Training data: thousands of continuous cardiac recordings from compatible cardiac monitors. ACE algorithm tuned using PPG intervals recorded from devices similar to the Zio Watch.
>1 (Certified Cardiographic Technicians)
—
—
ECG-based cardiac rhythm and beat detection
Machine learning based ECG Deep Learned (ECGDL) algorithm
—
—
Training data: thousands of continuous cardiac recordings from compatible cardiac monitors. ECGDL algorithm trained using ECG data collected from Zio devices.
>1 (Certified Cardiographic Technicians)
—
—
Indications for Use
The ZEUS System (Zio Watch), as part of the Zio Watch Service, is intended to process and analyze electrocardiogram (ECG) and photoplethysmogram (PPG) based data to detect and report on the presence of Atrial Fibrillation (AF) over the monitoring period. The report provides ECG information for the intended user to diagnose AF and contextual information for AF, both to be interpreted based on clinical judgment and experience. It is indicated for use on adult patients 22 years or older who are susceptible to developing or who have been diagnosed with AF. It is not intended for use on critical care patients.
Device Story
ZEUS System (Zio Watch) is a prescription-based SaMD that processes ECG and PPG data from the Zio Watch to aid clinicians in AF diagnosis. The system uses two AI-based algorithms: ECGDL for ECG analysis (beats, rhythms, heart rate) and AF Context Engine (ACE) for PPG-based AF detection. Data is processed into Transmission Reports, which are quality-reviewed by Certified Cardiographic Technicians (CCTs) before being provided to clinicians via a secure website. The system provides AF burden information in 30-minute intervals. It is used in ambulatory settings for non-critical care patients. Clinicians use the reports to inform diagnosis, characterization, and management of AF. The device benefits patients by providing actionable cardiac rhythm information derived from continuous monitoring.
Clinical Evidence
No clinical testing was performed in support of this premarket notification. Evidence is based on nonclinical design verification and validation testing, including conformance to AAMI ANSI ISO 14971, IEC 62304, AAMI ANSI IEC 60601-2-47, and AAMI ANSI EC57.
Technological Characteristics
SaMD system; utilizes AI/ML-based algorithms (ECGDL and AF Context Engine). Inputs: continuous PPG and transmission ECG. Outputs: curated Transmission Reports with AF detection and heart rate measurements. Complies with IEC 62304 (software lifecycle), AAMI ANSI IEC 60601-2-47 (ambulatory ECG systems), and AAMI ANSI EC57 (cardiac rhythm algorithm performance).
Indications for Use
Indicated for adult patients 22 years or older susceptible to or diagnosed with Atrial Fibrillation (AF). Not intended for critical care patients.
Regulatory Classification
Identification
A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.
Predicate Devices
iRhythm Technologies, Inc. Zio® ECG Utilization Service (ZEUS) System (K202527)
Verily Life Sciences, LLC. Study Watch with Irregular Pulse Monitor (K192415)
Submission Summary (Full Text)
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue square. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
July 19, 2022
iRhythm Technologies, Inc. Vishal Kanani Senior Regulatory Affairs Program Lead 699 8th Street. Suite 600 San Francisco, California 94103
Re: K213409
Trade/Device Name: ZEUS System (Zio Watch) Regulation Number: 21 CFR 870.1425 Regulation Name: Programmable Diagnostic Computer Regulatory Class: Class II Product Code: DQK, DXH Dated: July 15, 2022 Received: July 18, 2022
Dear Vishal Kanani:
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
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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-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,
Jennifer Shih Kozen Assistant Director Division of Cardiac Electrophysiology, Diagnostics and Monitoring Devices Office of Cardiovascular Devices 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) K213409
Device Name ZEUS System (Zio Watch)
#### Indications for Use (Describe)
The ZEUS System (Zio Watch), as part of the Zio Watch Service, is intended to process and analyze electrocardiogram (ECG) and photoplethysmogram (PPG) based data to detect and report on the presence of Atrial Fibrillation (AF) over the monitoring period. The report provides ECG information for the intended user to diagnose AF and contextual information for AF, both to be interpreted based on clinical judgment and experience. It is indicated for use on adult patients 22 years or older who are susceptible to developing or who have been diagnosed with AF. It is not intended for use on critical care patients.
