Regulating Flexibility for Artificial Intelligence FDA Experience with Predetermined Change Control Plans Article Swipe
Kyra L Rosen
,
Kenneth D. Mandl
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.26.25334477
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.26.25334477
Importance Predetermined Change Control Plans (PCCPs) are a recent regulatory innovation by the U.S. Food and Drug Administration (FDA) introduced to enable dynamic oversight of artificial intelligence and machine learning (AI/ML)-enabled medical devices. Objective To characterize FDA program of PCCPs among AI/ML-enabled medical devices, including device characteristics, preapproval testing, planned modifications, and post-clearance update mechanisms. Design This cross-sectional study reviewed FDA-cleared or approved AI/ML-enabled medical devices with authorized PCCPs. Setting AI/ML-enabled devices approved or cleared prior to May 30, 2025 were identified from an FDA-maintained public list and their characteristics extracted from FDA approval databases. Participants N/A Main Outcome(s) and Measure(s) Primary outcomes included (1) prevalence and characteristics of devices with authorized PCCPs, (2) types of FDA-authorized modifications, (3) presence and nature of preapproval testing, such as study design and subgroup testing, and (4) postmarket device update mechanisms and transparency. Results Among 26 identified AI/ML-enabled medical devices with authorized PCCPs, 92% were cleared via the 510(k) pathway, and all were classified as moderate risk. Devices were primarily intended for use in diagnosis or clinical assessment, and six had consumer-facing components. Authorized modifications spanned the product lifecycle, most commonly allowing model retraining (69% of devices), logic updates (42% of devices), and expansion of input sources (35% of devices). Preapproval testing was limited with seven devices prospectively evaluated and thirteen undergoing human factors testing. Subgroup analyses were reported for eleven devices and none included patient outcomes data. No postmarket studies or recalls were identified. User manuals could be identified online for 54% of devices, though many lacked performance details or mentioned PCCPs. Conclusions and Relevance FDA authorization of PCCPs grants manufacturers substantial flexibility to modify AI/ML-enabled devices postmarket, while preapproval testing and postmarket transparency are limited. These findings highlight the need for strengthened oversight mechanisms to ensure ongoing safety and effectiveness of rapidly evolving AI/ML-enabled technologies in clinical care.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.08.26.25334477
- https://www.medrxiv.org/content/medrxiv/early/2025/08/27/2025.08.26.25334477.full.pdf
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4413799671Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/2025.08.26.25334477Digital Object Identifier
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Regulating Flexibility for Artificial Intelligence FDA Experience with Predetermined Change Control PlansWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-27Full publication date if available
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Kyra L Rosen, Kenneth D. MandlList of authors in order
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https://doi.org/10.1101/2025.08.26.25334477Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2025/08/27/2025.08.26.25334477.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2025/08/27/2025.08.26.25334477.full.pdfDirect OA link when available
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Flexibility (engineering), Control (management), Computer science, Business, Artificial intelligence, Operations management, Process management, Engineering, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.included | 104, 232 |
| abstract_inverted_index.intended | 168 |
| abstract_inverted_index.learning | 30 |
| abstract_inverted_index.limited. | 284 |
| abstract_inverted_index.moderate | 163 |
| abstract_inverted_index.outcomes | 103, 234 |
| abstract_inverted_index.pathway, | 157 |
| abstract_inverted_index.presence | 120 |
| abstract_inverted_index.reported | 226 |
| abstract_inverted_index.reviewed | 60 |
| abstract_inverted_index.subgroup | 131 |
| abstract_inverted_index.testing, | 49, 125, 132 |
| abstract_inverted_index.testing. | 222 |
| abstract_inverted_index.thirteen | 218 |
| abstract_inverted_index.Objective | 34 |
| abstract_inverted_index.Relevance | 263 |
| abstract_inverted_index.devices), | 194, 199 |
| abstract_inverted_index.devices). | 207 |
| abstract_inverted_index.diagnosis | 172 |
| abstract_inverted_index.evaluated | 216 |
| abstract_inverted_index.expansion | 201 |
| abstract_inverted_index.extracted | 91 |
| abstract_inverted_index.highlight | 287 |
| abstract_inverted_index.including | 45 |
| abstract_inverted_index.mentioned | 259 |
| abstract_inverted_index.oversight | 24, 292 |
| abstract_inverted_index.primarily | 167 |
| abstract_inverted_index.Authorized | 181 |
| abstract_inverted_index.Importance | 1 |
| abstract_inverted_index.Measure(s) | 101 |
| abstract_inverted_index.Outcome(s) | 99 |
| abstract_inverted_index.artificial | 26 |
| abstract_inverted_index.authorized | 68, 112, 149 |
| abstract_inverted_index.classified | 161 |
| abstract_inverted_index.databases. | 95 |
| abstract_inverted_index.identified | 82, 144, 247 |
| abstract_inverted_index.innovation | 11 |
| abstract_inverted_index.introduced | 20 |
| abstract_inverted_index.lifecycle, | 186 |
| abstract_inverted_index.mechanisms | 138, 293 |
| abstract_inverted_index.postmarket | 135, 237, 281 |
| abstract_inverted_index.prevalence | 106 |
| abstract_inverted_index.regulatory | 10 |
| abstract_inverted_index.retraining | 191 |
| abstract_inverted_index.undergoing | 219 |
| abstract_inverted_index.Conclusions | 261 |
| abstract_inverted_index.FDA-cleared | 61 |
| abstract_inverted_index.Preapproval | 208 |
| abstract_inverted_index.assessment, | 175 |
| abstract_inverted_index.components. | 180 |
| abstract_inverted_index.flexibility | 271 |
| abstract_inverted_index.identified. | 242 |
| abstract_inverted_index.mechanisms. | 55 |
| abstract_inverted_index.performance | 256 |
| abstract_inverted_index.postmarket, | 276 |
| abstract_inverted_index.preapproval | 48, 124, 278 |
| abstract_inverted_index.substantial | 270 |
| abstract_inverted_index.Participants | 96 |
| abstract_inverted_index.characterize | 36 |
| abstract_inverted_index.intelligence | 27 |
| abstract_inverted_index.strengthened | 291 |
| abstract_inverted_index.technologies | 304 |
| abstract_inverted_index.transparency | 282 |
| abstract_inverted_index.AI/ML-enabled | 42, 64, 71, 145, 274, 303 |
| abstract_inverted_index.Predetermined | 2 |
| abstract_inverted_index.authorization | 265 |
| abstract_inverted_index.effectiveness | 299 |
| abstract_inverted_index.manufacturers | 269 |
| abstract_inverted_index.modifications | 182 |
| abstract_inverted_index.prospectively | 215 |
| abstract_inverted_index.transparency. | 140 |
| abstract_inverted_index.Administration | 18 |
| abstract_inverted_index.FDA-authorized | 117 |
| abstract_inverted_index.FDA-maintained | 85 |
| abstract_inverted_index.modifications, | 51, 118 |
| abstract_inverted_index.post-clearance | 53 |
| abstract_inverted_index.(AI/ML)-enabled | 31 |
| abstract_inverted_index.characteristics | 90, 108 |
| abstract_inverted_index.consumer-facing | 179 |
| abstract_inverted_index.cross-sectional | 58 |
| abstract_inverted_index.characteristics, | 47 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.46365799 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |