Evidence map›Paper›PMID 36005841›Full record

ArticleJournal of medical Internet research2022

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.

Paul Istasy, Wen Shen Lee, Alla Iansavichene, Ross Upshur, Bishal Gyawali, Jacquelyn Burkell, Bekim Sadikovic, Alejandro Lazo-Langner, Benjamin Chin-Yee

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 2 pooled it
–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

42 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  4. Digital oncology interventions and equity: a scoping review of design strategies to address cancer disparities.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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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

9 authors.

Paul IstasySchulich School of Medicine and Dentistry, Western University, London, ON, Canada.ORCID 0000-0003-2568-7542
Wen Shen LeeDepartment of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada.ORCID 0000-0002-6811-2493
Alla IansavicheneLibrary Services, London Health Sciences Centre, London, ON, Canada.ORCID 0000-0002-5651-1024
Ross UpshurDivision of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-1128-0557
Bishal GyawaliDivision of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada.ORCID 0000-0001-7444-8594
Jacquelyn BurkellFaculty of Information and Media Studies, Western University, London, ON, Canada.ORCID 0000-0003-2645-8127
Bekim SadikovicDepartment of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada.ORCID 0000-0001-6363-0016
Alejandro Lazo-LangnerDivision of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.ORCID 0000-0001-6869-8431
Benjamin Chin-YeeRotman Institute of Philosophy, Western University, London, ON, Canada.ORCID 0000-0003-0737-3603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.

objectiveWe aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?"

methodsFollowing PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.

resultsOf the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes.

conclusionsOur scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

Indexed as

Artificial IntelligenceHealth EquityHealthcare DisparitiesHealth Care SectorHumansIncomeartificial intelligencebiascancercancer epidemiologydigital healtheHealthepidemiologyglobal healthhealth disparityhealth equitymachine learningmobile phoneoncologypublic healthreviewscoping

Identifiers

PMID36005841
PMCPMC9667381

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.