| Type of Use (Select one or both, as applicable) | |
|---------------------------------------------------------------|--------------------------------------------------------------|
| <div> Prescription Use (Part 21 CFR 801 Subpart D) </div> | <div> Over-The-Counter Use (21 CFR 801 Subpart C) </div> |
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#### 510(k) Notification K213409
#### l. General Information
### Applicant:
iRhythm Technologies, Inc. 699 8th Street, Suite 600 San Francisco, CA 94103 USA Phone: 415-632-5700 Fax: 415-632-5701
#### Contact Person:
Vishal Kanani Sr. Regulatory Affairs Program Lead Phone: 213-400-0963 Email: vishal.kanani@irhythmtech.com
#### Date Prepared: October 15, 2021
#### Device Information II.
Trade Name: ZEUS System (Zio Watch)
#### Generic/Common Name:
Programmable diagnostic computer
#### Classification Names:
- . Programmable diagnostic computer [21CFR§870.1425]
- Telephone electrographic transmitter and receiver [21CFR§870.2920]
#### Regulatory Class:
Class II (Special controls)
#### Product Codes:
- DQK, Computer, Diagnostic, Programmable
- DXH, Transmitters and Receivers, Electrocardiographic, Telephone
#### III. Predicate Devices
The following predicate devices have been selected:
- Primary Predicate: iRhythm Technologies, Inc. Zio® ECG Utilization Service (ZEUS) System [K202527]
- Secondary Predicate: Verily Life Sciences, LLC. Study Watch with Irregular Pulse Monitor [K192415]
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#### IV. Indications for Use
The Indications for Use statement for the subject ZEUS System (Zio Watch) is as follows:
The ZEUS System (Zio Watch), as part of the Zio Watch Service, is intended to process and analyze electrocardiogram (ECG) and photoplethysmogram (PPG) based data to detect and report on the presence of Atrial Fibrillation (AF) over the monitoring period. The report provides ECG information for the intended user to diagnose AF and contextual information for AF, both to be interpreted based on clinical judgment and experience. It is indicated for use on adult patients 22 years or older who are susceptible to developing or who have been diagnosed with AF. It is not intended for use on critical care patients.
#### V. Device Description
The Zio Watch Service, consisting of the Zio Watch (manufactured by Verily Life Sciences, LLC.; "Verily") and ZEUS System (Zio Watch) (developed by iRhythm Technologies, Inc.; "iRhythm"), is prescription based and intended to be used with non-critical care, ambulatory patients 22 years or older who have been diagnosed with or are susceptible to developing AF. The Zio Watch Service does not provide AF diagnosis. However, it is intended to provide the prescribing clinician with information such as ECG and AF Context, a visual display of AF presence in 30minute intervals over a defined monitoring period from processing of PPG-based data, which is comparable to AF burden provided by the primary predicate ZEUS System. This information is intended to aid the clinician in their diagnosis, characterization, and/or management of AF per their clinical judgment and experience.
The ZEUS System (Zio Watch), the subject device of this 510(k) submission, is a software as a medical device (SaMD) system consisting of a collection of modules designed to process and analyze data from the Zio Watch into a curated report of preliminary findings intended for use by clinicians to aid in AF diagnosis.
The subject ZEUS System utilizes an artificial intelligence (Al) based ECG Analysis Software (ECGDL) to generate the initial ECG-based cardiac information provided to the clinician in Transmission Reports. In addition, continuously recorded PPG-based data is processed by a separate artificial intelligence (AI) based analysis software, the AF Context Engine (ACE), that detects the presence of AF. Specifically, the subject ZEUS System (Zio Watch) utilizes machine learning techniques for both the ECGDL and AF Context Engine algorithms.
