FDA change plans in radiology AI - Summary - MDSpire
Coming Soon: Introducing MDSpire News. Learn more
Conexiant’s news site is now MDSpire News. Learn more

FDA change plans in radiology AI

  • By

  • Kathryn Wighton

  • September 16, 2026

  • 3 min

Share

Objective:

To evaluate the adoption of Predetermined Change Control Plans (PCCPs) among FDA-cleared radiology AI/ML devices and assess the transparency of public documentation.

Approach:
  • Study Design: A systematic scoping review was conducted following PRISMA guidelines, registered in PROSPERO, evaluating FDA-cleared radiology AI/ML devices from 2015 to 2025.
  • Data Sources: FDA 510(k), de novo, premarket approval, and breakthrough device databases were accessed on April 1, 2026, with manual verification of PCCP devices against regulatory summaries.
  • Assessment Method: PCCP documentation was evaluated using an 8-point rubric covering specificity, data and validation transparency, real-world evaluation, and postmarket monitoring.
Key Findings:
  • Out of 1,394 FDA-listed AI/ML submissions, 1,080 (78%) were in radiology, with 1,068 (99%) cleared through the 510(k) pathway.
  • Among 870 unique radiology devices, 130 (15%) underwent sequential 510(k) clearances for significant modifications.
  • The mean interval between sequential submissions decreased from 25 months prior to 2021 to 13 months post-2021.
  • Of 37 radiology devices cleared with PCCPs, 34 (92%) were AI/ML devices, with 22 cleared in 2025.
  • Public documentation had a mean score of 5 on the 8-point rubric, with only 3 devices describing postmarket surveillance.
  • Mean documentation scores increased from 4 in 2024 to 5 in 2025.
Interpretation:

The analysis indicates an increase in the adoption of PCCPs among radiology AI devices, but public documentation often lacks comprehensive details on postmarket performance monitoring.

Limitations:
  • Analysis limited by publicly available data, with internal safety assessments and minor software changes inaccessible.
  • Database inconsistencies and challenges in deduplicating devices due to corporate rebranding.
  • The transparency rubric used was not designed as a comprehensive quality evaluation.
  • The recency of final FDA guidance may limit the analysis.
Conclusion:

The study highlights the need for improved transparency in public documentation regarding postmarket monitoring of radiology AI devices.

Sources:

Original Source(s)

Related Content