Findings
In a progressive neurodegenerative illness like Alzheimer's disease, slowing the rate of disease progression is considered a clinically meaningful outcome for patients and their caregivers.
The assessment of what constitutes a clinical benefit is highly dependent on the specific stage of AD being studied, the drug's mechanism of action, and the symptoms present in that patient population.
Direct input from patients and caregivers is critical for understanding disease burden and defining treatment benefits that are truly meaningful from their perspective.
The interpretation of score changes on Clinical Outcome Assessments (COAs) requires full context; an absolute point difference must be considered relative to the study's duration, the expected placebo decline, and the specific disease stage.
Evidence from biomarkers that show an effect on underlying disease pathology provides additional support and increases the persuasiveness of the changes observed on clinical endpoints.

Recommendations
Drug developers should implement multiple "fit-for-purpose" COAs that use different reporters (e.g., clinicians, observers) and methods to generate broad and diverse evidence of a drug's clinical benefit.
Sponsors should utilize both qualitative and quantitative methodologies to explore clinical meaningfulness, including assessing "meaningful within-patient change" throughout the development process.
Developers are encouraged to create and validate new COAs and leverage innovative approaches, such as digital health technologies, to better capture concepts that are relevant to patients, especially in the earliest stages of AD.
Throughout the drug development lifecycle, stakeholders should systematically collect and incorporate patient experience data to ensure that the perspectives, needs, and priorities of patients are meaningfully captured.

Regulatory Considerations
For a drug to gain approval, it must meet the regulatory standard of "substantial evidence of effectiveness," which is typically derived from adequate and well-controlled investigations designed to minimize bias.
The FDA defines clinical benefit as a clinically meaningful effect of a drug on how an individual feels, functions, or survives.
An assessment of clinical benefit is not limited to the primary endpoint; the consistency of findings across multiple endpoints (primary and secondary) is a key consideration during regulatory review.
The accelerated approval pathway may be used for serious conditions with unmet needs based on a surrogate endpoint, but traditional approval requires verification of clinical benefit in confirmatory trials.
The FDA's evaluation includes a benefit-risk analysis, which considers the severity of the disease and the availability of alternative therapies, recognizing that patients and physicians may accept greater risks for life-threatening illnesses.

Findings
The traditional process of evaluating biomarkers within the context of a single drug development program is inefficient and creates uncertainty for sponsors. This case-by-case approach leads to redundant efforts, slows down the development of novel therapies, and hinders the broad adoption of promising scientific tools. There is a clear need for a centralized, collaborative pathway to formally validate biomarkers, which can de-risk drug development, encourage innovation, and make the process more predictable and cost-effective for all stakeholders.

Recommendations
Drug developers, academic researchers, and other stakeholders should proactively engage with the FDA through the formal Biomarker Qualification Program to validate biomarkers for specific contexts of use. It is recommended to form public-private partnerships and other collaborations to pool resources and data, which strengthens the evidence package for a biomarker's utility. Developers should use the qualification process to establish a biomarker's value early, making it a publicly available and reliable tool that can accelerate the development of multiple drug products.

Regulatory Considerations
The Biomarker Qualification Program provides a distinct regulatory pathway for establishing a biomarker's validity for a specific Context of Use (COU), separate from an individual Investigational New Drug (IND) or New Drug Application (NDA). The process involves a three-stage submission and review cycle: the Letter of Intent, the Qualification Plan, and the Full Qualification Package. Once qualified, a biomarker is publicly listed and can be incorporated into multiple drug development programs without the need for sponsors to re-submit and re-justify the validation data for that specific COU, streamlining subsequent regulatory reviews.

Findings
Digital endpoints must not only support regulatory approval but also provide evidence that meets payer expectations for reimbursement and value-based care. The lack of early engagement with payers and health technology assessment (HTA) agencies is a key barrier to the adoption of digital clinical measures. Digital measures can enhance value-based care models by capturing patient-centered outcomes, reducing healthcare costs, and improving early disease detection. The scalability and generalizability of digital endpoints remain challenges, particularly for diverse populations and real-world healthcare settings. Technical and systematic barriers—such as data heterogeneity, stakeholder knowledge gaps, and inconsistent regulatory-payer alignment—are slowing the adoption of digital endpoint data for reimbursement decisions.