These results are also presented along with the ECG data in the Zio Watch Transmission Reports. The reported cardiac information includes AF detection (including PPG-based AF summary) and heart rate measurements. The ECG-based preliminary findings in the Zio Watch Transmission Reports are quality reviewed by Certified Cardiographic Technicians (CCTs) prior to publishing. After CCT review, the report containing the preliminary findings and associated ECG are provided to clinicians via a secure website.
- VI. Comparison of Technological Characteristics with Predicate Devices (Substantial Equivalence) The subject ZEUS System (Zio Watch) has the same intended use as the predicate devices. The differences in the technological characteristics between the subject and predicate devices do
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not raise any issues of safety or efficacy as the fundamental scientific technology and intended use is unchanged. Thus, the ZEUS System (Zio Watch) is considered substantially equivalent to the predicate device.
A comparison table outlining the differences and similarities between the subject device and the predicate devices is provided in Table 1.
| Feature | Subject Device:<br>ZEUS System (Zio Watch) | Primary Predicate Device: ZEUS System (K202527) | Secondary Predicate Device: Study Watch with Irregular Pulse Monitor (K192415) |
|------------------------------------------|-------------------------------------------------------------------|------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|
| <b>General Characteristics</b> | | | |
| Manufacturer | iRhythm Technologies, Inc. | iRhythm Technologies, Inc. | Verily Life Sciences LLC |
| Classification | Class II | Same | Same |
| Product Code | Classification Product Code: DQK<br>Subsequent Product Codes: DXH | Classification Product Code: DQK<br>Subsequent Product Codes: DSI, DXH | Classification Product Code: DXH<br>Subsequent Product Codes: DPS |
| Review Panel | Cardiovascular | Same | Same |
| Prescription/OTC | Prescription | Same | Same |
| <b>Technology</b> | | | |
| Data Input for AF<br>Detection Algorithm | ECG (Transmission) PPG-based data (Continuous) | ECG (Transmission) ECG (Continuous) | PPG (to notify user in the event of irregular pulse and recommended acquisition of ECG) |
| AF Detection<br>Algorithm | AF Context Engine (ACE) ECGDL | AutoTrigger Engine (ATE) ECGDL | Irregular Pulse Monitor |
| Beat Detection | Present | Same | None |
| Interoperability | Zio Watch | Zio XT Patch Zio AT Patch Zio Monitor | N/A |
#### Table 1. Substantial Equivalence Summary Table
#### VII. Performance Data
Safety and performance of the subject ZEUS System has been evaluated and verified in accordance with design specifications and to support a determination of substantial equivalence to the predicate devices.
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The design verification and validation testing performed on the subject device demonstrates that the subject ZEUS System is in conformance with FDA-recognized consensus standards and FDA guidance documents as highlighted in Table 2.
| FDA # | Body | Number/Version | Title |
|-------|---------------|---------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5-40 | AAMI ANSI ISO | 14971:2012(R)2010<br>(Corrected 4<br>October 2017) | Medical Devices – Application of Risk<br>Management to Medical Devices |
| 13-79 | IEC | 62304 Edition 1.1<br>2015-06<br>CONSOLIDATED<br>VERSION | Medical Device Software - Software Life<br>Cycle Processes |
| 3-127 | AAMI ANSI IEC | 60601-2-47:2012 | Medical Electrical Equipment -- Part 2-47:<br>Particular Requirements For The Basic<br>Safety And Essential Performance Of<br>Ambulatory Electrocardiographic Systems |
| 3-118 | AAMI ANSI | EC57:2012 | Testing and Reporting Performance<br>Results of Cardiac Rhythm and ST<br>Segment Measurement Algorithms |
| N/A | U.S. FDA | October 2, 2014 | Guidance for Industry and FDA Staff –<br>Content of Premarket Submissions for<br>Management of Cybersecurity in Medical<br>Devices |
| N/A | U.S. FDA | May 5, 2005 | Guidance for the Content of Premarket<br>Submissions for Software Contained in<br>Medical Devices |
| N/A | U.S. FDA | September 6, 2017 | Design Considerations and Premarket<br>Submission Recommendations for<br>Interoperable |
| N/A | U.S. FDA | July 2014 | The 510(k) Program: Evaluating<br>Substantial Equivalence in Premarket<br>Notifications [510(k)] |
Table 2. FDA-Recognized Consensus Standards & Guidance Document Summary
The nonclinical verification and performance test results established that the device meets its design requirements and intended use, that the design differences with the cleared device do not raise new questions of safety and efficacy. During development, potential hazards were evaluated and controlled by risk management activities, including risk analysis, risk mitigation, verification and benefit-risk analysis. The verification and validation testing demonstrate that the device meets all predetermined specifications.
