The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders. The Gastric Alimetry System is indicated for patients 12 years and older.
Device Story
Gastric Alimetry is an electrogastrography (EGG) system for non-invasive measurement of gastric myoelectrical activity. Input: surface myoelectrical signals captured via a 64-channel disposable adhesive array. Operation: Alimetry Reader digitizes signals; Alimetry App (iPad mini) manages setup and patient-reported symptoms; Alimetry Cloud provides secure portal for clinician access. Output: Gastric Alimetry Report containing myoelectrical waveforms, spectral analysis, symptom scores, and automated phenotype classification (Auckland Classification v1.0). Used in healthcare facilities by clinicians. The system aids diagnosis by quantifying gastric rhythm, amplitude, and meal response (Meal Response Ratio). Benefits include objective assessment of gastric myoelectrical function to support clinical decision-making for gastric disorders.
Clinical Evidence
No new clinical trials were required for this submission. The device relies on previously established clinical data for the predicate system. Pediatric reference intervals (ages 12-17) were established via a new study, and the Auckland Classification v1.0 framework is derived from expert consensus (Varghese et al., 2026). Software verification and validation testing were performed to confirm the safety and performance of the updated reporting and app features.
Technological Characteristics
System includes Alimetry Reader, 64-channel disposable electrode array (ANSI/AAMI EC12:2000 compliant), and charging dock. Connectivity via iPad mini (MDM-managed) and secure cloud. Non-invasive, battery-powered. Software uses fixed, deterministic rules for signal processing and automated phenotyping. Complies with IEC 60601-1 (safety) and IEC 60601-1-2 (EMC).
Indications for Use
Indicated for patients 12 years and older as an aid in the diagnosis of various gastric disorders.
Regulatory Classification
Identification
An electrogastrography system (EGG) is a device used to measure gastric myoelectrical activity as an aid in the diagnosis of gastric motility disorders. The device system includes the external recorder, amplifier, skin electrodes, strip chart, cables, analytical software, and other accessories.
Special Controls
*Classification.* Class II (Special Controls). The special controls are as follows:(1) The sale, distribution and use of this device are restricted to prescription use in accordance with § 801.109 of this chapter.
(2) The labeling must include specific instructions:
(i) To describe proper patient set-up prior to the start of the test, including the proper placement of electrodes;
(ii) To describe how background data should be gathered and used to eliminate artifact in the data signal;
(iii) To describe the test protocol (including the measurement of baseline data) that may be followed to obtain the EGG signal; and
(iv) To explain how data results may be interpreted.
(3) The device design should ensure that the EGG signal is distinguishable from background noise that may interfere with the true gastric myoelectric signal.
(4) Data should be collected to demonstrate that the device has adequate precision and the EGG signal is reproducible and is interpretable.
In addition to the general controls of the act, the 3CPM EGG Machine is subject to the following special controls: (1) The sale, distribution and use of this device are restricted to prescription use in accordance with 21 CFR 801.109. (2) The labeling must include specific instructions (a) to describe proper patient set-up prior to the start of the test, including the proper placement of electrodes; (b) to describe how background data should be gathered and used to eliminate artifact in the data signal; (c) to describe the test protocol (including the measurement of baseline data) which may be followed to obtain the EGG signal; and (d) to explain how data results may be interpreted. (3) The device design should ensure that the EGG signal is distinguishable from background noise that may interfere with the true gastric myoelectric signal. (4) Data should be collected to demonstrate that the device has adequate precision and the EGG signal is reproducible and is interpretable.
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FDA U.S. FOOD & DRUG ADMINISTRATION
September 4, 2026
Alimetry, Ltd.
% Lina Kontos
Partner
Hogan Lovells Cadwalader US LLP
555 Thirteenth St. NW
Washington, District of Columbia 20004
Re: K262292
Trade/Device Name: Gastric Alimetry
Regulation Number: 21 CFR 876.1735
Regulation Name: Electrogastrography System
Regulatory Class: Class II
Product Code: MYE
Dated: July 6, 2026
Received: July 7, 2026
Dear Lina Kontos:
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 (the 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 available 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.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).
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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-
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assistance/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,
SIVAKAMI VENKATACHALAM -S
*for*
Shanil P. Haugen, Ph.D.
