
Welcome to the sDHT Adoption Library, featuring NaVi
NaVi is a closed-environment AI research assistant that leverages a carefully curated library of more than 300+ vetted documents, including FDA guidance and industry best practices. NaVi helps you search and explore content across the sDHT Adoption Library and Roadmap using natural language questions.
The Library is intended to serve as a living resource. Content is added periodically as new guidance, standards, and peer-reviewed research are released.
Meet NaVi: Your AI-Powered Research Assistant
Library scope and selection
To ensure high-quality, relevant results, the Library follows a predefined scoping approach:
- Inclusions: FDA guidance, non-commercial standards, and peer-reviewed research (2018–Present) focused on sDHTs being used as measurement tools for medical products in U.S.-based clinical trials.
- Exclusions: Materials from single commercial entities, non-U.S. regulatory bodies (except select EMA guidances with direct U.S. cross-relevance), and conference proceedings, and conference proceedings.
Inclusion in the Library does not imply endorsement, completeness, or regulatory acceptability.
Library scope
Resources in the sDHT Adoption Library are identified using a predefined scoping approach and include publicly available FDA guidance, non-commercial standards and guidance, and peer-reviewed research relevant to sDHT use in U.S.-based clinical trials. Materials from single commercial entities, non-U.S. regulatory bodies, conference proceedings, and studies conducted exclusively outside the United States are excluded; inclusion does not imply endorsement or regulatory acceptability.
Last updated 2026: Library content is reviewed and updated on a periodic basis as new eligible materials become available.
510(k) Premarket Notification
510(k) Premarket Notification
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.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Artificial Intelligence and Machine Learning in Software as a Medical Device
Artificial Intelligence and Machine Learning in Software as a Medical Device
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.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Digital Health Regulatory Pathways
Digital Health Regulatory Pathways
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.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions
Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions
AI-DSFs undergo iterative improvements, necessitating a structured framework for modifications to ensure safety and effectiveness.
PCCPs enable manufacturers to streamline modifications by avoiding repeated marketing submissions, reducing regulatory burden.
Critical elements of a PCCP include data management practices, re-training protocols, performance evaluation, and user update procedures.
Comprehensive risk management and transparency are essential to address potential biases and maintain user trust.
Certain modifications, such as those significantly affecting safety or effectiveness, may still require a new marketing submission.
Recommendations
Structure PCCPs with a clear description of planned modifications, a detailed modification protocol, and a robust impact assessment.
Include methods for data collection, re-training, and performance evaluation aligned with quality system regulations.
Specify user update procedures to communicate changes transparently and ensure safe device use.
Address cybersecurity risks and bias mitigation strategies in modification protocols.
Use the FDA Q-Submission Program to discuss PCCPs prior to submitting marketing applications for AI-DSFs.
Regulatory Considerations
Adherence to 21 CFR Part 820 Quality System Regulations, including design controls and risk management.
PCCPs must include modifications that would otherwise require a PMA supplement or new 510(k) submission.
Modifications implemented under PCCPs must conform to FDA-reviewed protocols and be documented in the device master record.
Transparency to users via device labeling updates and public summaries of authorized PCCPs is required.
Modifications outside the scope of an authorized PCCP or deviations from the protocol require new FDA marketing submissions.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Requests for Feedback and Meetings for Medical Device Submissions: The Q-Submission Program
Requests for Feedback and Meetings for Medical Device Submissions: The Q-Submission Program
Pre-Submissions (Pre-Subs) allow submitters to obtain FDA feedback on specific questions before submitting formal IDEs, 510(k)s, PMAs, or other applications. Early feedback can improve submission quality and streamline the review process.
Submission Issue Requests (SIRs) provide a mechanism for addressing issues raised in FDA hold letters (e.g., 510(k) deficiencies) to help expedite resolutions.
Study Risk Determinations help sponsors clarify whether clinical studies are significant risk (SR), non-significant risk (NSR), or exempt from IDE regulations.
