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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.20498 |
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| _version_ | 1866913672365867008 |
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| author | González, Camila Fuchs, Moritz Santos, Daniel Pinto dos Matthies, Philipp Trenz, Manuel Grüning, Maximilian Chaudhari, Akshay Larson, David B. Othman, Ahmed Kim, Moon Nensa, Felix Mukhopadhyay, Anirban |
| author_facet | González, Camila Fuchs, Moritz Santos, Daniel Pinto dos Matthies, Philipp Trenz, Manuel Grüning, Maximilian Chaudhari, Akshay Larson, David B. Othman, Ahmed Kim, Moon Nensa, Felix Mukhopadhyay, Anirban |
| contents | Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Regulating radiology AI medical devices that evolve in their lifecycle González, Camila Fuchs, Moritz Santos, Daniel Pinto dos Matthies, Philipp Trenz, Manuel Grüning, Maximilian Chaudhari, Akshay Larson, David B. Othman, Ahmed Kim, Moon Nensa, Felix Mukhopadhyay, Anirban Computers and Society Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms. |
| title | Regulating radiology AI medical devices that evolve in their lifecycle |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2412.20498 |