753817›Full record

ReviewCurrent oncology reports2025

Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap.

Irene Dankwa-Mullan, Kingsley Ndoh, Darlington Akogo, Hermano Alexandre Lima Rocha, Sérgio Ferreira Juaçaba

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current oncology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

Irene Dankwa-MullanMilken Institute School of Public Health, Department of Health Policy and Management, George Washington University, Washington D.C., USA. Irene.Dankwamullan@gwu.edu.
Kingsley NdohHurone AI, Seattle, Washington, USA.
Darlington AkogoAshongman Estates, Accra, Ghana.
Hermano Alexandre Lima RochaCeará Cancer Institute, Ceará, Brazil.
Sérgio Ferreira JuaçabaCeará Cancer Institute, Ceará, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThis review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes. RECENT

findingsRecent studies demonstrate significant advancements in AI, such as deep learning for cancer diagnosis and predictive analytics for personalized treatment, showing potential for improved precision in care. However, concerns persist about the performance of AI tools across diverse populations due to biased training data. Access to AI technologies also remains limited, particularly in low-income and rural settings. AI holds promise for advancing cancer care, but its current application risks exacerbating existing health disparities. To ensure AI benefits all populations, future research must prioritize inclusive datasets, integrate social determinants of health, and develop ethical frameworks. Addressing these challenges is crucial for AI to contribute positively to cancer health equity and guide future research and policy development.

Indexed as

Artificial IntelligenceHealth EquityNeoplasmsHealthcare DisparitiesHumansAI biasArtificial intelligenceCancer health equityHealth disparitiesPrecision medicinePredictive analytics

Identifiers

PMID39753817

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.