Informational Meetings are non-feedback sessions aimed at familiarizing FDA staff with new devices or sharing updates on ongoing development.
The program encourages timely submissions, including supplements for ongoing discussions and amendments to update materials.
Recommendations
Clearly define the purpose and goals of the Q-Sub in the submission to facilitate effective FDA review.
Include specific, well-formulated questions that focus on a limited number of topics to ensure actionable feedback.
For Pre-Subs, align planned testing and submissions with FDA guidance and include detailed device descriptions, testing protocols, and relevant background information.
Use SIRs to discuss proposed solutions to deficiencies raised in FDA hold letters, focusing on timely resolution.
Draft and submit meeting minutes promptly (within 15 days of meetings) to ensure accurate documentation of FDA feedback.
Regulatory Considerations
Submitters should adhere to the timelines specified for different Q-Sub types, including 70 days for Pre-Sub feedback or 21 days for SIRs submitted promptly after a hold letter.
Q-Subs should include all relevant regulatory history and references to prior FDA communications to streamline the review process.
FDA feedback through the Q-Sub program is non-binding and based on the information available at the time; subsequent submissions must align with the provided feedback to maintain consistency.
Informational Meeting requests should clearly state that feedback is not expected and may be used to track interactions outside other formal Q-Sub types.
Confidentiality of Q-Subs is maintained in compliance with FDA’s disclosure regulations and the Freedom of Information Act (FOIA).
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Approval (PMA) and Premarket Notification [510(k)] Submission
Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Approval (PMA) and Premarket Notification [510(k)] Submission
CADe clinical performance studies must address key variables, including reader variability, disease prevalence, and device design differences.
Properly conducted MRMC studies are critical for assessing diagnostic effectiveness, incorporating both unaided and aided reading conditions.
Enriched datasets, while useful for stress testing, must be carefully designed to avoid bias and reflect intended use populations.
The truthing process (establishing reference standards) is essential to validate device performance claims and should be rigorously defined.
The FDA encourages pre-specification of hypotheses, statistical methods, and endpoints to ensure robust and interpretable results.
Recommendations
Design studies with representative patient populations and include diverse subgroups relevant to the device’s intended use.
Use validated statistical methods for MRMC analyses, reporting sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics.
Develop and document a detailed truthing process for establishing reference standards, ensuring consistency and reliability.
Conduct stress testing with enriched datasets to evaluate device performance under challenging conditions but avoid overrepresenting certain subsets.
Submit a complete study protocol and statistical analysis plan, including sample size justification, randomization methods, and scoring techniques.
Regulatory Considerations
CADe devices classified under 21 CFR 892.2050 or 892.2070 must comply with premarket notification requirements, including performance testing and labeling.
Standalone performance assessments may suffice in certain scenarios, but clinical studies are often necessary for substantial equivalence determinations.
Use of foreign clinical data requires justification of its applicability to U.S. populations and medical practice.
FDA expects data integrity controls, such as firewalls and audit trails, to prevent tuning bias in test datasets reused across studies.
The FDA encourages early engagement (e.g., Pre-Submission requests) for feedback on study protocols and regulatory pathways.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Notification [510(k)] Submissions
Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Notification [510(k)] Submissions
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.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Engagement Pathways to Communicate with U.S. Regulators (FDA – Food and Drug Administration)
Engagement Pathways to Communicate with U.S. Regulators (FDA – Food and Drug Administration)
There are various formal and informal engagement pathways available for developers of Digital Health Products and Combination Products to communicate with the FDA to seek advice regarding product classification, regulatory status, and submission strategies. Informal pathways include the Digital Health Inquiry (via the Digital Health Inbox), the DICE Mailbox Inquiry, and the Pre-RFD Process, which provide non-binding feedback. Formal pathways include the 513(g) Program for classification, and the Q-Submission Program (encompassing Pre-Submissions for pre-application feedback and SRD for risk determination).