Assistant Director
DHT3A: Division of Renal, Gastrointestinal,
Obesity and Transplant Devices
OHT3: Office of Gastrorenal, ObGyn,
General Hospital and Urology Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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| Indications for Use | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K262292 | ? |
| Please provide the device trade name(s). | | ? |
| Gastric Alimetry | | |
| Please provide your Indications for Use below. | | ? |
| The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders. The Gastric Alimetry System is indicated for patients 12 years and older. | | |
| Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
| Please select the age group(s) for which the device(s) is to be used. | ☐ Neonates/Newborns (Birth to < 29 days old) ☐ Infants (29 days old to < 2 years old) ☐ Children (2 years old to < 12 years old) ☑ Adolescents (12 years old to < 22 years old) ☑ Adults (22 years old and greater) | ? |
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K262292
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### 510(k) SUMMARY
### Alimetry's Gastric Alimetry
### Submitter
Alimetry Ltd.
Address: 86 Symonds St.
Grafton
Auckland 1010
New Zealand
Phone: +64 27 622 2306
Contact Person: Yaara Yarmut, Chief Regulatory Officer
Date Prepared: September 1, 2026
Name of Device: Gastric Alimetry
Common or Usual Name: Gastric Alimetry System
Classification Name: Electrogastrography system, 21 CFR 876.1735
Regulatory Class: Class II
Product Code: MYE
### Predicate Devices
Trade Name: Gastric Alimetry (K252504) (primary predicate)
Manufacturer: Alimetry Ltd.
### Device Description
The Gastric Alimetry System is an electrogastrography (EGG) device, used for non-invasively measuring the myoelectrical activity of the stomach at the surface of the abdomen. The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders.
The device is used to acquire and digitize gastric myoelectrical data and movement artifacts using the Alimetry Reader connected to a single use Gastric Alimetry Array which includes electrodes on an adhesive patch used for recording the myoelectrical data from the skin surface. The Gastric Alimetry App runs on an iPad mini and is used to set up the device and capture patient-reported symptom data. The Gastric Alimetry Report is provided to the clinicians at the end of the test and includes myoelectrical signal data for manual analysis, together with computed data summaries and plots. A Supplementary Report is also routinely available to clinicians that includes signal data from all 64 channels on the array. The Alimetry Cloud acts as
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a secure website portal for physicians to access Gastric Alimetry Reports. The Gastric Alimetry Dock (Accessory) is used to guide alignment of the Array and Reader during the setup procedure and charge the Reader.
The Gastric Alimetry System is non-invasive and used in healthcare facilities.
This submission addresses the following minor modifications:
- Updates to the Gastric Alimetry Report, including:
- Addition of automated phenotyping as defined by the Auckland Classification v1.0, a published framework derived by expert consensus (Varghese et al., 2026) which formalizes the phenotype information provided with the predicate.
- Addition of the Meal Response Ratio for quantifying the timing of the gastric response to a meal stimulus.
- Addition of reference intervals for patients aged between 12-17 years.
- Updates to the App to add an option for healthcare facilities to use iPads linked to their own mobile device management (MDM) software, instead of the Alimetry-provided MDM.
Additional minor updates were made to the Reader, Cloud, Report, App and labeling.
### Intended Use / Indications for Use
The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders.
The Gastric Alimetry System is indicated for use in patients 12 years and older.
There are no changes to the intended use in the modified Gastric Alimetry System, which has the same intended use as the predicate Gastric Alimetry System (K252504).
### Technological Characteristics / Substantial Equivalence
The modified Gastric Alimetry System retains the same core technological characteristics as the predicate Gastric Alimetry system. Both systems utilize identical hardware components: the Alimetry Reader, Gastric Alimetry Array, Gastric Alimetry Dock, and a commercial off-the-shelf iPad mini managed by a mobile device management system (MDM). iPad management using healthcare facilities' MDM is substantially equivalent to the use of Alimetry's MDM as it still requires the iPad to be managed by an MDM with the same controls.
