Evidence map›Paper›PMID 33960708›Full record

ReviewCancer medicine2021

Artificial intelligence in oncology: Path to implementation.

Isaac S Chua, Michal Gaziel-Yablowitz, Zfania T Korach, Kenneth L Kehl, Nathan A Levitan, Yull E Arriaga, Gretchen P Jackson, David W Bates, Michael Hassett

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
80citing papers in PubMed, 6 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

80 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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20 more citing papers are in PubMed but not listed here.

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.

Isaac S ChuaDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID 0000-0002-8404-8452
Michal Gaziel-YablowitzDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Zfania T KorachDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Kenneth L KehlHarvard Medical School, Boston, MA, USA.ORCID 0000-0001-5339-9797
Nathan A LevitanIBM Watson Health, Cambridge, MA, USA.
Yull E ArriagaIBM Watson Health, Cambridge, MA, USA.ORCID 0000-0002-7229-2028
Gretchen P JacksonIBM Watson Health, Cambridge, MA, USA.
David W BatesDivision of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Michael HassettHarvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high-value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user-design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.

Indexed as

Artificial IntelligenceBiasData CollectionDecision Support Systems, ClinicalHumansImage Interpretation, Computer-AssistedMachine LearningMedical OncologyPrecision MedicineResearch Reportartificial intelligencedeep learningmachine learningoncology

Identifiers

PMID33960708
PMCPMC8209596

What OpenQuestion holds

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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.