K223425 · Mdai, Inc. · LLZ · Feb 10, 2023 · Radiology
Device Facts
Record ID
K223425
Device Name
MD.ai Viewer
Applicant
Mdai, Inc.
Product Code
LLZ · Radiology
Decision Date
Feb 10, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
Software as a Medical Device
Indications for Use
MD.ai Viewer is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM and non-DICOM medical images and other healthcare data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data. MD.ai Viewer allows users to perform image manipulations, including window/level, rotation, measurement and markup. MD.ai Viewer provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data. Mobile usage is for reference and referral only. MD.ai Viewer is not intended for primary mammography interpretation.
Device Story
Software-based medical image viewer; operates on off-the-shelf workstations/web browsers. Inputs: DICOM/non-DICOM medical images (JPEG, PNG) from PACS, VNA, cloud, or local servers. Backend module handles data connection/processing; frontend web-based module renders images. Features: 2D/3D visualization (MPR, MIP), image manipulation (window/level, rotation, measurement, markup), and image processing filters (CLAHE). Used by radiologists, physicians, nurses, and technologists in clinical settings or remotely. Facilitates diagnosis by providing access to imaging data; supports collaboration via secure sharing. Employs HTTPS for encrypted transmission/storage; includes audit trails for HIPAA-compliant data management. Preloads lower-resolution images during scrolling to optimize performance on low-bandwidth networks.
Clinical Evidence
No clinical performance data or animal studies were performed. Evidence consists of non-clinical performance testing verifying design requirements and validation of measurement features using Digital Reference Objects compared against reference device K202335.
Technological Characteristics
Software-only, zero-footprint, browser-based viewer. Operates on off-the-shelf hardware. Supports DICOM and non-DICOM (JPEG, PNG) formats. Connectivity via HTTPS. Features include 2D/3D visualization (MPR, MIP), CLAHE histogram equalization, and measurement tools. Access control and audit trails implemented.
Indications for Use
Indicated for healthcare professionals to display, store, transfer, and measure DICOM and non-DICOM medical images to aid in diagnosis. Mobile usage is for reference and referral only. Not indicated for primary mammography interpretation.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue square. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
MDAI Inc. % Leon Chen CEO 110 Wall Street NEW YORK NY 10005
Re: K223425
February 10, 2023
Trade/Device Name: MD.ai Viewer Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: November 9, 2022 Received: November 14, 2022
Dear Leon Chen:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
{1}------------------------------------------------
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
{2}------------------------------------------------
## Indications for Use
510(k) Number (if known) K223425
Device Name MD.ai Viewer
## Indications for Use (Describe)
MD.ai Viewer is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM and non-DICOM medical images and other healthcare data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.
MD.ai Viewer allows users to perform image manipulations, including window/level, rotation, measurement and markup. MD.ai Viewer provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data.
Mobile usage is for reference and referral only.
MD.ai Viewer is not intended for primary mammography interpretation.
| Type of Use (Select <i>one</i> or <i>both</i> , as applicable) |
|----------------------------------------------------------------|
|----------------------------------------------------------------|
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
## CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
## *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{3}------------------------------------------------
Image /page/3/Picture/0 description: The image shows the logo for MD.ai. The letters "M" and "D" are in a light blue color, while the ".ai" is in black. The logo is simple and modern, and the use of color makes it visually appealing.
# K223425
## Administrative Information
| Submitter: | MDAI Inc |
|---------------------------------------|-------------------------------------------|
| Submission Type: | Traditional 510(k), New device |
| Address: | 110 Wall Street Suite 6-028, NY, NY 10005 |
| Phone Number: | 917-725-1883 |
| Contact Person/Company Representative | Leon Chen CEO, MDAI Inc |
| Email: | fda@md.ai |
## DEVICE INFORMATION
| Trade Name | MD.ai Viewer |
|-------------------|---------------------------------------------|
| Common Name | Medical image viewing and analysis software |
| Product Code | LLZ |
| Regulation Number | 892.2050 |
| Regulatory Class | Class 2 |
| Regulatory Name | System, Image Processing, Radiological |
| Review Panel | Radiology |
{4}------------------------------------------------
## Predicate Device Information
| Predicate Device Name | eUnity |
|---------------------------|----------------------------------------|
| Predicate Device K Number | K172490 |
| Product Code | LLZ |
| Regulation Number | 892.2050 |
| Regulatory Class | Class 2 |
| Regulatory Name | System, Image Processing, Radiological |
| Review Panel | Radiology |
To our knowledge the predicate device has not been subject to a design related recall.
