Networked Radiology Imaging and Treatment Planning Systems and FDA Cybersecurity Regulation: A Difference-in-Differences Analysis of 510(k) Clearance Delays.
The objective of this study is to determine whether 510(k) clearance duration for AI-enabled devices changed differentially relative to the general market around October 1, 2023—the date FDA began Refuse-to-Accept enforcement of Section 524B cybersecurity documentation completeness—using a freshly r...
| Publicado en: | Journal of Imaging Informatics in Medicine pp. 1 - 10 |
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| Autor principal: | |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196849861&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196849861 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Sep2026 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196849861 10.1007/s10278-026-02253-y 196849861 ppf: 1 ppct: 9 formats: tig: atl: Networked Radiology Imaging and Treatment Planning Systems and FDA Cybersecurity Regulation: A Difference-in-Differences Analysis of 510(k) Clearance Delays. aug: au: Lee, Chuelwon affil: RAQA Team, HUINNO Co., Ltd. sug: ab: The objective of this study is to determine whether 510(k) clearance duration for AI-enabled devices changed differentially relative to the general market around October 1, 2023—the date FDA began Refuse-to-Accept enforcement of Section 524B cybersecurity documentation completeness—using a freshly re-extracted, complete FDA 510(k) dataset. We analyzed 83,675 FDA 510(k) records (De Novo excluded; decisions dated January 2000 through August 2, 2026). AI-enabled codes were identified via FDA’s officially published AI-Enabled Medical Device List (26 codes, <italic>N</italic> = 7070). Log-transformed clearance duration was modeled with a difference-in-differences (DiD) regression (product code–clustered standard errors, adjusted for submission type and review pathway); a half-year event study specification tested parallel pre-rule trends and timing, and Bonferroni-corrected Mann–Whitney tests (15 comparisons) assessed code-level heterogeneity; AI-enabled status was assigned at the product code level and was not confirmed at the individual-submission level. After adjustment, the overall market showed no significant post-rule change (Post coefficient = 0.029, <italic>p</italic> = 0.141), while the AI-enabled cohort showed a robust increase (AI × Post = 0.212, <italic>p</italic> < 0.001; ≈24% longer). The two half-years nearest the cutoff showed no significant pre-rule trend, though the most distant pre-rule half-year (six half-years prior) showed a significant negative coefficient, an anomaly discussed in Limitations; the AI x Post effect itself became significant approximately 24 months post-implementation. Five codes—diagnostic ultrasound (IYN), CT scanners (JAK), radiology image processing software (LLZ), angiography/fluoroscopy systems (OWB), and radiotherapy-planning software (MUJ)—were Bonferroni significant (all <italic>p</italic> < 0.001) and together drove the aggregate effect; all five are radiology panel codes. The highest-volume AI code (QIH) showed no significant change after correction. The observed clearance time increase for AI-enabled submissions is associated with, and concentrated in, networked radiology panel imaging and treatment planning systems rather than AI-enabled software broadly, and it accumulates over roughly 6 to 24 months rather than appearing immediately. Because AI-enabled status is defined at the product code level, submissions in the control group may include non-AI cyber devices also subject to the cybersecurity rule, and the concurrent mandatory eSTAR transition is not separately identified; these findings should therefore be read as an association warranting device category–specific, rather than blanket AI/ML, regulatory attention, not as established evidence that Section 524B specifically caused the observed delay. pubtype: Academic Journal doctype: Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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