Medical devices # AI-enabled medical devices: what FDA's list and guidance show By QLogix · Published 12 October 2026 · 4 min read FDA's public list counted 1,614 AI-enabled medical devices through June 2026, about three in four of them in radiology. Two documents shape how they are built and changed: the final guidance on predetermined change control plans and the draft guidance on lifecycle management. AI-enabled devices on FDA's list through June 2026. 1,614 Share of those authorizations in radiology (1,230 devices). 76% AI-enabled devices authorized in the second quarter of 2026. 89 ## Key takeaways - FDA's list of AI-enabled medical devices reached 1,614 through June 2026, according to an analysis of the list by The Imaging Wire. FDA says the list is not comprehensive. - Radiology accounts for about three in four authorizations, a share that has stayed between 73% and 80% a year since 2023. - A predetermined change control plan lets a manufacturer make the model changes described in the plan without a new marketing submission for each one. - The draft lifecycle guidance of January 2025 sets out what a marketing submission for an AI-enabled device should contain. It was still a draft when this article was last updated. ## How many AI-enabled medical devices has FDA authorized? FDA publishes a list of AI-enabled medical devices that have been authorized for marketing in the United States and updates it periodically. An analysis of the list published in September 2026 counted 1,614 devices through the end of June 2026, up 5.9% on the first quarter. FDA authorized 92 AI-enabled devices in the first quarter of 2026 and 89 in the second. FDA describes the list as a resource for the public and not a comprehensive record. Devices are identified from AI-related terms in the public authorization summaries, so a device that uses AI without saying so in its summary may be missing. ## Which specialties do they serve? Radiology dominates. Of the 1,614 devices, 1,230 are radiology devices, which is 76%. Image analysis suits machine learning: the input is digital, large labelled datasets exist, and the output can be checked against a reader. Cardiovascular and neurology devices follow at a distance. Radiology's share of FDA's AI-enabled device authorizations - 202380 % - 202473 % - 202575 % - Q1 202692 devices authorized75 % - Q2 202689 devices authorized74 % Share of AI-enabled device authorizations that were radiology devices, by period. Source: The Imaging Wire analysis of FDA's AI-enabled medical device list, September 2026. ## What is a predetermined change control plan? A predetermined change control plan, or PCCP, is a part of a marketing submission in which the manufacturer describes the modifications it intends to make to an AI-enabled device software function after authorization, and how it will make them. Congress gave FDA the authority to accept such plans in 2022, in section 515C of the Federal Food, Drug, and Cosmetic Act. FDA's guidance on PCCPs for AI-enabled device software functions is final; it was first issued in December 2024 and the current version is dated August 2025. Once FDA has authorized a device with a PCCP, the manufacturer can implement the changes the plan covers without a new submission for each one, provided it follows the plan. The three parts of a PCCP - 1 Description of modifications The specific, planned changes to the model - 2 Modification protocol Data management, retraining, performance evaluation and update procedures - 3 Impact assessment Benefits and risks of the planned changes, and how the risks are mitigated The components FDA's final guidance recommends for a predetermined change control plan. ## What does the draft lifecycle guidance ask for? In January 2025 FDA published the draft guidance Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations. It takes a total product life cycle view and lists what a marketing submission should contain. - A device description that says what the AI does, its inputs and outputs, and who uses it. - User interface and labelling that tell the user what the model was trained on, how it performs and where its limits are. - A risk assessment that covers AI-specific risks, including bias and performance that varies between patient groups. - Data management: how training and test data were collected, kept separate and shown to represent the intended population. - Model description, development and validation, with performance broken down by relevant subgroups. - A plan for monitoring performance after the device is on the market. ## Where do robotics fit in? Robotically-assisted surgical devices are regulated as medical devices in their own right, and a growing number add AI features such as image guidance and motion planning. When they do, the same expectations apply to the AI function: a defined intended use, evidence of performance, transparency to the user and a plan for changes. On the manufacturing side, device makers now work under the Quality Management System Regulation, which took effect on 2 February 2026 and incorporates ISO 13485:2016. Software used in production and in the quality system, AI-enabled or not, is validated for its intended use with effort proportionate to risk. ## What should a device quality team do now? - Decide early whether the model will change after launch. If it will, plan the PCCP with the first submission instead of retrofitting it. - Treat training and test data as controlled records, with provenance, separation and representativeness documented. - Build post-market performance monitoring into the quality system, with complaint handling and CAPA connected to it. - Apply the ten Good Machine Learning Practice guiding principles that FDA, Health Canada and the MHRA published in 2021 as a design checklist. ## Common questions ### How many AI-enabled medical devices has FDA authorized? FDA's public list counted 1,614 AI-enabled medical devices through June 2026, according to an analysis published by The Imaging Wire in September 2026. About 76% are radiology devices. FDA notes the list is not comprehensive. ### Is the PCCP guidance for AI-enabled devices final? Yes. FDA's guidance on marketing submission recommendations for a predetermined change control plan for AI-enabled device software functions is final. It was first issued in December 2024 and the current version is dated August 2025. ### Can an AI-enabled device learn continuously after it is authorized? Changes to the model need to be covered by an authorized predetermined change control plan or by a new marketing submission where one is required. A PCCP describes the planned changes, the protocol for making them and their impact in advance. ## Sources - FDA: Artificial Intelligence-Enabled Medical Devices (the list) - The Imaging Wire, 13 September 2026: analysis of FDA's AI-enabled device list through June 2026 - FDA final guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions - FDA draft guidance: Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations (January 2025) - FDA: Quality Management System Regulation (QMSR) This article is general information for quality and regulatory professionals. It is not regulatory or legal advice. Check the source documents for their current status before acting on them. ## Topics - AI-enabled devices - PCCP - SaMD - Radiology AI - Surgical robotics - QMSR ## How QLogix helps - QMS digitization - Validation of AI-enabled systems All articles ## More from the blog - AI in quality · 12 October 2026FDA's seven-step credibility framework for AI, in plain termsFDA's draft guidance asks sponsors to show that an AI model is credible for a specific context of use. The effort scales with model risk: how much the model's output drives the decision, and how serious a wrong decision would be. - Manufacturing science · 12 October 2026AI in drug manufacturing: where FDA stands and what to build forFDA has no final guidance written only for AI in drug manufacturing. It has a 2023 discussion paper naming five open areas and a 2025 draft framework that covers manufacturing models. Together they show what a process model has to prove. - Manufacturing science · 12 October 2026Robotics in aseptic manufacturing: what Annex 1 expectsThe revised EU GMP Annex 1 names robotic systems, with isolators and RABS, as technologies to consider for protecting sterile products. Taking the operator out of the critical zone removes the largest contamination source and moves the quality work to design, software and data. ## Talk it through with someone who has run a plant Bring your backlog, your systems map or one investigation that keeps coming back. We will tell you plainly what we would do and what we would leave alone. Request a meeting Email us In the first meeting we cover - Your current QMS and how it connects to MES, LIMS and ERP - Where deviations, CAPAs and change controls slow down - Whether a readiness assessment makes sense, and what it would cover --- Source: https://qlogix.ai/blog/ai-enabled-medical-devices-fda QLogix · contact@qlogix.ai · +17146639012