Findings
Not all CDS software is regulated as a medical device; the FDA applies specific criteria to determine its classification.
CDS software functions are excluded from the device definition if they meet all four criteria in section 520(o)(1)(E) of the FD&C Act.
Automation bias in decision-making poses a risk, particularly in time-critical scenarios, and influences regulatory considerations.
Clear labeling and transparency about the basis for recommendations are essential for enabling HCPs to make independent decisions.
Software functions that provide specific diagnostic outputs or time-critical directives typically fail to meet the criteria for Non-Device CDS.

Recommendations
Clearly define the intended use, user population, and input medical information for CDS software in labeling.
Ensure that software provides transparent and plain language descriptions of algorithms, data sources, and validation results.
Avoid presenting specific treatment or diagnostic directives to ensure the software supports rather than replaces HCP judgment.
Include sufficient information to allow HCPs to independently review and understand the basis for software recommendations.
Engage with the FDA early in the development process for software functions with potential regulatory oversight.

Regulatory Considerations
CDS software functions that meet all four criteria under section 520(o)(1)(E) of the FD&C Act are excluded from FDA’s definition of a device.
Software intended for time-critical decision-making or replacing HCP judgment is generally considered a device.
Developers must ensure that software labeling and functionality align with the criteria for Non-Device CDS.
Transparency in data sources, algorithm logic, and validation methods is required to enable independent HCP decision-making.
The FDA may request additional information or oversight for software that poses significant risks to patient safety.

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

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

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

Findings
There is a lack of well-established consensus parameters for digitized performance outcomes with thresholds for validation acceptance criteria.
The amount of publicly available data on DHT validation remains limited.
Many DHT validation studies are conducted by single institutions and are not disclosed publicly.
The unique proposition of DHTs presents challenges for measure design, development, and validation.
Regulatory endorsements for DHTs in clinical investigations are limited.

Recommendations
Establish technical validation parameters and technology performance acceptance thresholds in the scientific community.
Develop hardware-agnostic approaches by sharing DHT data and cross-validating different algorithms.
Standardize data and create publicly shared databases to facilitate DHT acceptance in drug development.
Form precompetitive consortia via public-private partnerships and professional societies to advance DHT use.
Focus on data sharing to enable DHT measure development in a technology-agnostic way.

Regulatory Considerations
Validation requirements must include understanding the relationship between DHTs and conventional outcome assessments.
Evidence is needed that digital measures capture meaningful health aspects if they constitute an electronic Clinical Outcome Assessment (eCOA).
Initiatives like the CPP Digital Drug Development Tool can advance regulatory maturity by optimizing studies with multiple stakeholders.

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.

Findings
Variability in imaging acquisition, display, and interpretation methods across different clinical sites can increase endpoint measurement errors, potentially compromising a trial's ability to achieve its objectives.
Standard imaging procedures used in routine medical practice are often insufficient for clinical trials, which require greater standardization to reduce variability and ensure the interpretability of results.
In open-label trials, site-based image interpretation is vulnerable to bias because knowledge of a patient's clinical status can influence assessments.
Technical factors such as equipment upgrades, software changes, and inconsistent image quality can introduce errors and undermine the consistency of imaging data collected in multicenter trials.
Lack of consistency in image reader training and performance can lead to significant variability in endpoint measurements, reducing the precision of the treatment effect estimate.

Recommendations
Sponsors should develop and implement trial-specific imaging process standards, detailed in a document called an imaging charter, that go beyond routine medical practice.
Use a centralized image interpretation process to enhance the credibility of image assessments, ensure consistency, manage reader performance, and reduce variability.
Image readers should be blinded to treatment assignments and, in most cases, to other clinical data to prevent bias in the primary endpoint assessment.
Standardize all critical imaging procedures, including equipment settings, subject preparation, image acquisition protocols, site qualification processes, and ongoing quality control monitoring.
Establish clear procedures for image data transfer, quality assessment, locking, and archiving to maintain data integrity and ensure a verifiable audit trail.

Regulatory Considerations
Sponsors are encouraged to submit the imaging charter to the FDA for review, as compliance with the charter is an important part of verifying the trial's data integrity.
The use of investigational imaging equipment, software, or interpretation tools in a clinical trial must comply with all applicable FDA regulations, including investigational device exemption (IDE) requirements.
Imaging source data and records must be retained for a minimum of two years after a marketing application is approved or an investigation is discontinued, as specified in 21 CFR 312.
The final report submitted to the FDA for review should thoroughly document all imaging processes that took place during the trial, from acquisition and interpretation to data transfer.
The clinical protocol and consent forms must describe all imaging-related risks to subjects, such as radiation exposure, for review by institutional review boards (IRBs).