Similar nodule detection sensitivity and FP/scan compared to predicate
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Pulmonary Nodule Detection
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AUC increase of 0.073 (95% CI: 0.020, 0.125); reading time decrease of 23s (95% CI: -42, -3)
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Pivotal reader study: 249 scans
10 (board-certified radiologists)
Indications for Use
Computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest.
Device Story
InferRead Lung CT.AI is a SaaS-based computer-assisted detection (CADe) tool for chest CT examinations. It receives DICOM images from PACS, RIS, or CT scanners via a browser/server architecture. The system analyzes images to identify pulmonary nodules, providing bounding box annotations and quantitative measurements (max axial diameter, mean diameter, volume). It does not modify the original CT scan; annotations are overlaid on the original images and can be toggled. Used in clinical settings by radiologists to assist in nodule detection. The output provides adjunctive information to support clinical decision-making, potentially improving detection sensitivity and reducing reading time compared to unaided review.
Clinical Evidence
Retrospective, fully crossed, multi-reader multi-case (MRMC) study with 10 board-certified radiologists and 249 scans. Primary endpoint: LROC AUC improvement. Results: Aided vs. unaided AUC increase of 0.073 (95% CI: 0.020, 0.125), p<0.05. Secondary endpoint: reading time. Results: Aided vs. unaided reading time decrease of 23s (95% CI: -42, -3). Study demonstrates superior detection performance with the device compared to unaided read.
Indicated for use as computer-assisted reading tools to aid radiologists in detecting pulmonary nodules during review of chest CT examinations in asymptomatic populations. Requires both lungs in the field of view. Provides adjunctive information; not intended for use without the original CT series. Prescription use only.
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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Beijing Infervision Technology Co., Ltd. % Matt Deng 1900 Market St., 8th Floor PHILADELPHIA, PA 19103
July 2, 2020
### Re: K192880
Trade/Device Name: InferRead Lung CT.AI Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: OEB, LLZ Dated: June 2, 2020 Received: June 3, 2020
Dear Matt Deng:
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
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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 (OS) 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,
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)
#### K192880
Device Name InferRead Lung CT.AI
#### Indications for Use (Describe)
InferRead Lung CT.AI is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during the review of CT examinations of the chest on an asymptomatic population. InferRead Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.AI provides adjunctive information and is not intended to be used without the original CT series.
Type of Use (Select one or both, as applicable)
| <div> <span> <span style="font-size:16px">☒</span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> |
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| <div> <span> <span style="font-size:16px">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |
|------------------------------------------------------------------------------------------------------------------------|
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# Summary of 510(k)
# Beijing Infervision Technology Co., Ltd. K192880
This 510(k) Summary is in conformance with 21CFR 807.92
| Submitter: | Beijing Infervision Technology Co., Ltd.<br>Room B401, 4th Floor, Building 1,<br>No.12 Shangdi Information Road.<br>Haidian District, Beijing, 100085<br>Phone: +86 10-86462323 |
|------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Contact: | Mr. Matt Deng<br>Email: dyufeng@infervision.com<br>Phone: 919-491-5457 |
| Company Contact: | Xiaoyan Fan<br>Project Leader |
| Date Prepared: | July 1, 2020 |
# Device Name and Classification
| Trade Name: | InferRead Lung CT.AI |
|-----------------------|--------------------------------------------------------------|
| Classification: | Class II |
| Regulation Number: | 21 CFR 892.2050, Picture archiving and communications system |
| Classification Panel: | Radiology |
| Product Code: | OEB, LLZ |
## Predicate Device:
| Trade Name | ClearRead CT |
|----------------------|--------------------------------------------------------------|
| Classification | Class II |
| 510(k) Number | K161201 |
| Regulation Number | 21 CFR 892.2050, Picture archiving and communications system |
| Classification Panel | Radiology Panel |
| Product Code | OEB, LLZ |
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## Device Description
InferRead Lung CT.AI uses the Browser/Server architecture, and is provided as Service (SaaS) via a URL. The system integrates algorithm logic and database in the same the simplicity of the system and the convenience of system maintenance. The server is able to accept chest CT images from a PACS system, Radiological Information System) or directly from a CT scanner, analyze the images and provide output annotations regarding lung nodules. Users are an existing PACS system to view the annotations. Dedicated servers can be located at hospitals and are directly connected to the hospital networks. The software consists of 4 modules which are Image reception (Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Image display (NeoViewer).
