The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
Device Story
Standalone image analysis software; integrates with PACS/radiology workflows via DICOM. Inputs: frontal (AP/PA) chest X-rays in DICOM format. Processing: pre-processing module; core analysis using pre-trained convolutional neural networks (CNNs); post-processing module. Outputs: secondary DICOM series with labeled overlays (breathing tube tip and carina) and PDF report with preview images/textual summary. Used in clinical settings by physicians/radiologists. Provides adjunctive information for tube placement confirmation; does not replace original images. Benefits: facilitates accurate, rapid assessment of breathing tube position relative to carina.
Clinical Evidence
Bench testing only. Performance evaluated on 162 chest X-ray images. Ground truth established by manual annotation from three U.S. radiologists. Primary endpoints: localization accuracy of carina and breathing tube tip, and distance measurement error between them. Results: Carina absolute distance mean 2.15mm (95% CI upper bound 2.35mm); breathing tube tip absolute distance mean 1.97mm (95% CI upper bound 2.13mm); distance error mean 1.98mm (95% CI upper bound 2.20mm). All results met preset acceptance criteria.
Technological Characteristics
Standalone software; DICOM-based connectivity. Core processing uses pre-trained convolutional neural networks (CNNs). Outputs include DICOM overlays and PDF reports. No physical materials or energy sources. Software level of concern: Moderate.
Indications for Use
Indicated for adult patients requiring confirmation of breathing tube (tracheal tube) position on chest X-rays. Intended for use by licensed physicians trained in breathing tube placement evaluation.
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).
Predicate Devices
ClearRead+Confirm Image Processing System (K123526)
Submission Summary (Full Text)
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA acronym followed by the full name of the agency on the right. The Department of Health & Human Services logo is a stylized depiction of an eagle. The FDA acronym and the agency's full name are in blue.
Qure.ai Technologies % Bunty Kundnani Head of Regulatory Affairs Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E) Mumbai, Maharashtra 400063 INDIA
Re: K212690
Trade/Device Name: qXR-BT Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: November 22, 2021 Received: November 24, 2021
Dear Bunty Kundnani:
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
{1}------------------------------------------------
requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); 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-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 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,
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
{2}------------------------------------------------
# Indications for Use
510(k) Number (if known) K212690
Device Name qXR-BT
### Indications for Use (Describe)
The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------------------------------------------------------|--|
| <label><input checked="" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D)</label> | |
| <label><input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C)</label> | |
## 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}------------------------------------------------
# 510(k) SUMMARY Qure.ai's qXR-BT
#### 1 SUBMITTER
Qure.ai Technologies Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E), Mumbai 400 063 Phone: +91-9768123013 Contact Person: Bunty Kundnani
Date Prepared: November 22, 2021
#### 2 DEVICE
| Name of Device: | qXR-BT |
|-----------------------|--------------------------------------------------|
| Common or Usual Name: | Automated Radiological Image Processing Software |
| Classification Name: | Medical image management and processing system |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2050 |
| Product Code: | QIH |
#### PREDICATE DEVICE 3
| Name of Device: | ClearRead+Confirm Image Processing System |
|-----------------|-------------------------------------------|
| Manufacturer: | Riverain Technologies LLC |
| 510(k) Number: | K123526 |
#### 4 INTENDED USE / INDICATIONS FOR USE:
The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest Xrays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.
{4}------------------------------------------------
#### ട DEVICE DESCRIPTION
qXR-BT is a standalone image analysis software used during the review of digital chest radiographic images, intended to facilitate determining the position of the breathing tube relative to the carina. Standard of care medical imaging workflows are well established, and include pre-existing software components such as a PACS, DICOM viewer and imaging worklist; qXR-BT is designed to integrate with these components.
X-rays are sent to qXR-BT by means of transmission functions within the user's PACS system. Upon completion of processing, the qXR-BT device returns results to the user's PACS or other userspecified radiology software system or database.
The input to the qXR-BT device is a chest X-ray (AP and PA, referred to as frontal) in digital imaging and communications in medicine (DICOM) format.
The qXR-BT device produces PDF and DICOM format outputs that enable users to view the position of a breathing tube and an anatomical landmark (carina).
The PDF format output contains preview images that show segmented structures outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software device.
The DICOM format output consists of a single complete additional DICOM series for each input scan. This DICOM output contains labeled overlays indicating the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer.
The qXR-BT analysis module consists of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.
Image /page/4/Figure/8 description: This image shows a flowchart of the qXR-BT Analysis process. The process starts with chest x-ray scans in DICOM format, followed by pre-processing, core processing, and post-processing. The output of the post-processing step is then used to generate a secondary image with markers and labels in DICOM format, as well as a report with a preview image and table in PDF format. The title of the flowchart is "qXR-BT Analysis".
