K201985 · AliveCor, Inc. · DQK · Nov 12, 2020 · Cardiovascular
Device Facts
Record ID
K201985
Device Name
KardiaAI
Applicant
AliveCor, Inc.
Product Code
DQK · Cardiovascular
Decision Date
Nov 12, 2020
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.1425
Device Class
Class 2
Attributes
Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Atrial Fibrillation Detection
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Bradycardia Detection
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Tachycardia Detection
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Sinus Rhythm with Wide QRS Detection
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Sinus Rhythm with Premature Ventricular Contractions Detection
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Sinus Rhythm with Supraventricular Ectopy Detection
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Indications for Use
KardiaAI is a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms from adult subjects (when prescribed or used under the care of a physician). The device supports analyzing data recorded in compatible formats from any ambulatory ECG devices such as event recorders, or other similar devices. The library is intended to be integrated into other device software. The library is not intended for use in life supporting, or sustaining systems, or ECG monitors, or cardiac alarm, or OTC use only devices. The KardiaAl library provides the following capabilities: - Filtering ECG noise, - Reporting heart rate measurement from ECGs, - Detecting noisy ECGs. - Reporting ECG rhythm analysis for the presence of sinus rhythm, atrial fibrillation, bradycardia, for ECGs detected as sinus rhythm, detecting normal sinus rhythm with with wide QRS, sinus rhythm with premature ventricular contractions (PVC), and sinus rhythm with supraventricular ectopy; - Detecting QRS complexes in an ECG. - For ECGs detected as sinus rhythm, classifying individual beats as a PVC or non-PVC beat, and - Generating an average beat from an ECG The device is not intended for use in patients who have pacemakers, ICDs, or other implanted electronic devices.
Device Story
KardiaAI is a software-only library (SaMD) integrated into AliveCor-compatible ambulatory ECG devices (e.g., KardiaMobile, Triangle System). It processes single-channel ECG inputs to provide automated analysis via an API. Functions include noise filtering, QRS detection, heart rate measurement, and rhythm classification (sinus rhythm, AFib, bradycardia, tachycardia). For sinus rhythm, it further identifies wide QRS, PVCs, and supraventricular ectopy; generates average beat representations; and produces R-R interval tachograms. The device is operated under physician care. Outputs are stored, transferred, and displayed by the host device for review by healthcare professionals. The device provides supportive diagnostic information to assist clinical decision-making regarding arrhythmia management. It benefits patients by enabling automated, remote analysis of ambulatory ECG data.
Clinical Evidence
Bench testing only. Algorithm performance verified using proprietary AliveCor ECG databases and comparative testing against ANSI/AAMI EC57 databases. Human factors usability study conducted per IEC 62366-1:2015 and FDA guidance confirmed users understand device outputs and appropriate clinical follow-up actions.
Technological Characteristics
Software-only API library. Implements proprietary ECG processing and analysis algorithms. Operates on ambulatory ECG data. Integrated into host device software. No physical hardware components. Complies with IEC 62366-1:2015 for usability.
Indications for Use
Indicated for adult patients (18+) requiring assessment of ambulatory ECG rhythms. Contraindicated for patients with pacemakers, ICDs, or other implanted electronic devices. Not for life-supporting systems, cardiac alarms, or OTC use.
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.
Omron Model BP7900 Blood Pressure Monitor + EKG (K182579)
Submission Summary (Full Text)
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November 12, 2020
AliveCor, Inc. % Prabhu Raghavan Principal MDQR, LLC 444 Castro Street, Suite 600 Mountain View. California 94041
Re: K201985
Trade/Device Name: KardiaAI Regulation Number: 21 CFR 870.1425 Regulation Name: Programmable Diagnostic Computer Regulatory Class: Class II Product Code: DOK, DPS Dated: October 12, 2020 Received: October 13, 2020
Dear Prabhu Raghavan:
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
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statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jennifer Shih 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) K201985
Device Name
KardiaAI
#### Indications for Use (Describe)
KardiaAI is a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms from adult subjects (when prescribed or used under the care of a physician). The device supports analyzing data recorded in compatible formats from any ambulatory ECG devices such as event recorders, or other similar devices. The library is intended to be integrated into other device software. The library is not intended for use in life supporting, or sustaining systems, or ECG monitors, or cardiac alarm, or OTC use only devices.
The KardiaAl library provides the following capabilities:
- · Filtering ECG noise,
- · Reporting heart rate measurement from ECGs,
- · Detecting noisy ECGs.
· Reporting ECG rhythm analysis for the presence of sinus rhythm, atrial fibrillation, bradycardia, for ECGs detected as sinus rhythm, detecting normal sinus rhythm with with wide QRS, sinus rhythm with premature ventricular contractions (PVC), and sinus rhythm with supraventricular ectopy;
· Detecting QRS complexes in an ECG.
