ClariSIGMAM is a software application intended for use with compatible full field digital mammography systems. ClariSIGMAM calculates percent breast density defined as the ratio of fibroglandular tissue to total breast area estimates. ClariSIGMAM uses this numerical value to provide breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) to aid interpreting physicians in the assessment of breast tissue composition. ClariSIGMAM produces adjunctive information. It is not a diagnostic aid.
Device Story
Standalone software application; analyzes 'for presentation' 2D digital mammograms; calculates fibroglandular tissue area, total breast area, and percent breast density; categorizes density into BI-RADS groups (A+B fatty, C+D dense). Operates on mammography workstations or PACS; receives images via DICOM protocol; outputs structured reports or secondary captures. Used by radiologists/interpreting physicians to provide adjunctive information for breast tissue assessment; supports clinical decision-making regarding breast density. Benefits include automated, reproducible density quantification to assist physician interpretation.
Clinical Evidence
No clinical studies performed. Bench testing only. Validation used a dataset of 837 images compared against a consensus reference standard from four expert readers. Results for binary density classification (Fatty vs. Dense) showed 86.6% accuracy for fatty and 87.3% for dense, with a Kappa of 0.734 (95% CI: 0.688, 0.781).
Technological Characteristics
Standalone software; DICOM-compliant; compatible with GE and Hologic FFDM systems. Uses automated image processing to segment fibroglandular tissue and breast area. Standards: ISO 14971 (risk management), NEMA PS 3.1-3.20 (DICOM).
Indications for Use
Indicated for use with compatible full field digital mammography systems to aid interpreting physicians (radiologists/specialists) in assessing breast tissue composition by calculating percent breast density and providing BI-RADS density group information (fatty vs. dense). Not a diagnostic aid.
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).
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ClariPi Inc. % Harry Park President ClariPi Detroit Office 1645 Park Creek Ct. Rochester Hills DETROIT MI 48309
September 10, 2021
Re: K203785
Trade/Device Name: ClariSIGMAM Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: August 9, 2021 Received: August 9, 2021
Dear Harry Park:
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 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-reporting
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combination-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 medical devices and radiation-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
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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## Indications for Use
510(k) Number (if known) K203785
Device Name ClariSIGMAM
#### Indications for Use (Describe)
ClariSIGMAM is a software application intended for use with compatible full field digital mammography systems. ClariSIGMAM calculates percent breast density defined as the ratio of fibroglandular tissue to total breast area estimates. ClariSIGMAM uses this numerical value to provide breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) to aid interpreting physicians in the assessment of breast tissue composition. ClariSIGMAM produces adjunctive information. It is not a diagnostic aid.
Type of Use (Select one or both, as applicable)
| <span style="font-size:10pt">☒</span> Prescription Use (Part 21 CFR 801 Subpart D) |
|------------------------------------------------------------------------------------|
| <span style="font-size:10pt">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) |
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# 510(k) Summary
Image /page/3/Picture/1 description: The image shows the logo for "Clariπ MEDICAL IMAGING SOLUTIONS". The word "Clari" is in bold black font, and the pi symbol is in blue. Below the word "Clariπ" is the phrase "MEDICAL IMAGING SOLUTIONS" in a smaller, gray font.
### K203785
This 510(k) Summary is being submitted in accordance with the requirements of as required by section 807.92(c).
