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.

Regulatory Considerations
The Premarket Notification (510(k)) database is a critical component of the FDA's regulatory framework for medical devices. Its primary function is to house information on devices that have been cleared through the 510(k) pathway, which is the most common route to market for medical devices in the U.S.

A 510(k) submission's central requirement is to demonstrate "substantial equivalence" to a legally marketed predicate device. This means the new device is as safe and effective as a device already on the market. Clearance of a 510(k) does not denote "approval" in the same way as a Premarket Approval (PMA) application but rather confirms that the device meets the necessary criteria for marketing.

The database provides transparency and serves as an essential resource for manufacturers to identify potential predicate devices for their own submissions. For healthcare providers, patients, and researchers, it offers a way to verify the regulatory status and clearance basis for a specific device.

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
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
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
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
The development and evaluation of medical devices require scientifically plausible and reliable tools for collecting data to support regulatory submissions. A lack of standardized, pre-vetted tools can lead to inefficiencies and unpredictability in the device development and review process. The qualification of development tools can be applied across a wide range of device areas, including cardiovascular, neurology, imaging, and cybersecurity. The evidence required for tool qualification must be robust enough to support its intended context of use.

Recommendations
Tool developers, medical device sponsors, research organizations, and academic institutions are encouraged to voluntarily submit proposals to the MDDT program to qualify their tools. Submissions should include a detailed description of the tool, a clearly defined context of use (COU), specific performance criteria, and a comprehensive plan for collecting evidence to validate the tool's performance and scientific plausibility. Collaboration in developing tools and supporting evidence is recommended to pool resources and increase the acceptance of qualified tools.

Regulatory Considerations
The MDDT program is a formal regulatory mechanism for the FDA to qualify tools that can be used to support assessments of medical device safety, effectiveness, or performance. Once a tool is qualified for a specific context of use, the FDA accepts assessments from that tool in support of regulatory submissions without needing to re-evaluate the tool's suitability. The program recognizes four main categories of tools: Non-clinical Assessment Models (NAM), Biomarker Tests (BT), Clinical Outcome Assessments (COA), and an "Other" category for tools that do not fit the primary classifications.

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
Diverse Pathways to Market Exist: The case studies demonstrate there is no single "right" way to approach the FDA; successful strategies are highly varied and include De Novo requests, 510(k) clearances, and leveraging established pathways for new indications.
Early FDA Engagement is Crucial: A consistent theme across the successful case studies is the value of engaging with the FDA early and often. This collaborative approach helps de-risk the development process, clarify evidentiary requirements, and build trust.
"Drug-like" Evidence Can Be a Differentiator: For novel software-based interventions, particularly digital therapeutics, generating a robust body of evidence similar to that of a pharmaceutical (i.e., randomized controlled trials) is a key strategy for gaining regulatory and commercial success.
Platform-Based Approaches are Emerging: Companies are finding success by moving from single-product solutions to integrated platforms that can monitor multiple health aspects, which requires a more holistic regulatory strategy.

Recommendations
Leverage Pre-Submission (Pre-Sub) Meetings: Sponsors are strongly encouraged to use the Q-Submission program to gain valuable, early feedback from the FDA on their validation plans and overall regulatory strategy.
Build a Multi-faceted Commercialization Plan: Regulatory clearance is only one step. The case studies recommend developing a comprehensive strategy that considers market access, reimbursement, and payer engagement from the outset.
Address Underserved Markets: The examples highlight opportunities for innovation in underserved areas, such as pediatrics and behavioral health, where DHTs can fill significant gaps in care.
Innovate on Evidence Generation: Sponsors should be prepared to innovate not just in their technology, but also in their approach to clinical evidence, tailoring their trial designs to best demonstrate the unique value of their digital product.

Regulatory Considerations
Understand the Risk Classification: The regulatory pathway for a DHT is determined by its intended use and associated risk level. Sponsors must correctly classify their device to determine if a 510(k), De Novo, or other pathway is appropriate.
AI/ML Devices Have Unique Needs: For products incorporating artificial intelligence or machine learning, sponsors must address specific regulatory considerations, such as predetermined change control plans (PCCPs), to manage algorithm updates post-market.
Interoperability is a Key Factor: For devices intended to be part of a connected health ecosystem (e.g., automated insulin dosing systems), demonstrating interoperability and cybersecurity is a critical component of the regulatory submission.

Findings
Enhanced documentation is required for high-risk device software where flaws could result in serious injury or death.
Risk management plans should include robust risk assessments, including residual risk evaluations.
Verification and validation activities are critical to confirm software functionality and mitigate risks.
The lack of traceability between software design and requirements can undermine device safety and effectiveness.
Unresolved software anomalies must be carefully documented and justified based on a risk assessment.

Recommendations
Use a risk-based approach to determine whether basic or enhanced documentation levels are required for premarket submissions.
Include comprehensive risk management documentation, detailing hazard identification, risk control measures, and residual risk evaluations.
Provide detailed system and software architecture diagrams, highlighting relationships between modules and external systems.
Document unresolved software anomalies and justify their impact on safety and effectiveness using a risk-based rationale.
Align software development, configuration management, and maintenance practices with FDA-recognized standards like ANSI/AAMI/IEC 62304.

Regulatory Considerations
Adherence to 21 CFR Part 820 Quality System regulations, emphasizing design controls and risk management.
Submission of risk management files and unresolved software anomalies as part of premarket documentation.
Use of system and software architecture diagrams to demonstrate software functionality and risk mitigation.
Implementation of cybersecurity measures as part of software validation and risk management processes.
Documentation of premarket changes and interactions between device functions and external systems, particularly in multi-function devices.

Findings
There is widespread confusion among digital health developers regarding the complex and evolving regulatory landscape, with many uncertain about whether their products require regulation or which pathway to pursue. This lack of a clear regulatory strategy acts as a significant barrier to market access, investor confidence, and user trust. The heterogeneity of the digital health sector, coupled with varying international requirements, further complicates the path to market for innovators, hindering the scalability of effective solutions.

Recommendations
Digital health innovators should proactively integrate a tailored regulatory strategy into their core business plan, viewing it as a commercial differentiator rather than a hurdle. Developers are encouraged to utilize resources like DiMe’s regulatory pathway tools to navigate the U.S. and global landscapes effectively. Early and continuous engagement with regulators and collaborative efforts across the industry are essential to ensure products are developed to meet both market needs and regulatory standards, ultimately accelerating the delivery of high-quality digital health solutions to patients.

Regulatory Considerations
A comprehensive policy framework is necessary for the successful integration of digital health technologies, encompassing regulatory authorization, value assessment, and reimbursement. Developers must understand the nuances of different regulatory classifications, such as Software as a Medical Device (SaMD), and their specific evidentiary requirements. Greater international harmonization of regulatory standards is crucial for enabling global scalability. Regulatory bodies should continue to develop agile frameworks that can accommodate the rapid pace of innovation in digital health while ensuring patient safety and product effectiveness.