## Indications for Use
InferRead Lung CT.AI is comprised of computer assisted to aid the radiologist in the detection of pulmonary nodules during the review of CT examinations of the chest on an asymptomatic population. InferRead Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.Al provides adjunctive information and is not intended to be used without the original CT series.
### Risk Analysis Method
The InferRead Lung CT.AI was assessed to determine risks to health associated with the use of the device. Risks related to safety and usability were considered. A risk analysis was conducted in accordance with ISO 14971:2007, Medical devices – Application of risk management to medical devices. Several risks were assessed, including, but not limited to device malfunction and improper use.
#### Substantial Equivalence
InferRead Lung CT.AI is substantially equivalent to the ClearRead CT (K161201) currently on the market.
The table below provides a detailed comparison of InferRead Lung CT.AI to the predicate device.
| Item | InferRead Lung CT.AI<br>(Subject Device) | ClearRead CT (K161201)<br>(Predicate Device) | Comparison |
|----------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for Use | InferRead Lung CT.AI is<br>comprised of computer<br>assisted reading tools<br>designed to aid the<br>radiologist in the detection<br>of pulmonary nodules | ClearRead CT™ is<br>comprised of computer<br>assisted reading tools<br>designed to aid the<br>radiologist in the detection of<br>pulmonary nodules during | The indications for use of InferRead Lung<br>CT.AI are identical to the indications for<br>use of the previously cleared ClearRead<br>CT. |
| | | | |
| | during the review of CT examinations of the chest on an asymptomatic population. InferRead Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.AI provides adjunctive information and is not intended to be used without the original CT series. | review of CT examinations of the chest on an asymptomatic population. The ClearRead CT requires both lungs be in the field of view. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series. | |
| Intended Use | Computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest. | Computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest. | The intended use of InferRead Lung CT.AI is identical to the intended use of the previously cleared ClearRead CT. |
| Accessories/Tools<br>Required by the<br>User (Platform) | Must be used in conjunction with a PACS system or an Image Viewer that reads DICOM images. | Must be used in conjunction with a PACS system or an Image Viewer that reads DICOM images. | The accessories required by the user for InferRead Lung CT.AI are identical to the accessories required by the user for the previously cleared ClearRead CT. |
| User Access<br>Point | Post Processing Application | Post Processing Application | The user access point of InferRead Lung CT.AI is identical to the user access point of the previously cleared ClearRead CT. |
| Image Input | DICOM | DICOM | The image input of InferRead Lung CT.AI is identical to the image input of the previously cleared ClearRead CT. |
| Type of Scans | CT | CT | The type of scans for InferRead Lung CT.AI are identical to the type of scans for the previously cleared ClearRead CT. |
| Automatically<br>Locate and<br>Identify Lung | Yes | Yes | The function of automatically locating and identifying lung nodules for InferRead Lung CT.AI is identical to the function of |
| | | | |
| | | | lung nodules for the previously cleared<br>ClearRead CT. |
| Modifies the<br>Original CT<br>Scan | No | Yes | According to the device description of the<br>predicate device found in its 510K<br>summary, "ClearRead CT is a dedicated<br>post-processing application that generates<br>a secondary vessel suppressed Lung CT<br>series with CADe marks and associated<br>region descriptors intended to aid the<br>radiologist in the detection of pulmonarynodules." ClearRead CT modifies the<br>original scan by performing vessel<br>suppression. On the contrary, InferRead<br>Lung CT.AI does not modify the original<br>scan, only shows the locations of<br>pulmonary nodules.<br>This difference does not affect the<br>intended use or safety and effectiveness of<br>the device. |
| Requires a<br>Disjoint<br>Comparison with<br>the Original CT<br>Scan | No | Yes | Since ClearRead CT creates a secondary<br>vessel suppressed Lung CT series, it<br>requires the user to have an original CT<br>series on a separate window. There are 2<br>series open at the same time. We refer to<br>this setup as "disjoint comparison". On the<br>contrary, InferRead Lung CT.AI does not<br>require 2 series open, as its CADe marks<br>overlay with the original CT scan and can<br>be toggled on and off. Therefore,<br>InferRead Lung CT.AI does not require<br>"disjoint comparison"<br>This difference does not affect the<br>intended use or safety and effectiveness of<br>the device. |
| | | | InferRead Lung CT.AI display the original<br>CT scans. |
| Nodule Marking | A bounding box is provided<br>around nodules | A bounding box is provided<br>around nodules | The function of providing a bounding box<br>around nodules for InferRead Lung CT.AI<br>is identical to the indications for use of the<br>previously cleared ClearRead CT. |