Recommendations
Pharma and medical product developers should engage early with payers and regulators to ensure digital endpoints align with reimbursement expectations. Payers and HTA bodies should establish clear evidence thresholds for digital endpoint validation, ensuring consistency in market access decisions. Digital endpoints should be validated against health-related quality of life (HRQoL) measures and patient-reported outcomes (PROs) to demonstrate clinical relevance. Real-world evidence (RWE) should be incorporated into clinical trials alongside digital endpoints to strengthen reimbursement applications. Stakeholders should prioritize scalable, patient-centered digital measures that capture disease progression over time and across different care settings.

Regulatory Considerations
Integrated Evidence Plans (IEPs) should be developed early to align digital endpoint evidence with regulatory and payer requirements. Digital endpoints should be assessed through multi-stakeholder collaboration, ensuring validation across pharmaceutical, regulatory, and reimbursement frameworks. Payers and regulators should work together to create aligned pathways for digital measure acceptance, reducing delays in market access. Data security, privacy, and interoperability must be addressed to support regulatory approval and patient trust in digital health solutions. The industry should leverage international regulatory-payer collaboration models, such as the HTA-EMA partnership and the FDA Payor Communication Task Force, to accelerate global digital endpoint adoption.

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
Patient experience data provides critical context for regulatory review by illuminating disease burden, unmet medical needs, and the aspects of a condition that matter most to patients.
A systematic approach is necessary to ensure patient experience data is robust enough for regulatory consideration, moving beyond anecdotal evidence to scientifically rigorous data collection.
Early engagement between sponsors and the FDA is a key factor for successfully incorporating patient perspectives into a drug development program.
The value of patient-reported outcomes (PROs) and other clinical outcome assessments (COAs) is highly context-dependent, varying significantly across different diseases and patient populations.

Recommendations
Drug sponsors should leverage the FDA's meeting process to discuss their strategies for collecting and submitting patient experience data early in the development lifecycle.
Sponsors should utilize the repository of meeting reports as a learning resource to understand best practices and common challenges in patient-focused drug development for specific conditions.
Patient advocacy groups should actively participate in these discussions to ensure the full spectrum of patient experiences is captured and communicated to both regulators and developers.
Researchers should develop and validate novel tools and methodologies for capturing and analyzing patient experience data that are meaningful for both clinical and regulatory purposes.

Regulatory Considerations
Patient experience data is a key component of the benefit-risk assessment, providing evidence that can inform regulatory decisions regarding a drug's approval and labeling.
The FDA's review of patient experience data is guided by a commitment to patient-focused drug development, as mandated by the 21st Century Cures Act and supported by user fee agreements like PDUFA.
The scientific rigor of data collection and analysis is paramount; for patient experience data to be influential, it must meet high standards of validity and reliability.
Transparency is a core principle, and the publication of these meeting reports is intended to provide clear examples of how patient input can be effectively integrated into regulatory submissions.

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
Cybersecurity is an integral part of medical device safety and effectiveness, and manufacturers are responsible for addressing it throughout the entire device lifecycle. The FDA considers a device's cybersecurity as part of its benefit-risk assessment for both premarket and postmarket activities. A lack of robust cybersecurity controls can lead to patient harm, compromised device functionality, and breaches of data privacy. The dynamic nature of cybersecurity threats requires ongoing monitoring, risk management, and timely implementation of mitigation strategies.

Recommendations
Manufacturers should build cybersecurity into devices from the design phase ("secure by design") and conduct a thorough risk analysis to identify and mitigate potential vulnerabilities. Premarket submissions should include comprehensive documentation of the device's cybersecurity controls, a risk management plan, and a plan for postmarket surveillance and response. Manufacturers should establish a robust postmarket surveillance program to monitor for, identify, and address new cybersecurity threats in a timely manner. Clear and informative labeling is essential to help users understand and manage cybersecurity risks.

