Discussion on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep" by Jean Feng, Scott Emerson, and Noah Simon.

Szczegóły
Comments

Tytuł:: Discussion on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep" by Jean Feng, Scott Emerson, and Noah Simon.
Autorzy:: Pennello G; Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Sahiner B; Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Gossmann A; Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Petrick N; Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Źródło:: Biometrics [Biometrics] 2021 Mar; Vol. 77 (1), pp. 45-48. Date of Electronic Publication: 2020 Oct 11.
Typ publikacji:: Journal Article; Comment
Język:: English
Imprint Name(s):: Publication: Alexandria Va : Biometric Society
Original Publication: Washington.
MeSH Terms:: Machine Learning*
Software*
Policy
References:: Biggerstaff, B.J. (2000) Comparing diagnostic tests: a simple graphic using likelihood ratios. Statistics in Medicine, 19, 649-663.
Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O. and Roth, A. (2015) Statistics. The reusable holdout: Preserving validity in adaptive data analysis. Science, 349, 636-638.
Gossmann, A., Pezeshk, A. and Sahiner, B. (2018) Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed ‘test’ dataset and a potential solution. In: Nishikawa, R.M., Samuelson, F.W., (Eds.) Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 07 March 2018, Vol. 10577, pp. 105770K-105770K-12. Bellingham, WA: SPIE, the International Society for Optics and Photonics.
Hu, N., Huang, L. and Tiwari, R.C. (2015) Signal detection in FDA AERS database using Dirichlet process. Statistics in Medicine, 34, 2725-2742.
Lee, C.S. and Lee, A.Y. (2020) Clinical applications of continual learning machine learning. The Lancet Digital Health, 2, e279x02013;e281.
Roelofs, R., Shankar, V., Recht, B., Fridovich-Keil, S., Hardt, M., Miller, J. and Schmidt, L. (2019) A Meta-analysis of overfitting in machine learning. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K.L. and Cesa-Bianchi, N.C. (Eds.) Advances in Neural Information Processing Systems 32. Red Hook, NY: Curran Associates, Inc., pp. 9175-9185.
U.S. Food and Drug Administration (2012) Computer-assisted detection devices applied to radiology images and radiology device data - Premarket notification [510(k)] submissions. https://www.fda.gov/media/77635/download [Accessed 1 June 2020].
U.S. Food and Drug Administration (2019a) Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-Based software as a medical device (SaMD)-discussion paper and request for feedback. https://www.fda.gov/media/122535/download [Accessed: 1 June 2020].
U.S. Food and Drug Administration (2019b) Statement on agency's efforts to increase transparency in medical device reporting. https://www.fda.gov/news-events/press-announcements/statement-agencys-efforts-increase-transparency-medical-device-reporting [Accessed: 1 June 2020].
U.S. Food and Drug Administration (2020a) Clinical performance assessment: considerations for computer-assisted detection devices applied to radiology images and radiology device data in premarket notification (510(k)) submissions: guidance for industry and FDA staff. https://www.fda.gov/media/77642/download [Accessed: 1 June 2020].
U.S. Food and Drug Administration (2020b) Premarket notification 510(k). https://www.fda.gov/medical-devices/premarket-submissions/premarket-notification-510k [Accessed: 1 June 2020].
Entry Date(s):: Date Created: 20201011 Date Completed: 20210719 Latest Revision: 20210719
Update Code:: 20240105
DOI:: 10.1111/biom.13381
PMID:: 33040332
: Czasopismo naukowe

Pełny tekst

Comment on: Biometrics. 2021 Mar;77(1):31-44. (PMID: 32981103)
Comment in: Biometrics. 2021 Mar;77(1):52-53. (PMID: 33040357)

Informacja