Recommendations
Manufacturers should use the provided map to determine the appropriate pathway based on their product type (standalone digital health or combination product) and the type of advice they are seeking (informal or formal). The Pre-Submission (Pre-Sub) program is recommended as an opportunity to obtain formal feedback "prior" to submitting an application, particularly if a new product's regulatory pathway is unclear or if planning a study to support a future application. Combination Product manufacturers can use CPAMs to clarify marketing authorization standards or post-market modification requirements.
Regulatory Considerations
The 513(g) Request provides information on a product's classification and applicable regulatory requirements but does not determine substantial equivalence or make final marketing authorization decisions. Programs like the CDRH-Payor Connection and Parallel Review with CMS are voluntary and designed to expedite patient access by aligning clinical evidence for both regulatory clearance/approval and coverage decisions. Participation in these programs, however, does not alter the FDA’s existing, separate standards for regulatory review.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Technical Performance Assessment of Quantitative Imaging in Radiological Device Premarket Submissions
Technical Performance Assessment of Quantitative Imaging in Radiological Device Premarket Submissions
Findings
Quantitative imaging extracts numerical values from medical data that are subject to systematic error and random variation. The utility of these values depends on well-characterized performance and sufficient user information for interpretation. Performance specifications often change throughout the operating range of a device, such as volumetric reproducibility varying by structure size. Fully automated functions require more robust analytical validation than manual or semi-automated functions because they lack the opportunity for expert user correction. While phantoms serve as high-quality reference standards for ground truth, they are simplifications that may not fully reflect clinical performance.
Recommendations
Manufacturers should provide a detailed technical description of the quantitative imaging function, including the measurand, algorithm training paradigms, and level of automation. Performance specifications should incorporate objective reference values when available to allow for comparisons between subject and predicate devices. A sensitivity analysis should be conducted to determine the impact of sources of error like patient characteristics, image acquisition protocols, and image processing. Labeling must include clear instructions for user-performed quality assurance and specify any limitations where the function has been found ineffective. For automated devices, manufacturers should help users understand scenarios where the function might generate an incorrect output that is not easily identifiable.
Regulatory Considerations
The FDA recommends following a ten-step technical performance assessment process, ranging from defining the measurand to comparing statistical results against pre-defined acceptance criteria. Premarket submissions should include performance data demonstrating that the device meets claims regarding bias, precision, linearity, and limits of quantitation. Uncertainty should be reported in units of the measurand and cover the entire operating range of the function. Manufacturers are encouraged to use the Q-Submission process to address questions regarding regulatory status or specific requirements. Software implementation details should align with existing FDA guidance for the content of premarket software documentation.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Acceptance of Clinical Data to Support Medical Device Applications and Submissions: Frequently Asked Questions
Acceptance of Clinical Data to Support Medical Device Applications and Submissions: Frequently Asked Questions
FDA requires OUS clinical investigations to comply with GCP, ensuring the credibility and accuracy of data and protecting human subjects.
Statements on GCP compliance and supporting information are mandatory for OUS data submissions.
Waivers are permitted in circumstances where GCP compliance is unattainable or where local regulations differ significantly from FDA requirements.
Investigations must demonstrate that OUS data are applicable to U.S. populations and medical practices.
Sponsors must provide robust documentation, including investigator qualifications, site descriptions, IEC reviews, and informed consent processes.
Recommendations
Ensure clinical investigations adhere to GCP standards, including IEC review and informed consent, for all OUS clinical data submitted to FDA.
Include detailed supporting information in submissions, such as investigator qualifications, facility descriptions, protocols, and data summaries.
Clearly identify any deviations from GCP and justify how data integrity and subject protection were maintained.
Use FDA’s Pre-Submission Program to discuss potential challenges with GCP compliance or data validation before submission.
Retain all required records for at least two years after FDA’s decision on the application or submission.
Regulatory Considerations
FDA evaluates OUS clinical data on a case-by-case basis, considering the adequacy of GCP compliance and supporting documentation.
For significant risk device investigations, sponsors must provide the most comprehensive documentation, while non-significant risk and exempt devices require less detailed information.
