Evidence map›Paper›PMID 37361549›Full record

ReviewJournal of medical imaging (Bellingham, Wash.)2023

Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges.

Nicholas Petrick, Weijie Chen, Jana G Delfino, Brandon D Gallas, Yanna Kang, Daniel Krainak, Berkman Sahiner, Ravi K Samala

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Review
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  15. The Frontiers of Smart Healthcare Systems.Healthcare (Basel, Switzerland) · 2024
    Review
  16. Review
  17. Review
  18. Review
  19. Review
  20. AI in imaging: the regulatory landscape.The British journal of radiology · 2024
    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

8 authors.

Nicholas PetrickU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.ORCID https://orcid.org/0000-0001-5167-8899
Weijie ChenU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.
Jana G DelfinoU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.ORCID https://orcid.org/0000-0001-9957-2866
Brandon D GallasU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.ORCID https://orcid.org/0000-0001-7332-1620
Yanna KangU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Product Evaluation and Quality, Silver Spring, Maryland, United States.
Daniel KrainakU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Product Evaluation and Quality, Silver Spring, Maryland, United States.
Berkman SahinerU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.
Ravi K SamalaU.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, Maryland, United States.ORCID https://orcid.org/0000-0002-6661-4801

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities. Approach: AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.S. Food and Drug Administration (FDA) regulatory concepts, processes, and fundamental assessments for a wide range of medical imaging AI/ML device types. Results: The device type for an AI/ML device and appropriate premarket regulatory pathway is based on the level of risk associated with the device and informed by both its technological characteristics and intended use. AI/ML device submissions contain a wide array of information and testing to facilitate the review process with the model description, data, nonclinical testing, and multi-reader multi-case testing being critical aspects of the AI/ML device review process for many AI/ML device submissions. The agency is also involved in AI/ML-related activities that support guidance document development, good machine learning practice development, AI/ML transparency, AI/ML regulatory research, and real-world performance assessment. Conclusion: FDA's AI/ML regulatory and scientific efforts support the joint goals of ensuring patients have access to safe and effective AI/ML devices over the entire device lifecycle and stimulating medical AI/ML innovation.

Indexed as

AI/MLassessment methodsmedical imagingregulatory conceptsregulatory science

Identifiers

PMID37361549
PMCPMC10289177

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.