- l. SUBMITTER
ClariPi Inc. 3F, 70-15, Ihwajang-gil, Jongno-gu Seoul, Korea, Republic of [03088] Tel: +82-2-741-3014 Fax: +82-2-743-3014 Email: claripi@claripi.com
Contact person: Ms. Hyun-Sook Park, CEO Date Prepared: September 09, 2021
- II. DEVICE
Name of Device: ClariSIGMAM Common or Usual Name: Automated Radiological Image Processing Software Classification Name: Medical image management and processing (21 CFR 892.2050) Regulatory Class: II Product Code: QIH
#### III. PREDICATE DEVICE
This predicate has not been subject to a design-related recall. The ClariSIGMAM software device, addressed in this premarket notification, is substantially equivalent to the following commercially available software:
| Device Classification Name | System, Image processing, Radiological |
|-----------------------------|--------------------------------------------------------------------------|
| 510(k) Number | K170540 |
| Device Name | DM-Density |
| Applicant | Densitas, Inc.<br>1344 Summer Street, Suite 311.2<br>Halifax, CA B3h 0A8 |
| Regulation Number | 892.2050 |
| Classification Product Code | LLZ |
| Date Received | 02/13/2017 |
| Decision Date | 02/23/2018 |
| 510k Review Panel | Radiology |
Further to the predicate device, ClariPi has identified the following currently marketed devices as reference predicate device of proposed ClariSIGMAM:
| Device Classification Name | System, Image processing, Radiological |
|----------------------------|----------------------------------------|
| 510(k) Number | K132742 |
| Device Name | iReveal (formerly M-Vu Breast Density) |
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Image /page/4/Picture/0 description: The image shows the logo for Clariπ Medical Imaging Solutions. The word "Clari" is in black, followed by a lowercase "i" with a dot above it, also in black. To the right of the "i" is the Greek letter pi in blue. Below the word "Clariπ" are the words "MEDICAL IMAGING SOLUTIONS" in a smaller font size.
| Applicant | iCAD, Inc. (formerly VuCOMP, Inc.)<br>98 Spit Brook Road, Suite 100<br>Nashua, NH 03062 USA |
|-----------------------------|---------------------------------------------------------------------------------------------|
| Regulation Number | 892.2050 |
| Classification Product Code | LLZ |
| Date Received | 09/03/2013 |
| Decision Date | 12/03/2013 |
| 510k Review Panel | Radiology |
#### IV. DEVICE DESCRIPTION
ClariSIGMAM software is a standalone software application that automatically analyzes "for presentation" 2D digital mammograms to assess breast tissue composition.
The software assesses the breast density of women and generates a breast density group information for the patient (BI-RADS A+B as fatty and BI-RADS C+D as dense) in accordance with the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) density classification scale.
Output of breast density by ClariSIGMAM is designed to display on a mammography workstation or PACS as DIOCM mammography structured report or secondary capture. The reports are configured to provide the following data:
- Breast area (cm²) for each breast
- Fibroglandular tissue area (cm²) for each breast
- Percent breast density for each breast
- · Breast density group information for the patient (BI-RADS A+B as fatty and BI-RADS C+D as dense)
#### V. INDICATIONS FOR USE
ClariSIGMAM is a software application intended for use with compatible full field digital mammography systems. ClariSIGMAM calculates percent breast density defined as the ratio of fibroglandular tissue to total breast area estimates. ClariSIGMAM uses this numerical value to provide breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) to aid interpreting physicians in the assessment of breast tissue composition. ClariSIGMAM produces adjunctive information. It is not a diagnostic aid.
#### VI. SUBSTANTIAL EQUIVALENCE TABLE
The subject device (ClariSIGMAM) is substantially equivalent to the predicate devices (K170540, DM-Density) which also calculate breast density as a ratio of fibro-glandular tissue and total breast area estimates. Both devices are not to be used as diagnostic aids, but to provide adjunctive information only. Users in both cases must be qualified medical practitioners and exercise their professional judgment when formulating diagnostic decisions and selecting appropriate treatment paths that are supported by breast density data. Both devices utilize standard DICOM communications protocol to receive Digital mammograms, processes them and automatically transfers the DICOM summary report to the picture archiving and communication system (PACS).
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Image /page/5/Picture/0 description: The image shows the logo for "ClariPi MEDICAL IMAGING SOLUTIONS". The word "Clari" is in bold black font, and the pi symbol is in a light blue color. Underneath the logo is the text "MEDICAL IMAGING SOLUTIONS" in a smaller, lighter font.