Regulatory Considerations
The FDA has the authority to take action against devices with inadequate cybersecurity that pose a risk to public health. The agency recommends that manufacturers use the Q-submission process to discuss specific cybersecurity questions related to their device submissions. Compliance with recognized standards and best practices for cybersecurity is strongly encouraged. Manufacturers must report certain cybersecurity incidents to the FDA as part of their postmarket reporting requirements. The FDA collaborates with other government agencies and stakeholders to promote a coordinated approach to medical device cybersecurity.

Findings
Establishing cross-sector consortia does not guarantee success without a unified objective and stakeholder buy-in. A neutral, independent facilitator is a key element for successful governance in many collaborative platforms. Many consortia lack consistent methods for storing critical data, meeting minutes, and regulatory briefing packages, which creates barriers after project completion. Regulatory success depends heavily on the early development of a strategy that defines the necessary evidence to validate innovative methodologies. Successful examples include the qualification of biomarkers for polycystic kidney disease and type 1 diabetes, as well as imaging measures for Alzheimer’s disease.

Recommendations
Consortium members should develop an initial regulatory strategy during the project scoping and planning phases. Teams must explicitly define the context of use for any proposed tool to articulate exactly what decisions the output will inform. A robust data strategy should be implemented early, including formal agreements for data use, standardization, and sharing that remain in place in perpetuity. Consortia must prioritize sustainability plans to ensure data and active databases remain available for research and regulatory use after funding expires. Projects should integrate regulatory science expertise from the start to cover both EU and US frameworks.

Regulatory Considerations
Regulators require individual patient-level data that is fully curated, standardized, and presented through formal submissions like qualification applications. Formal regulatory endorsement ensures a tool can be trusted for consistent interpretation in drug development and marketing authorization evaluations. Early engagement with agencies such as the FDA and EMA is essential to gain feedback on novel methodologies and align study designs with regulatory expectations. Specific pathways like the EMA Qualification of Novel Methodologies and the FDA Qualification Process for Drug Development Tools should be utilized. Regulatory qualification may require ongoing access to databases to support the long-term use of the methodology.

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.

Key Principles (Findings)
The central principle of the FDA's program is that Digital Health Technologies (DHTs) offer significant potential to make clinical trials more efficient, patient-centric, and capable of capturing novel data. A key finding is that a collaborative, multifaceted approach is necessary to address the challenges of incorporating DHT-derived data into regulatory decision-making. The program acknowledges that ensuring data quality, validating new endpoints, and establishing clear regulatory expectations are critical for the successful adoption of these technologies in drug development.

Program Activities (Recommendations)
The FDA's activities in this area function as implicit recommendations for the industry. The agency is actively:
Developing a Framework: Creating and publishing a clear framework to guide the use of DHTs in drug and biological product development.
Engaging Stakeholders: Convening public meetings and workshops to foster collaboration and share learning among patients, biopharmaceutical companies, DHT manufacturers, and academia.
Supporting Demonstration Projects: Funding and overseeing research projects to address critical gaps and demonstrate the reliability and validity of specific digital measures.
Building Internal Expertise: Establishing a DHT Steering Committee and enhancing internal knowledge to ensure consistent and expert review of submissions containing DHT-derived data.

Regulatory Considerations
This webpage emphasizes the FDA's commitment to creating a clear regulatory framework for the use of DHTs in drug development. It highlights that while DHTs offer great promise, they also present new regulatory challenges related to data integrity, validation, and analysis. The FDA's approach involves a combination of issuing new regulatory guidance, promoting stakeholder collaboration, and advancing regulatory science. Sponsors are encouraged to engage with the FDA to discuss their use of DHTs in clinical trials to ensure alignment with the agency's expectations. The establishment of the CDRH Digital Health Center of Excellence provides a dedicated resource for such engagement.

To be released.