## Figure 1: Schematic showing the design of qXR-BT device
#### б COMPARISON WITH PREDICATE DEVICE
Like the potential predicate devices, qXR-BT facilitates the confirmation of placement of a medically inserted tube on chest X-rays. In terms of establishing substantial equivalence, the subject and predicate device have the same intended use, as an image processing tool that generates a secondary digital radiographic image to facilitate the confirmation of the position of
{5}------------------------------------------------
medically inserted tube. The indications for use proposed for the subject device are similar to those of the predicate device, with the primary difference being that the predicate device is intended to facilitate the confirmation of multiple types of lines, tubes, and wires, while the subject device is intended to facilitate the confirmation of only one type of tube, viz. breathing tubes.
| | Predicate Device<br>ClearRead+Confirm<br>(K123526) | Subject Device<br>qXR-BT |
|-------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | ClearRead+Confirm | qXR-BT |
| 510(k) Number | K123526 | K212690 |
| Regulation | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Regulation Description | Medical image management<br>and processing system<br><br>Formerly: Picture archiving<br>and communications system | Medical image management and<br>processing system |
| Product Code | LLZ | QIH |
| Device type | Radiological Image<br>Processing System | Automated Radiological Image<br>Processing Software |
| Manufacturer | Riverain Technologies | Qure.ai Technologies |
| Intended use / Indications<br>for Use | ClearRead+Confirm is<br>intended to generate an<br>enhanced, secondary digital<br>radiographic image of the<br>chest to facilitate<br>confirmation of line/tubes.<br>The enhanced AP or PA<br>image of the chest provides<br>improved visibility of lines<br>and tubes. The ClearRead<br>+Confirm image provides<br>adjunctive information and<br>is not a substitute for the<br>original PA/AP image. This<br>device is intended to be used<br>by trained professionals,<br>such as physicians,<br>radiologists, and technicians,<br>on patients with lines and<br>tubes and is not intended to<br>be used on pediatric<br>patients. | The qXR-BT device is intended to<br>generate a secondary digital chest X-<br>ray image that facilitates<br>confirmation of the position of a<br>breathing tube and an anatomical<br>landmark on adult chest X-rays. This<br>device is intended for use by licensed<br>physicians who are trained in the<br>evaluation of breathing tube<br>placement on chest X-rays. The qXR-<br>BT image provides adjunctive<br>information and is not a substitute<br>for the original PA/AP image. |
| Modality | Digital chest radiograph | Digital chest radiograph |
| Input format | DICOM | DICOM |
| | Predicate Device<br>ClearRead+Confirm<br>(K123526) | Subject Device<br>qXR-BT |
| Output Format | Secondary digital chest X-ray<br>image | Secondary digital chest X-ray image<br>and other Multiple electronic reports<br>with localization information of<br>segmented structures |
| Intended User | Physicians, radiologists and<br>technicians | Physicians and radiologists |
| Hardware | No additional hardware;<br>standalone software device<br>that integrates with PACS<br>through DICOM protocols | qXR-BT is standalone software<br>deployed on-premise or on the<br>cloud, that integrates with PACS or<br>other hardware or software imaging<br>platforms, including digital<br>radiographic processing systems<br>through DICOM protocols |
| Comparison of Differences between qXR-BT and the predicate device | | |
| Types of lines and tubes | Various medically inserted<br>tubes, lines, tubes and wires | Medically inserted breathing tubes<br>(tracheal tubes) only |
| Internal algorithms used<br>for image processing and<br>the mechanism by which<br>the output is displayed to<br>the clinician | ClearRead+Confirm uses a<br>bone suppression<br>mechanism with overall<br>image enhancements to<br>improve the visibility of lines<br>and tubes and provides no<br>boxes or markings to the<br>user. | qXR-BT uses pre-trained<br>convolutional neural networks to<br>process the images, and the device<br>highlights the tip of the tube and the<br>carina using markings on the<br>secondary image. |
Table 1: Comparison between qXR-BT and the Predicate Device
{6}------------------------------------------------
#### 7 TESTING
### Software
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device has a Moderate level of concern.
## Performance Testing
Qure.ai performed standalone performance testing to test the accuracy of qXR-BT's analysis. The number of Chest X-ray images used for this performance testing was 162. Table 2 shows localization accuracy for the 2 target structures – carina and tip of breathing tube and the accuracy of distance measurement between these 2 structures. The ground truth was based on manual annotation of three radiologists from United States. For the target structures, the
{7}------------------------------------------------
standalone performance exceeded the preset acceptance criteria. The table below shows a summary of the results of performance testing.
| Target<br>Structure<br>(Number of<br>scans) | Metric | Mean<br>(Standard<br>Deviation) | Median (10th -<br>90th percentile) | Mean (95% CI) | Success Criteria |
|---------------------------------------------------------------------|----------------------|---------------------------------|------------------------------------|-----------------------|--------------------------------|
| Carina (162) | Absolute<br>Distance | 2.15 (1.25) | 1.86 (0.63 -<br>4.05) | 2.15 (1.96 -<br>2.35) | Upper bound of<br>95% CI ≤ 3mm |
| Tip of<br>Breathing<br>Tube (162) | Absolute<br>Distance | 1.97 (1.09) | 1.8 (0.7 - 3.64) | 1.97 (1.80 –<br>2.13) | Upper bound of<br>95% CI ≤ 3mm |
| Distance<br>between tip<br>of breathing<br>tube and<br>carina (162) | Absolute<br>Error | 1.98 (1.41) | 1.64 (0.27 -<br>4.12) | 1.98 (1.76 –<br>2.20) | Upper bound of<br>95% CI ≤ 6mm |
## Table 2: Overall Results of Accuracy Testing in mm
Cl = confidence interval
qXR-BT also passed software validation and system verification checks.
#### 8 CONCLUSION
The comparison in Table 2 and the software and performance testing presented above demonstrate that the qXR-BT device is substantially equivalent to the 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.