Findings
The FDA classifies CDS software as Non-Device CDS only if it meets four specific criteria related to data inputs, information display, HCP support, and independent reviewability. Software functions that analyze medical images, signals from IVDs, or patterns from signal acquisition systems remain regulated as medical devices. Non-Device CDS must be intended for health care professionals and not for patients or caregivers. Automation bias and the time-critical nature of decision-making are key factors in determining whether an HCP can truly review the basis of a recommendation independently. If software provides a specific diagnostic or treatment directive rather than a list of options, it generally fails to meet the exclusion criteria.

Recommendations
Developers should ensure that software intended as Non-Device CDS provides a plain language description of the underlying algorithm and the data used for validation. The software or labeling must clearly identify the intended HCP user, the patient population, and the required input medical information. To support independent review, the software should highlight the source of its clinical recommendations, such as specific clinical practice guidelines or peer-reviewed studies. Developers are encouraged to use usability testing to verify that HCPs can understand the basis of recommendations without relying primarily on the software’s output. For multiple function products, developers should follow the FDA’s policy for assessing products that contain both device and non-device functions.

Regulatory Considerations
The FDA applies a risk-based approach to software oversight, focusing on functions that acquire or analyze complex medical data like ECG waveforms or genomic sequences. Software intended for time-sensitive or critical medical decisions is typically regulated as a device because the user lacks the time to independently verify the recommendation. The agency intends to exercise enforcement discretion for certain software functions that provide only one clinically appropriate recommendation if all other non-device criteria are met. Sponsors may use the Q-Submission process to discuss alternative approaches or clarify the regulatory status of specific software functions. Existing digital health policies continue to apply to software functions that meet the device definition, including mobile medical applications.

Findings
The most promising aspects of mental health for digital measurement are sleep, physical activity, stress, and social behavior, which have the strongest scientific evidence. Core barriers to adoption include high cost and limited access, data privacy concerns, poor technological literacy, and a lack of technology adaptation for specific mental health needs. Essential technology characteristics for "fit-for-purpose" sDHTs include usability, reliable performance, strong data privacy and security, and long battery life.

Recommendations
Research and development should prioritize moving promising measures (sleep, activity, stress, social behavior) to large-scale clinical trials. Algorithms must be refined and clinically validated for mental health indications, and new sensor modalities should be explored. Infrastructure must be developed by creating standards and ontologies for mental health sensor data to ensure interoperability and scalability. To improve access and equity, financial support mechanisms and inclusive, culturally tailored design are critical.

Regulatory Considerations
The report does not provide a separate section for "Regulatory Considerations" but emphasizes that future development and funding should prioritize clinical validation across diverse populations. It notes the importance of a clear understanding of the intended measurement claims and the need for rigorous validation studies to move beyond pilot and feasibility stages to demonstrate real-world clinical utility.

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
Collaborative Communities are sustained, multi-stakeholder forums (including patients, industry, academia, and the FDA) dedicated to solving shared challenges in the medical device ecosystem. These communities are not intended to replace formal regulatory mechanisms. They are equipped to perform activities such as:
Developing best practices and strategies.
Generating and evaluating evidence to support novel approaches.
Clarifying ill-defined challenges and generating consensus on definitions.
Addressing issues related to product quality and safety.

Recommendations
The FDA/CDRH does not establish or fund these communities. Instead, the FDA recommends that interested stakeholders convene and lead these groups. The FDA reviews opportunities on a case-by-case basis for participation, considering:
The community's potential public health impact.
Alignment with the CDRH mission, priorities, and resources.
The existence of a formal governance structure, a convener, a plan to measure success, and a mechanism for sustained engagement.

Regulatory Considerations
The FDA's participation in these communities is a strategic priority for advancing regulatory science and fostering responsible medical device innovation. Examples of digital health-related collaborations include those focused on AI/ML, Digital Biomarkers, Digital Health Technologies (DHTs), and Real-World Data (RWD). The outcomes developed by these groups can inform and accelerate the development of science-based solutions to policy and scientific challenges.

