Evidence map›Paper›PMID 36342705›Full record

ReviewJAMA internal medicine2022

Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations.

Kunal C Potnis, Joseph S Ross, Sanjay Aneja, Cary P Gross, Ilana B Richman

Registry-linked trialAbstract readReview
In one paragraph

Review in JAMA internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06401434 (EVALUATE), which is not on this map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing 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.

NCT06401434 nacompletednot on this mapstarted 2024, after this paper: background citation

EVALUATE: Estimating the Value-Add of Lunit Software's Use to Accelerate TB Elimination

TypeinterventionalSponsorFreundeskreis Für Internationale Tuberkulosehilfe e.VRan2024 to 2025Enrolled23,835ConditionsTuberculosisArmsOn-site radiologist, CAD-based triage (with INSIGHT CXR software)
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Pooled it
  3. Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Review
  4. Article
  5. Review
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  7. Review
  8. A guide to cancer screening.Nature reviews. Clinical oncology · 2026
    Review
  9. Article
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  11. Review
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  16. Article
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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.

Kunal C PotnisYale School of Medicine, New Haven, Connecticut.
Joseph S RossSection of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.
Sanjay AnejaCenter for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut.
Cary P GrossSection of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.
Ilana B RichmanSection of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.

Funding

Short-Term Reserch Training: Students in Health SchoolsT35HL007649 · NHLBI · YALE UNIVERSITY · PI Sarwat I Chaudhry, Erica L Herzog · 1987 to 2026
$5.7M
Leveraging Physician Networks to Improve Care and Outcomes for Low-Income PopulationsR01HL144644 · NHLBI · YALE UNIVERSITY · PI NDUMELE, CHIMA · 2019 to 2021
$1.6M
Supporting Personalized Decision Making for Breast Cancer Screening among Older WomenK08CA248725 · NCI · YALE UNIVERSITY · PI RICHMAN, ILANA · 2021 to 2025
$1.3M
AHRQ HHS R01 HS022882AHRQ HHS R01 HS025164FDA HHS U01 FD005938NCI NIH HHS K08 CA248725NHLBI NIH HHS R01 HL144644NHLBI NIH HHS T35 HL007649
6 · The paper itself

Abstract

Importance: Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain. Objectives: To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system. Evidence Review: Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021. Findings: Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices. Conclusions and Relevance: The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.

Indexed as

Breast NeoplasmsDevice ApprovalArtificial IntelligenceEarly Detection of CancerFemaleHumansMulticenter Studies as TopicRetrospective StudiesUnited StatesUnited States Food and Drug Administration

Identifiers

PMID36342705
PMCPMC10623674

What OpenQuestion holds

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Registered trials

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.