The difference lies in image source modalities and breast density category. The subject device is compatible and validated to certain Hologic and GE digital mammography systems whereas the predicate device (K170540) is compatible with Hologic Selenia Dimensions and Hologic Lorad Selenia the reference device (K132742) is compatible with all digital radiography (DR) systems and computer radiography (CR) systems. The predicate device reports four BI-RADS categories as the output of the device whereas the subject device reports dense (BI-RADS C+D) versus fatty (BI-RADS A+B). It has no effect on the safety or efficacy of the subject device and does not raise any potential safety risks, and the subject device is identical in performance to the legally marketed device.
| Item | Subject Device -<br>ClariSIGMAM | Predicate Device-<br>DM-Density<br>(K170540) | Reference Device -<br>M-Vu Breast Density<br>(K132742) |
|--------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended<br>Use /<br>Indication<br>for Use | ClariSIGMAM is a<br>software application<br>intended for use with<br>compatible full field<br>digital mammography<br>systems.<br>ClariSIGMAM<br>calculates percent<br>breast density defined<br>as the ratio of<br>fibroglandular tissue to<br>total breast area<br>estimates and<br>provides breast<br>density group<br>information (BI-RADS<br>A+B as fatty and BI-<br>RADS C+D as dense)<br>to aid radiologists in<br>the assessment of<br>breast tissue<br>composition.<br>ClariSIGMAM<br>produces adjunctive<br>information. It is not a<br>diagnostic aid. | DM-Density is a<br>software application<br>intended for use with<br>compatible full field<br>digital mammography<br>systems. DM-Density<br>calculates percent<br>breast density defined<br>as the ratio of<br>fibroglandular tissue to<br>total breast area<br>estimates. DM-Density<br>provides these<br>numerical values for<br>each breast as well as<br>a density category to<br>aid interpreting<br>physicians in the<br>assessment of breast<br>tissue composition.<br>DM-Density produces<br>adjunctive information.<br>It is not a diagnostic<br>aid. | M-Vu Breast Density is<br>a software application<br>intended for use with<br>digital mammography<br>systems. M-Vu Breast<br>Density calculates<br>breast density as a<br>ratio of fibroglandular<br>tissue and total breast<br>area estimates. M-Vu<br>Breast Density<br>provides these<br>numerical values for<br>each breast as well as<br>a density category to<br>aid radiologists in the<br>assessment of breast<br>tissue composition. M-<br>Vu Breast Density<br>produces adjunctive<br>information, It is not an<br>interpretive or<br>diagnostic aid. |
| Intended<br>User | Interpreting<br>Physicians,<br>Radiologists and<br>Specialists | Interpreting Physicians | Interpreting Physicians |
| Image<br>Source<br>Modalities | GE Senograph 2000D<br>GE Senograph DS<br>GE Senographe<br>Pristina<br>Hologic Selenia<br>Dimensions<br>Hologic Lorad Selenia | Hologic Selenia<br>Dimensions<br>Hologic Lorad Selenia | All digital radiography<br>(DR) systems and<br>computed radiography<br>(CR) systems |
| Image | DICOM digital | DICOM full field digital | DICOM digital |
| Item | Subject Device –<br>ClariSIGMAM | Predicate Device–<br>DM-Density<br>(K170540) | Reference Device –<br>M-Vu Breast Density<br>(K132742) |
| Format | mammography imager<br>– For Presentation;<br>RCC, LCC, RMLO,<br>LMLO | mammography imager<br>– For Presentation;<br>RCC, LCC, RMLO,<br>LMLO | mammography imager<br>– For Processing;<br>RCC, LCC, RMLO,<br>LMLO |
| Output<br>Data | For each breast:<br>• Area of fibroglandular<br>tissue ( $cm^2$ )<br>• Area of breast ( $cm^2$ )<br>• Area-based breast<br>density (%)<br>For each patient:<br>• Breast density group<br>information for the<br>patient (BI-RADS<br>A+B as fatty and BI-<br>RADS C+D as<br>dense) | BI-RADS 4th Ed.<br>For each breast:<br>• Area of fibroglandular<br>tissue ( $cm^2$ )<br>• Area of breast ( $cm^2$ )<br>• Area-based breast<br>density (%)<br>For each patient: DM-<br>Density breast density<br>grade and percent<br>breast density<br>BI-RADS 5th Ed.<br>For each patient: DM-<br>Density breast density<br>grade | For each breast:<br>• Area of fibroglandular<br>tissue ( $cm^2$ )<br>• Area of breast ( $cm^2$ )<br>• Area-based breast<br>density (%)<br>For each patient:<br>VuCOMP density<br>grade/BIRADS breast<br>density |
The following information compares the predicate devices and the subject device.