Findings
The document introduces a risk-based credibility assessment framework for establishing and evaluating the credibility of an Artificial Intelligence (AI) model's output when used to support regulatory decisions regarding drug safety, effectiveness, or quality. The framework outlines a 7-step process beginning with defining the question of interest and the Context of Use (COU). Credibility is defined as trust, established through evidence, in the AI model's performance for a particular COU. The credibility assessment is tailored to the AI model risk, which is a combination of model influence (the AI model's evidence contribution relative to other evidence) and decision consequence (the significance of an adverse outcome from an incorrect decision). The document highlights challenges with AI use, including variability in development datasets (training/tuning), the need for methodological transparency due to model complexity, difficulty in quantifying and interpreting uncertainty in model output, and the potential for performance change over time (data drift), which necessitates life cycle maintenance.

Recommendations
Sponsors and interested parties should define the question of interest and clearly define the COU, detailing the AI model's specific role and scope and whether other information will be used. They should assess the AI model risk (low, medium, or high) to ensure that subsequent credibility assessment activities (Step 4) are commensurate with that risk and tailored to the COU. For Step 4, the credibility assessment plan should include a description of the model, model development process (including inputs, architecture, feature selection, and rationale), and data used (training and tuning data). Development data must be deemed fit for use (relevant and reliable) to mitigate issues like algorithmic bias. The plan should also detail the model evaluation process using independent test data and include performance metrics with confidence intervals, an estimate of uncertainty, and a description of model limitations. Early engagement with the FDA is strongly encouraged to discuss model risk and the adequacy of the credibility assessment plan.

Regulatory Considerations
The risk-based credibility assessment framework is intended to help organize and document information for regulatory submissions. The required stringency of assessment activities and the level of documentation should be commensurate with the AI model risk. For AI models whose performance can change over time (e.g., in pharmaceutical manufacturing or postmarketing), sponsors must implement life cycle maintenance plans to monitor performance and manage changes in a risk-based manner. Changes to AI models should be evaluated through the manufacturer's change management system and may require re-execution of parts of the credibility assessment plan. Early engagement can be facilitated through formal meetings (e.g., Pre-IND) or other specialized programs listed in the guidance, such as the Center for Clinical Trial Innovation (C3TI), the Model-Informed Drug Development (MIDD) Paired Meeting Program, and the Emerging Technology Program (ETP) or Advanced Technologies Team (CATT).

Findings
MCID represents the smallest change that someone living with Alzheimer's disease would identify as important, but faces several universal application challenges. Alzheimer's disease progresses differently for each individual, complicating the establishment of universal standards that account for individual-level issues. The disease is gradual and evolving, with what is perceived as clinically meaningful varying significantly at early and late disease stages. People living with Alzheimer's disease and caregivers may have differing perspectives on treatment benefits, making it challenging to establish appropriate MCID. Current Alzheimer's trials rely on various tests to evaluate cognitive and functional impairments, but these tests often lack sensitivity to early-stage changes and are affected by variability in rater rankings. Digital biomarkers offer promising approaches for detecting real-time, objective clinical differences and improving patient outcomes through continuous monitoring, individualized assessments, and artificial intelligence learning for complex analytical predictions.

Recommendations
Digital biomarkers and advanced health technologies should be leveraged to enable continuous monitoring and individualized assessments that can better capture meaningful change in Alzheimer's disease. The primary focus must remain on outcomes that truly matter to people living with Alzheimer's disease and their caregivers, ensuring that the principle of clinical meaningfulness is not lost as new technologies are introduced.

Regulatory Considerations
Important considerations around standardization, accuracy, and integration into current clinical frameworks must be addressed as digital biomarkers are adopted. As new technologies are introduced alongside evolving regulatory frameworks, maintaining focus on clinically meaningful outcomes for patients and caregivers is essential.

Findings
The DHCoE works to strategically advance science and evidence for digital health technologies (DHTs).
Key areas of focus include Artificial Intelligence / Machine Learning (AI/ML) in Software as a Medical Device (SaMD), Cybersecurity, Augmented Reality (AR) and Virtual Reality (VR), and Wireless Medical Devices.
The DHCoE develops and publishes Guidances with Digital Health Content and maintains a Digital Health Policy Navigator to provide clarity on regulatory policies.
Digital health technologies are acknowledged as having the potential to facilitate decentralized clinical trial activities and allow for continuous or frequent measurements of clinical features remotely.
Programs and initiatives include the Software Precertification (Pre-Cert) Pilot Program, the Regulatory Accelerator, and the Diagnostic Data Program.
The center is also involved in international harmonization on device regulatory policy and standards.

