Findings
The traditional medical device regulatory paradigm is not designed for the adaptive nature of AI/ML technologies, which can learn and change after they are on the market. A key benefit of AI/ML is its ability to improve performance by learning from real-world data, but this also presents a unique regulatory challenge. To ensure patient safety and device effectiveness, a new, flexible regulatory framework is required that can accommodate these iterative improvements. Transparency and robust monitoring are essential to manage the risks associated with evolving algorithms.

Recommendations
The FDA proposes a "Predetermined Change Control Plan" (PCCP) to be included in premarket submissions. This plan would specify the anticipated modifications to the device (the "what") and the methodology for implementing and validating those changes (the "how"). The development of "Good Machine Learning Practice" (GMLP) is encouraged to ensure that AI/ML algorithms are developed and validated using best practices. Manufacturers should implement robust real-world performance monitoring to ensure that their devices remain safe and effective after deployment.

Regulatory Considerations
The FDA is developing a new regulatory framework tailored to the unique aspects of AI/ML-based SaMD, which will leverage a TPLC approach. The agency has issued an "AI/ML SaMD Action Plan" that outlines its multi-pronged approach, including issuing draft guidance on PCCPs and promoting the harmonization of GMLP. The FDA is actively collaborating with stakeholders to foster innovation while ensuring patient safety. The agency maintains a public list of authorized AI/ML-enabled medical devices to enhance transparency.

Findings
AI-enabled medical devices require robust risk assessment to address data drift, bias, and transparency challenges.
The total product lifecycle (TPLC) approach is essential for managing AI-enabled devices, ensuring continuous oversight and updates.
There is a need for improved standardization in AI model validation and performance monitoring to ensure consistency in regulatory submissions.
Effective data management practices, including dataset representativeness and bias control, are critical for AI model development.
Cybersecurity vulnerabilities in AI-enabled medical devices must be proactively addressed to prevent risks to patient safety and data integrity.

Recommendations
AI-enabled device manufacturers should integrate Good Machine Learning Practice (GMLP) principles throughout the device lifecycle.
Marketing submissions should include comprehensive documentation of AI model development, validation, and performance monitoring.
Developers should implement transparency measures, such as model interpretability and explainability, to enhance user trust and understanding.
AI models must undergo rigorous bias evaluation to ensure equitable performance across diverse patient populations.
A predetermined change control plan (PCCP) should be established to allow safe and effective AI model updates post-market without additional FDA submissions.

Regulatory Considerations
FDA encourages early engagement through the Q-Submission Program for AI-enabled device manufacturers.
Compliance with FDA-recognized consensus standards, such as ANSI/AAMI/ISO 14971 for risk management, is recommended.
AI-enabled devices must meet labeling requirements, ensuring that users clearly understand model inputs, outputs, and performance metrics.
Post-market surveillance and continuous monitoring of AI model performance are necessary to ensure ongoing safety and effectiveness.
Cybersecurity measures must be included in regulatory submissions, detailing safeguards against data breaches and unauthorized model modifications.

Findings
The use of Artificial Intelligence (AI) and Machine Learning (ML) is being applied to a broad range of drug development activities with the potential to accelerate the process and make clinical trials safer and more efficient. The inclusion of AI/ML is most common in the clinical development/research phase of regulatory submissions. Concerns exist that AI/ML algorithms could amplify errors and preexisting biases in underlying data sources, which raises issues related to generalizability and ethical considerations. Other challenges include limited explainability due to model complexity and proprietary reasons, as well as managing risks related to data quality, reliability, and representativeness. The FDA recognizes that a careful, risk-based assessment of the specific context of use (COU) is needed when evaluating AI/ML.

Recommendations
Stakeholders should adhere to practices in three key areas: human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation. A risk management plan should be applied to identify and mitigate risks based on the COU, guiding the level of documentation and transparency. Practices are needed to ensure the integrity of AI/ML and address issues like bias and missing data. For models, developers should use pre-specification steps and clear documentation for development and assessment criteria. Models must be monitored over time for reliability and consistency, and Real-World Data (RWD) performance can provide valuable feedback, including for potential re-training.

Regulatory Considerations
The FDA encourages early engagement through mechanisms like the Critical Path Innovation Meetings (CPIM), ISTAND Pilot Program, and Emerging Technology Program to discuss relevant AI/ML methodologies or technologies. The Verification and Validation (V&V 40) risk-informed credibility assessment framework and the principles for Good Machine Learning Practices (GMLP), while not specific to drug development, are helpful guides for evaluating models. The industry is exploring the use of a Predetermined Change Control Plan (PCCP) mechanism for AI/ML-based devices to proactively specify and manage modifications, enhancing adaptability. In general, a risk-based approach should guide the level of evidence and record keeping needed for the verification and validation of AI/ML models for a specific COU.

Findings
CADe devices must meet classification requirements under 21 CFR 892.2050, including general and special controls, and require FDA clearance through 510(k) submissions.
Each new CADe device or significant modification must demonstrate substantial equivalence to a predicate device in terms of safety and effectiveness.
Robust testing and validation are necessary, including standalone and clinical performance assessments, to evaluate detection accuracy and false positive rates.
Devices with substantive technological differences or new intended uses may require clinical performance assessments.
Enrichment strategies for study populations (e.g., including challenging cases) are encouraged but should not bias performance evaluations.

Recommendations
Clearly describe the CADe algorithm, training datasets, scoring methodologies, and intended use in premarket submissions.
Conduct standalone performance assessments to measure detection accuracy and generalizability.
Compare new devices to predicate devices whenever possible, using consistent datasets and methodologies.
Develop and submit user training materials that address expected device performance, limitations, and appropriate usage scenarios.
Provide comprehensive labeling, including indications for use, directions, warnings, precautions, and performance metrics, to ensure clinician understanding and appropriate application.

Regulatory Considerations
All CADe devices under 21 CFR 892.2050 must comply with 510(k) premarket notification requirements, including general and special controls.
Changes to CADe algorithms or device characteristics must be evaluated for significant impact on safety and effectiveness, potentially requiring new submissions.
Devices with altered indications for use or significant technological differences may need additional clinical performance studies to demonstrate substantial equivalence.
Labeling must comply with 21 CFR Part 801 and provide sufficient information to describe the device, its intended use, and directions for use.
Manufacturers should consult FDA for guidance on substantial modifications or unique device characteristics.