Waivers may be granted when justified by public health considerations or when local laws prohibit compliance with specific FDA requirements.
FDA retains the right to inspect clinical sites or review source documents to validate data integrity and compliance with GCP.
Sponsors must ensure that OUS data are valid and relevant to the U.S. population and medical practice.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Deciding When to Submit a 510(k) for a Software Change to an Existing Device
Deciding When to Submit a 510(k) for a Software Change to an Existing Device
Software changes must be assessed for potential impacts on the safety and effectiveness of the device, even if they are routine updates.
A risk-based assessment is required to determine whether changes introduce new risks, modify existing risks, or necessitate new risk controls.
Cybersecurity updates, routine maintenance changes, and minor clarifications may not require a new 510(k) if they do not affect performance specifications or safety.
Substantial modifications, such as adding new algorithms, introducing new functionalities, or modifying clinical performance, generally require a new 510(k).
Manufacturers must document all changes, even those not requiring a new 510(k), in compliance with QS regulations.
Recommendations
Use the provided flowchart and guiding principles to evaluate whether software changes exceed the regulatory threshold for submission of a new 510(k).
Conduct a risk-based assessment for all changes, focusing on new or modified risks and the adequacy of existing risk controls.
Verify and validate changes to ensure they meet device specifications and do not introduce unintended consequences.
Submit a new 510(k) for changes that significantly affect clinical functionality, performance specifications, or introduce new risks that are not mitigated.
Maintain detailed documentation of all decisions regarding software changes, including rationale for whether submission of a new 510(k) was required.
Regulatory Considerations
Submission of a new 510(k) is required for changes that could significantly impact the safety or effectiveness of a device or constitute a major modification to its intended use.
Cybersecurity updates generally do not require a new 510(k) if they solely strengthen security without affecting device performance.
Manufacturers must evaluate cumulative changes and submit a new 510(k) if the combined impact exceeds the regulatory threshold.
Device software changes involving substantial algorithm modifications or system architecture updates are likely to require a new 510(k).
Changes that address compliance during a recall or correction must be evaluated under FDA's guidance for recalls and device enhancements.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Medical Device Accessories – Describing Accessories and Classification Pathways
Medical Device Accessories – Describing Accessories and Classification Pathways
An accessory is defined as a finished device that supports, supplements, or augments the performance of a parent device.
Accessories are classified based on their individual risk profiles when used with parent devices, which may differ from the classification of the parent device.
The De Novo process can be used for new accessory types with no existing classification, enabling lower-risk accessories to be classified in Class I or II.
Articles not specifically intended for use with a medical device (e.g., generic batteries or monitors) are not considered accessories unless labeled or promoted for such use.
FDA encourages using pre-submission requests to obtain feedback before submitting Accessory Requests or De Novo classifications.
Recommendations
Determine whether an article qualifies as an accessory by evaluating its intended use with a parent device based on labeling and promotional materials.
Evaluate the risks associated with the accessory when used as intended with its parent device, considering both unique and parent-related risks.
Use the Accessory Request process for new or existing accessories to propose appropriate classifications, supported by evidence of risk profiles and proposed regulatory controls.
Submit De Novo requests for new accessory types lacking existing classifications, providing data on performance, risks, and mitigation measures.
Include clear and comprehensive labeling for accessories, specifying compatibility and performance with identified parent devices.
Regulatory Considerations
Classification of accessories should reflect their risks and required controls, independent of their parent device classification.
Accessories categorized as Software as a Medical Device (SaMD) must meet the same risk-based classification framework applied to other medical devices.
Manufacturers can request reclassification or exemption from 510(k) requirements for previously classified accessories through applicable FDA mechanisms.
FDA must respond to Accessory Requests for existing accessory types within 85 days and De Novo requests within 120 days, as specified in the FD&C Act.
The Paperwork Reduction Act governs the submission of accessory classification requests, requiring compliance with established timelines and documentation requirements.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.