Recommendations
The DHCoE recommends that stakeholders, including sponsors and DHT manufacturers, engage with the agency early to discuss the use of DHTs in drug development or for decentralized clinical trials (DCTs).
Stakeholders are encouraged to use the Digital Health Policy Navigator tool to assess whether a particular software function meets the device definition and is the focus of FDA oversight.
The DHCoE emphasizes the need for a patient-centered approach for AI/ML-enabled devices that considers issues like usability, equity, trust, and accountability, and promotes transparency.

Regulatory Considerations
The DHCoE's work includes innovating the regulatory paradigm for digital health, moving towards models that may include shifting scrutiny from the pre-market to the post-market phase and focusing on the capability of firms (Software Pre-Cert Pilot Program).
The FDA has committed, as part of PDUFA VII, to activities such as publishing a Framework for the Use of DHTs in Drug and Biological Product Development and establishing a DHT Steering Committee.
The center provides information to help determine the regulatory status of various digital health products, such as Software as a medical device (SaMD), mobile medical applications (MMA), and General Wellness products.
Submissions for products with device software functions must include recommended documentation for the FDA's evaluation of safety and effectiveness.
For questions regarding upcoming premarket submissions, stakeholders are directed to contact the appropriate review division through a Q-submission.

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
AI/ML technologies offer dynamic learning capabilities but require careful regulation to ensure safety and effectiveness.
The FDA recognizes that traditional regulatory paradigms may not align with the adaptive nature of AI/ML and is developing frameworks to address this.
Guidance documents, such as the AI/ML SaMD Action Plan and predetermined change control plan (PCCP) recommendations, provide a structured approach for handling software updates.
Collaboration across FDA centers (CDRH, CBER, CDER) facilitates consistent regulatory practices for AI/ML across medical products.
Transparency and real-world data integration are key focuses in regulating AI/ML technologies.

Recommendations
Manufacturers should use FDA's premarket pathways, including 510(k), De Novo, or PMA, for AI/ML-enabled SaMD.
Apply Good Machine Learning Practices (GMLP) during development to ensure algorithm reliability, transparency, and patient safety.
Include a predetermined change control plan (PCCP) in submissions to allow for iterative updates without requiring resubmissions.
Follow lifecycle management practices to maintain AI/ML system performance after deployment.
Engage with FDA early in development to align on appropriate regulatory strategies for novel AI/ML implementations.

Regulatory Considerations
AI/ML-driven SaMD updates may require premarket review, depending on the significance of changes and associated risks.
The FDA has outlined principles for transparency, including clear labeling and documentation of AI/ML system capabilities and limitations.
Guidance documents like the "Good Machine Learning Practice" and "Marketing Submission Recommendations for PCCP" should be followed for compliance.
Collaboration between FDA centers ensures alignment on the use of AI in combination products and broader healthcare applications.
Lifecycle management strategies must account for real-world data to ensure continuous learning and safe AI/ML system updates.

Findings
Data analytics are challenging due to diverse metrics and study aims.
Most devices lack built-in software for data output.
There is a lack of comparison and validation studies for different devices and metrics.
Validation of PA metrics is difficult due to the absence of a gold standard.
The integration of various databases is needed but challenging.

Recommendations
Conduct comparison and validation studies between different brands of devices and PA metrics.
Develop standardized metrics for measuring PA.
Improve data integration methods across different studies and databases.
Focus on developing built-in software for devices to facilitate data output.
Encourage research on the validation of PA metrics.

Regulatory Considerations
1Not mentioned

Findings
Standard assessments lack sensitivity in early stages of neurodegenerative diseases.
Challenges with the validity and quality of RMT measurements.
Issues related to equity and inclusion in deploying digital tools.
Importance of considering feasibility, acceptance, usability, and ecological validity of digital endpoints.

Recommendations
Develop regulatory strategies early on.
Ensure equity and inclusion in deploying digital tools.
Address challenges related to the validity and usability of digital endpoints.
Promote public-private partnerships to address privacy and security concerns.
Involve patients and stakeholders in the design and implementation of digital tools.

Regulatory Considerations
Acceptance of digital endpoints by regulatory authorities is crucial.
Validation with current gold standards and clinically meaningful legacy endpoints.
Ensure data security and privacy.