Both systems also utilize an equivalent secure Alimetry Cloud for data storage and access. The fundamental function of recording, storing, viewing, and processing gastric myoelectrical activity remains unchanged, and the general content of the generated reports is similar, with minor modifications:
Modifications to the Gastric Alimetry Report, including: Addition of automated phenotyping as defined by the Auckland Classification v1.0, a framework derived by expert consensus which formalizes the phenotype information provided with the predicate. The phenotypes according to the Auckland Classification include six main phenotypes (Dysrhythmic, High Frequency, Low Meal Response, Sensorimotor, Continuous, and Late Onset Symptoms) and two emerging phenotypes (Low Frequency and High Amplitude); Addition of the 'Meal Response Ratio' (MRR) to serve as the primary indicator of postprandial meal response; Addition of reference intervals for patients aged between 12-17 years. Adult reference intervals were previously established and following a
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new study conducted in participants ages 12–17 years, reference intervals have now been established for this population as well.
Note that “automated” throughout this Summary refers to the application of fixed, deterministic rules, and does not denote the use of artificial intelligence or machine learning as part of the classification functions of the software.
A table comparing the key features of the subject and predicate devices is provided below.
| | Gastric Alimetry System | Gastric Alimetry System (predicate) | Discussion |
| --- | --- | --- | --- |
| Intended Use | To record, store, view and process gastric myoelectrical activity as an aid to the diagnosis of various gastric disorders. | To record, store, view and process gastric myoelectrical activity as an aid to the diagnosis of various gastric disorders. | Same as the predicate. |
| Indications for Use | Patients 12 years and older | Patients 12 years and older | Same as the predicate. |
| Technological Characteristics | | | |
| Setup and Charging Dock | Gastric Alimetry Dock for charging and user convenience | Gastric Alimetry Dock for charging and user convenience | Same as the predicate. |
| Mobile Device Management | iPad managed either by Alimetry's MDM, or by the clinic's MDM | iPad managed via Alimetry's MDM | Similar. iPad still has to be managed by an MDM with the same controls. |
| Electrodes | Disposable; same components as ECG electrodes. Peel-and-stick patch. | Disposable; same components as ECG electrodes. Peel-and-stick patch. | Same as the predicate. |
| Weight of device on patient | 225 grams - Reader and Array | 225 grams - Reader and Array | Same as the predicate. |
| Sampling Frequency | raw sampling: 250 Hz logging: 4 Hz | raw sampling: 250 Hz logging: 4 Hz | Same as the predicate. |
| Low Frequency Range | DC | DC | Same as the predicate. |
| High frequency range | 2 Hz | 2 Hz | Same as the predicate. |
| Number of channels | 64 (+2 reference) | 64 (+2 reference) | Same as the predicate. |
| Electrode to recorder interface | Reader located on the Array and directly connected to it. | Reader located on the Array and directly connected to it. | Same as the predicate. |
| Screen | Dedicated tablet for system operation | Dedicated tablet for system operation | Same as the predicate. |
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| Motion Sensor | Accelerometer | Accelerometer | Same as the predicate. |
| --- | --- | --- | --- |
| Power Source | Battery powered | Battery powered | Same as the predicate. |
| Reporting Features | | | |
| Visualization of myoelectrical waveforms | Yes, all channels. | Yes, all channels. | Same as the predicate. |
| Spectral Analysis | Yes | Yes | Same as predicate. |
| Frequency and Amplitude of Myoelectrical Activity | Yes | Yes. | Same as predicate. |
| Other Myoelectrical Parameters | Measure of rhythmic stability Measure of gastric response to meal stimulus presented as 'Meal Response Ratio'. Other measures of myoelectrical activity can be assessed visually using the spectrogram, amplitude plot, and/or signal traces. | Measure of rhythmic stability. Measure of gastric response to meal stimulus presented as 'Fed:Fasted Amplitude Ratio'. Other measures of myoelectrical activity can be assessed visually using the spectrogram, amplitude plot, and/or signal traces. | Similar. Presentation is comparable. The change from ff-AR to Meal Response Ratio as the primary measure of gastric response to meal stimulus reflects the International BSGM Working Group's recommendation (Varghese et al., 2026). This change does not raise different questions of safety or efficacy. The subject device includes reference intervals for patients aged 12 years and older, whereas the predicate limited the reference intervals to patients aged 18 and older. |
| Symptom Outputs | Severity scores for each symptomTotal Symptom Burden ScoreIdentification of patterns of symptoms (Meal Induced, Meal | Severity scores for each symptomTotal Symptom Burden ScoreIdentification of patterns of symptoms (Meal Induced, Meal | Same as predicate. |
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| | Alleviated, Late Onset, Continuous, or none) • Summary of Gut-Brain Wellbeing symptom scores ('depressive symptoms', 'stress symptoms', and 'anxiety symptoms') • Gut-Brain Wellbeing Total Score | Alleviated, Late Onset, Continuous, or none) • Summary of Gut-Brain Wellbeing symptom scores ('depressive symptoms', 'stress symptoms', and 'anxiety symptoms') • Gut-Brain Wellbeing Total Score | |