## REFERENCE DEVICE
| Reference Device Name | Ambra PACS including Ambra ProViewer |
|---------------------------|----------------------------------------|
| Predicate Device K Number | K202335 |
| Product Code | LLZ |
| Regulation Number | 892.2050 |
| Regulatory Class | Class 2 |
| Regulatory Name | System, Image Processing, Radiological |
| Review Panel | Radiology |
## Device Description
MD.ai Viewer is a software-based medical image viewer used with off-the-shelf workstation and web browsers for the 2D & 3D visualization of DICOM and non-DICOM medical images. MD.ai Viewer is intended for storage, display, manipulation, measurement and processing of radiological data, including images, reports and other clinical information. It has the following primary features and functions
- . Zero-footprint HTML5 medical image upload, transfer and display of medical images between facilities
{5}------------------------------------------------
# MD.ai
## 510(k) Summary MD.ai Viewer (21 CFR 807.92)
- Easy access to images for all participants in the healthcare process, including radiologists, technologists, physicians, nurses and other patient care practitioners
- Serve as information and data management system for for DICOM and non-DICOM medical images
- . Tools for image manipulation, annotation and measurement.
- . Metadata information and orientation labels display
- . Advanced image manipulation functions like view synchronization across series, 3D visualization like MIP and MPR
- . Advanced image processing filters like histogram equalization (CLAHE) filter to aid in visualization of pathological features in the images
- . Encrypted transmission of medical images through secured networks
- . Encrypted storage of medical images
- . HIPAA-compliant data management, including centralized storage of user activities via audit trails.
- . Management of users, roles, and permissions
{6}------------------------------------------------
510(k) Summary MD.ai Viewer (21 CFR 807.92)
Image /page/6/Figure/2 description: The image shows a diagram of a system architecture for a medical imaging platform. On the left side, under the label "Users", are icons representing physicians, radiologists, project admins, and image upload, all connected to a cloud labeled "MD.ai Viewer" via HTTPS. On the right side, under the label "Health System", a proxy server is connected to PACS and modality via DICOM, and cloud storage and identity provider via HTTPS, all connected to the cloud labeled "MD.ai Viewer" via HTTPS.
MD.ai Viewer consists of configurable software-only modules that display and process digital medical images, and associated medical information to aid in the day-to-day operations and workflow of clinicians and healthcare practitioners. The web browser based medical image viewer serves as the frontend module which users interact with in viewing the imaging data. The backend module handles the connection and processing of data from a variety of sources within the health system, in view of preparing visualizations to be rendered by the viewer.
MD.ai Viewer can connect and access the medical images across different sources in a health system: an existing PACS or VNA, cloud storage or local server-based storage. Users can also upload images securely into MD.ai Viewer which can be shared and enables collaboration with other users. The data connection and imaging data processing is handled by MD.ai Viewer backend module which supports the standardized transmission protocol as defined in the DICOM standard. In situation where secure network link is not available between health system and MD.ai cloud instance, the MD.ai Viewer proxy server can provide a secure and encrypted transfer of imaging data.
Users interact with MD.ai Viewer through a standard web browser, thus providing access to full quality images from anywhere and supporting a greater efficiency for care. MD.ai Viewer utilizes authorization and authentication mechanisms that enforces authorized users to access the
{7}------------------------------------------------
imaging data. The system extends beyond the hospital and its internal network. With proper authorization, MD.ai Viewer can be accessed by clinical users outside of the hospital network. This way referring physicians can easily call up the imaging data of their patients or external expert accessing the imaging data for additional opinion.
