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FDA fosters innovative approaches in research, resources and collaboration

Nature Machine Intelligence volume 4, pages 97–98 (2022)Cite this article

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To the Editor—We were pleased to read the December 2020 editorial in this journal, “Research, reuse, repeat”1. The editorial extolls the benefits of making tools (methods, data and code) available to others who can replicate the results and build on previously developed work. Here, we highlight three lines of activities in the US Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) that embody the spirit portrayed in the editorial. These activities include research, resources and collaborations that enable assessment of the safety and efficacy of FDA-regulated products, including products enabled by artificial intelligence and machine learning (AI/ML).

First, the FDA performs scientific research. Along with other research activities across the agency, the Office of Science and Engineering Laboratories (OSEL), the research arm of the CDRH, is focused on approximately 20 program areas covering a wide range of medical device technologies. OSEL scientists are charged with conducting regulatory research to accelerate product development and bring products to patients as rapidly as possible. Results of these efforts are disseminated in multiple ways, including through peer-reviewed publications and by incorporating research results into regulatory resources.

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References

  1. Nat. Mach. Intell. 2, 729 (2020).

  2. FDA CDRH (Center for Devices and Radiologic Health, US Food and Drug Administration). Software as a Medical Device (SAMD): Clinical Evaluation (FDA, 2017); https://www.fda.gov/media/100714/download

  3. FDA CDRH. Guidance for industry and FDA staff - Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Notification 510(k) Submissions (FDA, 2012); https://www.fda.gov/media/77635/download

  4. FDA CDRH. Guidance for industry and FDA staff - Clinical Performance Assessment: Considerations for Computer-assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification [510(K)] Submissions (FDA, 2020); https://www.fda.gov/media/77642/download

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  8. FDA CDRH. De Novo Request Evaluation of Automatic Class III Designation for Philips IntelliSite Pathology Solution (PIPS): Decision Summary (FDA, 2017); https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN160056.pdf

  9. FDA CDRH. Decision Summary for Paige Prostate, DEN200080 (2021); https://www.accessdata.fda.gov/cdrh_docs/pdf20/DEN200080.pdf

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Acknowledgements

The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.

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Authors and Affiliations

  1. FDA Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, USA

    Brandon D. Gallas, Aldo Badano, Katherine Elfer, Victor Garcia & Nicholas Petrick

  2. Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA

    Sarah Dudgeon

  3. Yale University, Biological and Biomedical Sciences, New Haven, CT, USA

    Sarah Dudgeon

  4. Massachusetts General Hospital/Harvard Medical School, Department of Pathology, Center for Integrated Diagnostics, Boston, MA, USA

    Jochen K. Lennerz

  5. Retired, FDA, Silver Spring, MD, USA

    Kyle Myers

  6. FDA Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Silver Spring, MD, USA

    Ed Margerrison

Authors
  1. Brandon D. Gallas
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  2. Aldo Badano
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  3. Sarah Dudgeon
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  4. Katherine Elfer
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  5. Victor Garcia
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  6. Jochen K. Lennerz
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  7. Kyle Myers
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  8. Nicholas Petrick
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  9. Ed Margerrison
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Corresponding authors

Correspondence to Brandon D. Gallas, Aldo Badano or Ed Margerrison.

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Gallas, B.D., Badano, A., Dudgeon, S. et al. FDA fosters innovative approaches in research, resources and collaboration. Nat Mach Intell 4, 97–98 (2022). https://doi.org/10.1038/s42256-022-00450-2

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