| Provides Nodule<br>Characteristics | Yes, the maximum axial<br>plane longest diameter,<br>mean diameter and volume<br>information are provided. | Yes, the volume, maximum<br>axial plane diameter,<br>minimum axial plane<br>diameter, and average<br>density in Hounsfield units<br>are provided. (from website) | InferRead Lung CT.AI has mean diameter<br>measurement function that is not provided<br>by predicate device. Mean diameter is the<br>average of maximum axial plane diameter<br>and minimum axial plane diameter. And<br>this difference does not affect the safety<br>and effectiveness of the device. |
| Detection<br>Target(s) | Solid, Sub Solid (part solid<br>and ground glass) nodules | Solid, Sub Solid (part solid<br>and ground glass) nodules | The detection targets of InferRead Lung<br>CT.AI are identical to the detection targets<br>of the previously cleared ClearRead CT.<br>They have the same definition principles<br>for actionable nodules classification. |
| Size of Detection<br>Targets | 4mm and above, supports<br>visualization of nodules<br>smaller than 4mm | 5mm and above, supports<br>visualization of nodules<br>smaller than 5mm | The InferRead Lung CT.AI detects smaller<br>nodules. This difference does not affect<br>the intended use or safety and<br>effectiveness of the device. This difference<br>has been addressed with the completion of<br>stand-alone performance characteristics<br>testing. |
## Detailed Comparison of the Subject and Predicate Devices
Infervision InferRead Lung CT.AI Traditional 510(k)
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#### Testing Summary
#### Non-clinical performance evaluation
Software testing was performed in accordance with General Principles of Software Validation; Final Guidance for Industry and FDA Staff (January 11, 2002). Software testing which included unit testing, software integration testing and software system testing was performed on InferRead Lung CT.AI. It was demonstrated that InferRead Lung CT.AI, when used according to operating instructions, met all requirement specifications. All system functionalities were tested and passed. Measurement performance was validated on phantom and clinical data to assess reproducibility and accuracy. Standalone performance testing which included chest CT scans from patients who underwent lung cancer screening was performed to validate detection accuracy of InferRead Lung CT.AI. Results showed that InferRead Lung CT.AI had similar nodule detection sensitivity and FP/scan compared to those of the predicate device. Based on the results of verification and validation tests it is concluded that InferRead Lung CT.AI is effective and safe in the detection of nodules.
### Clinical performance evaluation
A pivotal reader study which was a retrospective, fully crossed, multi-reader multi-case (MRMC) study was conducted to validate that the device conformed to the defined user needs and intended uses. A total of 10 board-certified radiologists and a collection of 249 scans were involved in the reader study. The purpose of the reader study was to validate that with the aided of InferRead Lung CT.AI radiologists' nodule detection performance could significantly improve without significantly increasing reading time at a significance level alpha of 0.05 (two-sided). The reader study measured the area under the curve (AUC) of the localization receiver operating characteristic (LROC) response when using InferRead Lung CT.AI relative to the unaided read. The study also measured the radiologists' interpretation time when using InferRead Lung CT.AI relative to unaided interpretations. Results showed that InferRead Lung CT.AI was found to significantly increase the AUC (Aided - Unaided: 0.073, 95%CI: 0.020, 0.125), indicating the detection performance through using the device is superior to the unaided read for detecting nodules. Moreover, InferRead Lung CT.AI was also found to decrease reading times (Aided - Unaided: -23s, 95%Cl: -42, -3). In conclusion, the pivotal study showed that with the aided of InferRead Lung CT.AI radiologists' nodule detection performance could significantly improve without significantly increasing reading time.
#### Substantial Equivalence Conclusions
In conclusion, the intended use for InferRead Lung CT.AI is the same as that of the previously cleared ClearRead CT (K161201). The technological characteristics demonstrate that the InferRead Lung CT.AI is substantially equivalent to the previously cleared ClearRead CT (K161201), and the testing shows that the InferRead Lung CT.AI is substantially equivalent to the previously cleared ClearRead CT (K161201) and assures that the InferRead Lung CT.AI is as safe and effective as the previously cleared ClearRead CT (K161201).
#### Conclusion
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The 510(k) Pre-market Notification for InferRead Lung CT.AI contains adequate information and data to determine that InferRead Lung CT.AI is as safe and effective as the legally marketed predicate device.
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.