MD.ai Viewer provides end-users with the ability for industry standard features such as Window/ Level, Image Flip and Rotate, Invert, Hanging Protocol, Image Measurements, and Keyboard/Mouse shortcuts. Images are initially displayed in the 2D view mode, but with the ability to toggle into advanced viewing mode of 3D/MPR for relevant exam type. It supports processing and displaying Multiplanar Reconstruction (MPR) and different intensity rendering modes based on user-defined slab thickness. It also provides image processing filters like histogram equalization (CLAHE) filter to better visualize pathological features when displaying low contrast images from some modality devices.
MD.ai Viewer provides an image rendering mechanism that preloads lower resolution images during image scrolling to improve interactivity and performance for users operating in lower network bandwidth while the full quality image is loaded in the background.
The use of a secure data transmission protocol and data encryption ensure high data security for data management via the Internet. MD.ai Viewer tracks user activity via audit trails and stores the audit data on the centralized server
## SUBJECT DEVICE INDICATIONS FOR USE
MD.ai Viewer is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM and non-DICOM medical images and other healthcare data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.
MD.ai Viewer allows users to perform image manipulations, including window/level, rotation, measurement and markup. MD.ai Viewer provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data.
Mobile usage is for reference and referral only.
MD.ai Viewer is not intended for primary mammography interpretation.
{8}------------------------------------------------
## SUBJECT DEVICE CONTRAINDICATIONS FOR USE
MD.ai Viewer is not indicated for interpreting mammography.
## Substantial Equivalence Discussion
## Predicate Device Indications for Use
eUnity is a software application that displays medical image data and associated clinical reports to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.
eUnity allows users to perform image manipulations, including window/level, rotation, measurement and markup. eUnity provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data, and mobile access to images.
eUnity displays both lossless and lossy compressed images. For lossy images, the medical professional user must determine if the level of loss is acceptable for their purposes. Display monitors or mobile devices used for reading medical images for diagnostic purposes must comply with applicable regulatory approvals and with quality control requirements for their use and maintenance. For mobile diagnostic usage when a full workstation is not available.
Mobile usage for mammography is for reference and referral only.
### 1.1. Subject Device Indications for Use
MD.ai Viewer is a software-based viewer intended to be used with off-the-shelf hardware for the display of DICOM and non-DICOM medical images and other healthcare data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.
MD.ai Viewer allows users to perform image manipulations, including window/level, rotation, measurement and markup. MD.ai Viewer provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data.
{9}------------------------------------------------
Mobile usage is for reference and referral only. MD.ai Viewer is not intended for primary mammography interpretation.
### Indications for Use Equivalence Discussion 1.2.
The Predicate Device (eUnity) and Subject Device (MD.ai Viewer) are both software-based viewer designed to receive, display, and measure image data and indicated for use to aid in diagnosis for trained healthcare professionals.
Both devices shared common technological characteristics. Both are intended as a zero-download, zero-footprint browser-based viewer. By leveraging the industry standard browser-based technology, both devices enable image data display with off-the-shelf hardware.
Both devices are capable of displaying image data in DICOM format from imaging modalities that are standard in the provision of care as well as non-DICOM image data captured in widely used JPEG or PNG format.
Further comparison between the two devices as performed in Device Comparison Table.
The indications for use of the Predicate Device and Subject Device are substantially equivalent.
