How the FDA
regulates medical devices

An introduction on medical device classification, regulation, and how to get a new product to market in the U.S.

AI/ML Regulations

Guiding principles for AI/ML regulations

01

Multi-disciplinary expertise is leveraged throughout the total product life cycle

In-depth understanding of a model’s intended integration into clinical workflow, and the desired benefits and associated patient risks, can help ensure that ML-enabled medical devices are safe and effective and address clinically meaningful needs over the lifecycle of the device.

Number 1
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02

Good software engineering and security practices are implemented

Model design is implemented with attention to the “fundamentals”: good software engineering practices, data quality assurance, data management, and robust cybersecurity practices. These practices include methodical risk management and design process that can appropriately capture and communicate design, implementation, and risk management decisions and rationale, as well as ensure data authenticity and integrity.

03

Clinical study participants and data sets are representative of the intended patient population

Data collection protocols should ensure that the relevant characteristics of the intended patient population (for example, in terms of age, gender, sex, race, and ethnicity), use, and measurement inputs are sufficiently represented in a sample of adequate size in the clinical study and training and test datasets, so that results can be reasonably generalized to the population of interest.

Number 3
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04

Training data sets are independent of test sets

Training and test datasets are selected and maintained to be appropriately independent of one another. All potential sources of dependence, including patient, data acquisition, and site factors, are considered and addressed to assure independence.

05

Selected reference datasets are based upon best available methods

Accepted, best available methods for developing a reference dataset ensure that clinically relevant data are collected and the limitations of the reference are understood. Accepted reference datasets in model development and testing that promote and demonstrate model robustness and generalizability across the intended patient population are used.

Number 5
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06

Model design is tailored to the available data and reflects the intended use of the device

Model design is suited to the available data and supports the active mitigation of known risks, like overfitting, performance degradation, and security risks. The clinical benefits and risks related to the product are well understood, used to derive clinically meaningful performance goals for testing, and support that the product can safely and effectively achieve its intended use.

07

Focus is placed on the performance of the human-AI team

Where the model has a “human in the loop,” human factors considerations and the human interpretability of the model outputs are addressed with emphasis on the performance of the human-AI team, rather than just the performance of the model in isolation.

Number 7
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08

Testing demonstrates device performance during clinically relevant conditions

Statistically sound test plans are developed and executed to generate clinically relevant device performance information independently of the training data set. Considerations include the intended patient population, important subgroups, clinical environment and use by the Human-AI team, measurement inputs, and potential confounding factors.

09

Users are provided clear, essential information

Users are provided ready access to clear, contextually relevant information that is appropriate for the intended audience (such as health care providers or patients) including: the product’s intended use and indications for use, characteristics of the data used to train and test the model, known limitations, user interface interpretation, and clinical workflow integration of the model.

Number 9
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10

Deployed models are monitored for performance and re-training risks are managed

Deployed models have the capability to be monitored in “real world” use with a focus on maintained or improved safety and performance. When models are periodically or continually trained after deployment, there are appropriate controls in place to manage risks of overfitting, unintended bias, or degradation of the model that may impact the safety and performance of the model.

01

Multi-disciplinary expertise is leveraged throughout the total product life cycle

In-depth understanding of a model’s intended integration into clinical workflow, and the desired benefits and associated patient risks, can help ensure that ML-enabled medical devices are safe and effective and address clinically meaningful needs over the lifecycle of the device.

Number 1
02

Good software engineering and security practices are implemented

Model design is implemented with attention to the “fundamentals”: good software engineering practices, data quality assurance, data management, and robust cybersecurity practices. These practices include methodical risk management and design process that can appropriately capture and communicate design, implementation, and risk management decisions and rationale, as well as ensure data authenticity and integrity.

number 2
03

Clinical study participants and data sets are representative of the intended patient population

Data collection protocols should ensure that the relevant characteristics of the intended patient population (for example, in terms of age, gender, sex, race, and ethnicity), use, and measurement inputs are sufficiently represented in a sample of adequate size in the clinical study and training and test datasets, so that results can be reasonably generalized to the population of interest.

Number 3
04

Training data sets are independent of test sets

Training and test datasets are selected and maintained to be appropriately independent of one another. All potential sources of dependence, including patient, data acquisition, and site factors, are considered and addressed to assure independence.

Number 4
05

Selected reference datasets are based upon best available methods

Accepted, best available methods for developing a reference dataset ensure that clinically relevant data are collected and the limitations of the reference are understood. Accepted reference datasets in model development and testing that promote and demonstrate model robustness and generalizability across the intended patient population are used.

Number 5
06

Model design is tailored to the available data and reflects the intended use of the device

Model design is suited to the available data and supports the active mitigation of known risks, like overfitting, performance degradation, and security risks. The clinical benefits and risks related to the product are well understood, used to derive clinically meaningful performance goals for testing, and support that the product can safely and effectively achieve its intended use.

Number 6
07

Focus is placed on the performance of the human-AI team

Where the model has a “human in the loop,” human factors considerations and the human interpretability of the model outputs are addressed with emphasis on the performance of the Human-AI team, rather than just the performance of the model in isolation.

Number 7
08

Testing demonstrates device performance during clinically relevant conditions

Statistically sound test plans are developed and executed to generate clinically relevant device performance information independently of the training data set. Considerations include the intended patient population, important subgroups, clinical environment and use by the Human-AI team, measurement inputs, and potential confounding factors.

Number 8
09

Users are provided clear, essential information

Users are provided ready access to clear, contextually relevant information that is appropriate for the intended audience (such as health care providers or patients) including: the product’s intended use and indications for use, characteristics of the data used to train and test the model, known limitations, user interface interpretation, and clinical workflow integration of the model.

Number 9
10

Deployed models are monitored for performance and re-training risks are managed

Deployed models have the capability to be monitored in “real world” use with a focus on maintained or improved safety and performance. When models are periodically or continually trained after deployment, there are appropriate controls in place to manage risks of overfitting, unintended bias, or degradation of the model that may impact the safety and performance of the model.

Number 10
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