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Image /page/6/Picture/0 description: The image shows the logo for "Clariπ MEDICAL IMAGING SOLUTIONS". The word "Clari" is in black, and the pi symbol is in blue. The words "MEDICAL IMAGING SOLUTIONS" are in a smaller font size and are located below the word "Clari".
#### VII. PERFORMANCE DATA
Non-clinical performance testing has been performed on ClariSIGMAM, (the subject device) and demonstrates compliance with the following International and FDA-recognized consensus standards and FDA guidance document:
- . ISO 14971 Medical devices - Application of risk management to medical devices
- NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM) ●
- . Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005.
- Design Considerations and Pre-market Submission Recommendations for Interoperable ● Medical Devices issued September 6, 2017.
- . The subject device, was tested in accordance with the internal Verification and Validation processes of ClariPi Inc. Verification and Validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications, and the risk management results. Validation testing included:
- ClariSIGMAM-generated breast density estimates on substantial data sets were compared with Gold Standard breast density estimates which were established by generating breast density measurements using an interactive thresholding software (Cumulus, Sunnybrook Health Sciences Centre, Toronto, ON, Canada) by expert radiologist.
- Reproducibility for what is known to decrease breast density with age was evaluated by running ClariSIGMAM on a substantial data set.
- Breast density estimates with ClariSIGMAM were made on a paired set of mammograms with a maximum imaging period of a one year apart in order to assess if ClariSIGMAMgenerated breast density estimates were reproducible over time.
- ClariSIGMAM was run over substantial data sets and the breast density estimates for left and right breasts were compared to confirm that the results are similar for each view.
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Image /page/7/Picture/0 description: The image shows the logo for ClariPi Medical Imaging Solutions. The word "Clari" is in bold black font, followed by a stylized blue pi symbol. Below the word "ClariPi" is the text "MEDICAL IMAGING SOLUTIONS" in a smaller, lighter font.
- Comparison of breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) between experts' visual assessment and automated assessment with ClariSIGMAM was assessed on reference standard dataset for BI-RADS breast density category that was established by a consensus visual assessment of expert readers according to BI-RADS 5th Edition.
ClariSIGMAM results were compared to a consensus assessment from four expert readers' independent assessments of breast density category on a dataset that spanned all compatible FFDM systems. The results for the binary density task using are summarized below:
- · Confusion matrix for ClariSIGMAM and the reference standard on binary breast density task (BI-RADS A+B as fatty vs. BI-RADS C+D as dense).
| | | Readers' consensus | | |
|-------------|-------|--------------------|-------|----------|
| | | Fatty | Dense | Accuracy |
| ClariSIGMAM | Fatty | 293 | 63 | 86.6% |
| | Dense | 45 | 436 | 87.3% |
| | Total | 338 | 499 | |
n=837; Kappa 0.734 [0.688, 0.781]
The test results in this 510(k), demonstrate that ClariSIGMAM:
- . complies with the aforementioned international and FDA-recognized consensus standards and
- . FDA guidance document, and
- Meets the acceptance criteria and is adequate for its intended use. ●
Therefore, ClariSIGMAM, is substantially equivalent to the currently marketed predicate devices, in terms of safety and effectiveness.
#### Clinical Testing:
ClariSIGMAM does not require clinical studies to demonstrate substantial equivalence to the predicate devices.
#### VIII CONCLUSIONS
Verification and Validation activities required to establish safety and effectiveness of ClariSIGMAM, were performed. Testing involved system level tests, performance tests, and safety tests from risk analysis. These tests demonstrated the subject device meets pre-defined functionality requirements.
The subject device and predicate devices are substantially equivalent in the areas of technical characteristics, general function, application, and intended use. Test results with the substantial datasets demonstrate that the subject device is as safe and effective as the predicate devices. Therefore the subject device is substantially equivalent to the predicate devices.
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.