| Characteristic | Subject<br>Device:MD.ai Cloud<br>Platform | Primary<br>Predicate:Client<br>Outlook eUnity | Comment |
|------------------------------------------------------------------------------|---------------------------------------------------------------------|-----------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| User Install Requirements | Thin Client - no<br>install, runs within<br>browser | Thin Client - no install,<br>runs within browser | Equivalent |
| Communication | DICOM, non-DICOM | DICOM, non-DICOM,<br>IHE | The Subject Device supports the DICOM<br>communication protocol used within the<br>IHE framework. Similar to the Predicate<br>Device, it fulfills the role of Image Display<br>described in the IHE Radiology Integration<br>Profile. |
| Modalities | CR, CT, DX, IVOCT,<br>MR, MG, NM, OCT,<br>OT, PT, RF, SC, US,<br>XA | CR, CT, DX, ECG, MR,<br>MG, NM, OP, PR, PT, RF,<br>SC, SR, US, XA, XL | Key modalities used in provision of care<br>are supported. |
| Window Level,<br>Rotate/Pan/Zoom, Reset,<br>Presets, Invert | Yes | Yes | Equivalent |
| Multi-study viewing, Image<br>Export, Image Sharing<br>compliant | Yes | Yes | Equivalent |
| Metadata Display/Hide | Yes | Yes | Equivalent |
| Orientation Labels,<br>Keyboard Shortcuts | Yes | Yes | Equivalent |
| Measurements,<br>Annotations | Yes | Yes | Equivalent |
| Full Screen Mode,<br>Multi-monitor, Layouts | Yes | Yes | Equivalent |
| Linking Series, Image<br>Scrolling, Linked Scrolling,<br>Reference Lines | Yes | Yes | Equivalent |
| GSPS, KIN | No | Yes | Subject Device manages the presentation<br>state as an internal record that allows<br>Intended users to interact with the<br>presentation state within the device. The<br>Subject Device currently does not support<br>the export of presentation state in GSPS<br>and KIN. The difference does not impact<br>the equivalence for the Predicate and<br>Subject Device. |
| Multiplanar reformat (MPR) | Yes | Yes | Equivalent |
| Maximum Intensity<br>Projection (MIP) | Yes | Yes | Equivalent |
| Oblique, Volume<br>Rendering, Opacity Presets,<br>Scalpel tool, bone removal | No | Yes | The Predicate Device provides additional<br>capabilities in 3D advanced visualization<br>which the Subject Device does not have.<br>The difference does not impact the<br>equivalence for the Predicate and Subject<br>Device. |
| Sharpen, blur, emboss,<br>edge filters | Yes | No | These filters are part of the image<br>manipulation tool included in the Subject<br>Device which the users could use when<br>displaying the image data. The difference<br>does not impact the equivalence for the<br>Predicate and Subject Device. |
| Histogram Equalization<br>filter | Yes | No | These filters are part of the image<br>manipulation tool included in the Subject<br>Device which the intended users could<br>use when displaying the image data. The<br>difference does not impact the<br>equivalence for the Predicate and Subject<br>Device. |
| Data Encryption | HTTPS | HTTPS | Equivalent |
| Data Security | Stored on server | Stored on server | Equivalent |
| Built-in access<br>control or parent<br>application access<br>control | Built-in access control<br>or parent application<br>control | Equivalent | Equivalent |
## Device Comparison Table
{10}------------------------------------------------
# MD.ai
# 510(k) Summary MD.ai Viewer (21 CFR 807.92)
{11}------------------------------------------------
## Non-Clinical Performance Testing
The MD.ai Viewer was subjected to non-clinical performance testing which verified that the design requirements were successfully met. Intended use and user needs were successfully validated. The measurement features of MD.ai Viewer were validated using Digital Reference Objects and comparison with the reference device - K202335.
As the intended use, functionality and performance of the subject MD.ai Viewer and predicate eUnity device are equivalent, the result of the non-clinical performance testing is evidence that the MD.ai Viewer performs in an equivalent manner to the eUnity device.
## Clinical Performance Data Equivalence Discussion
No clinical performance data were performed for this submission.
## Animal Studies Data
No animal studies were performed for this submission.
## Conclusion
Based on the comparisons and analyses detailed above in this summary, we believe that the information and performance test reports of the MD.ai Viewer provided in this Traditional
{12}------------------------------------------------
Image /page/12/Picture/0 description: The image shows the logo for MD.ai. The letters "MD" are in a light blue color, while the ".ai" is in black. The letters are simple and modern.
510(k) submission are sufficient to demonstrate the safety and effectiveness as compared to the eUnity (K172490) predicate device.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.