| --- | --- | --- | --- |
| **Clinical Summary** | Report contains all key information on a summary page along with a prepopulated field with an assessment of the relevant patient classifications based on the Auckland Classification phenotypes. | Report contains all key information on a summary page. Spectral results are summarized with a summary sentence at the top of the page. A box at the bottom page is provided for the clinician to document their summary. Phenotype classification provided in supplemental labelling materials. | Similar. The added automated assessment based on test outputs existing in the predicate report, and all of these outputs are still displayed prominently in the subject report so as to enable independent interpretation as would be performed in using the predicate system. |
| **Technical / recording quality outputs** | Yes. Impedance, artifacts, test protocol (test duration, meal type, and meal consumption), BMI. Movement, Array position and spatial distribution of amplitude. Test quality cautions are also included if quality parameters are outside the recommended range. | Yes. Impedance, artifacts, test protocol (test duration, meal type, and meal consumption), BMI. Movement, Array position and spatial distribution of amplitude Test quality cautions are also included if quality parameters are outside the recommended range. | Same as the predicate. |
| **Safety Features** | | | |
| **Biocompatibility** | Array and Reader in contact with the patient. | Array and Reader in contact with the patient. | Same as the predicate. No changes to any patient-contact materials. |
| **Software** | Setup (including clinical data), device control, data acquisition software and | Setup (including clinical data), device control, data acquisition software and | Same as the predicate. |
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| | system checks via App running on a tablet. | system checks via App running on a tablet. | |
| --- | --- | --- | --- |
| System Checks | Impedance monitor and connectivity check displayed prior to recordings. | Impedance monitor and connectivity check displayed prior to recordings. | Same as the predicate. |
| Sterilization | Electrodes are disposable, non-sterile. Reader and Dock are reprocessed, not supplied sterile. | Electrodes are disposable, non-sterile. Reader and Dock are reprocessed, not supplied sterile. | Same as the predicate. |
| Standards with which the Device Complies | | | |
| Electrodes | ANSI/AAMI EC12:2000 – compliance with relevant requirements. | ANSI/AAMI EC12:2000 – compliance with relevant requirements. | Same as the predicate. |
| Medical Electrical Equipment | IEC 60601-1 | IEC 60601-1 | Same as the predicate. |
| Electromagnetic Compatibility | IEC 60601-1-2 | IEC 60601-1-2 | Same as the predicate. |
### Performance Data
The modifications to the device since the prior clearances, namely modifications to the App and the Report, including addition of the Auckland Classification V1.0, addition the Meal Response Ratio metric, addition of reference intervals for patients aged between 12-17 years and addition of an option for healthcare facilities to use iPads linked to their own mobile device management (MDM) software, did not significantly impact the safety or performance of the device and did not require submission of new bench testing data beyond software verification and validation testing.
### Conclusions
The modified Gastric Alimetry System is as safe and effective as the Gastric Alimetry System. The Gastric Alimetry System has the same intended uses and indications, and similar technological characteristics and principles of operation as its predicate device. The minor technological differences between the modified Gastric Alimetry System and its predicate device raise no new or different questions of safety or effectiveness.
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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.
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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 as a Medical Device), then narrow by AI/ML (devices with AI).
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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.
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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.
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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.
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Reading rule for every project: how many summaries do you read in full?
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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.
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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.
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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.
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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.
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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.
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A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
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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.