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#### VIII. Algorithm Training and Validation
The subject ZEUS System (Zio Watch) utilizes machine learning techniques for both the ECGDL and AF Context Engine algorithms.
#### ECG Deep Learned (ECGDL) Algorithm
The ECG Deep Learned analysis algorithm (ECGDL) analyzes ECG recordings to provide beats, runs, rhythms, and heart rate detection.
#### AF Context Engine
AF Context Engine (ACE) is responsible for processing PPG-based data collected by the Zio Watch device to characterize the amount of AF over the patient's wear period. ACE retrieves PPGbased data in specific time segments (analysis intervals) from the Zio Watch and then provides AF presence/absence determination in intervals to be included in Zio Watch Transmission report.
### Algorithm Training
The source of training data for the ECGDL and ACE algorithms of the subject device are continuous cardiac recordings from compatible cardiac monitors. Training data is collected from thousands of recordings, which have already undergone Certified Cardiographic Technician (CCT) review.
The ACE algorithm was trained using ECG data collected from Zio devices. In addition, the algorithm was tuned using PPG intervals recorded from devices similar to the Zio Watch.
The ECGDL algorithm was trained using ECG data collected from Zio devices for rhythm and beat detection.
### Algorithm Validation
#### Verily Prospective Study (ACE):
The Verily Prospective Study data consists of multi-day PPG recordings obtained from the Zio Watch, along with ECG-based, CCT-reviewed reference rhythm labels obtained from reference Zio XT Patches worn simultaneously.
| Recording device | Zio Watch |
|------------------|-----------------|
| Channel(s) | Wrist-based PPG |
| Recording length | Up to 14 days |
| Environment | Ambulatory* |
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| Demographics | Patients at least 22 years or older who are at<br>risk of having an AF event, as determined by<br>having a diagnosis of paroxysmal AF. |
|--------------|----------------------------------------------------------------------------------------------------------------------------------------|
| | Age: Median=67 [25%,75%] = [59,73] |
| | Gender: 45.5% Female |
| | Regional Demographics (USA):<br>Midwest: 8.0%,<br>Mountain: 32.1%,<br>West: 32.1%,<br>Northeast: 14.3%,<br>South: 13.4% |
*In-clinic during enrollment
### ZWAF Database (ECGDL):
The Zio Watch AF (ZWAF) database consists of ECG data obtained from Zio Watch as used in the Study Watch AF Detection At Home study ("Verily Prospective Study") sponsored by Verily Life Sciences LLC.
| Recording device | Zio Watch |
|------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| Channel(s) | Single-lead ECG: equivalent to lead I, on-wrist<br>dry electrodes |
| Recording length | 45 seconds |
| Environment | Ambulatory* |
| Demographics | Patients at least 22 years or older who are at<br>risk of having an AF event, as determined by<br>having a diagnosis of paroxysmal AF. |
*In-clinic during enrollment
#### IX. Clinical Testing in Support of Substantial Equivalence Determination
No clinical testing was performed in support of this premarket notification.
#### X. Conclusion
The results confirm by evaluation and provision of objective evidence that the design outputs met the design input requirements. The results of the nonclinical testing performed demonstrate that the subject ZEUS System (Zio Watch) meets the requirements of established conformance standards and performance specifications necessary for its intended use and does
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not raise new questions of safety or effectiveness as compared to the predicate device. The subject ZEUS System (Zio Watch) is substantially equivalent to its predicate devices.
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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.
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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.