- · For ECGs detected as sinus rhythm, classifying individual beats as a PVC or non-PVC beat, and
- · Generating an average beat from an ECG
The device is not intended for use in patients who have pacemakers, ICDs, or other implanted electronic devices.
Type of Use (Select one or both, as applicable)
> Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/3/Picture/0 description: The image shows the logo for AliveCor. The logo is in a teal color and features the company name in a simple, sans-serif font. A registered trademark symbol is located to the upper right of the name.
#### 510(k) Summary for K201985
Prepared in accordance with the requirements of 21 CFR 807.92
#### Submitter Information [807.92(a)(1)]
| Submitter/Applicant | AliveCor, Inc.<br>444 Castro Street, Suite 600<br>Mountain View, CA 94041<br>Phone: 650-396-8553<br>Fax: 650-282-7932 |
|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Contact Person | Prabhu Raghavan<br>Regulatory Consultant for AliveCor<br>MDQR, LLC<br>444 Castro Street, Suite 600<br>Mountain View, CA 94041<br>Phone: 408-316-5707<br>Fax: 650-282-7932<br>Email: prabhu@mdqr.solutions |
| Submitter and<br>Secondary Contact Person | Saket Bhatt<br>VP of Regulatory and Quality<br>444 Castro Street, Suite 600<br>Mountain View, CA 94041<br>Phone: 408-701-7319<br>Fax: 650-282-7932<br>Email: ra@alivecor.com |
Date Prepared
July 15, 2020
#### Device Information [807.92(a)(2)]
| Trade Name | KardiaAI |
|-------------------------|----------------------------------|
| Common Name | Programmable diagnostic computer |
| Classification | 21 CFR§870.1425 |
| Device Class | Class II |
| Product Code | DQK |
| Subsequent Product Code | DPS |
### Predicate Information [807.92(a)(3)]
Predicate(s)
AliveCor, Inc., KardiaAI, K181823
#### Device Description [807.92(a)(4)]
KardiaAI is a software library that implements various ECG processing and analysis algorithms. This Software as a Medical Device (SaMD) computes various physiologic parameters from an ECG and provides these capabilities in the form of an Application Program Interface (API)
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510(k) Summary for KardiaAI AliveCor, Inc.
library. AliveCor-designed ECG devices ("target device") incorporate the API library into their device software to enable algorithmic analysis of ECGs to provide analytical capabilities. KardiaAI provides ECG processing functions, including ECG noise filtering and detection of noisy ECGs. It performs rhythm analysis on ECGs, specifically detecting atrial fibrillation, bradycardia, tachycardia and sinus rhythm, which can be further classified as normal sinus rhythm, sinus rhythm with wide QRS, sinus rhythm with premature ventricular contractions (PVCs), and sinus rhythm with supraventricular ectopy. It further provides beat-level annotations, including beat-level ORS locations, and, for sinus rhythm ECGs, PVC/not-PVC annotations. It also provides an average beat ECG representation, and the R-R interval tachogram. Recording and viewing of ECGs and the results of the KardiaAI analyses are to be provided by other AliveCor FDA-cleared devices (i.e., the target devices) into which the API library is incorporated, such as AliveCor's Triangle System (K183319) and KardiaMobile System (K182396).
#### Indications for use [807.92(a)(5)]
KardiaAI is a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms from adult subjects (when prescribed or used under the care of a physician). The device supports analyzing data recorded in compatible formats from any ambulatory ECG devices such as event recorders, or other similar devices. The library is intended to be integrated into other device software. The library is not intended for use in life supporting, or sustaining systems, or ECG monitors, or cardiac alarm, or OTC use only devices.
The KardiaAI library provides the following capabilities:
- Filtering ECG noise.
- Reporting heart rate measurement from ECGs, ●
- o Detecting noisy ECGs,
- Reporting ECG rhythm analysis for the presence of sinus rhythm, atrial fibrillation, ● bradycardia, and tachycardia; for ECGs detected as sinus rhythm, detecting normal sinus rhythm, sinus rhythm with wide QRS, sinus rhythm with premature ventricular contractions (PVC), and sinus rhythm with supraventricular ectopy,
- Detecting QRS complexes in an ECG,
- For ECGs detected as sinus rhythm, classifying individual beats as a PVC or non-PVC ● beat, and
- o Generating an average beat from an ECG
The device is not intended for use in patients who have pacemakers, ICDs, or other implanted electronic devices.
#### Substantial Equivalence
The subject device has the same intended use as the predicate devices intended are to process (e.g., filter and detect noise) and analyze a single channel ECG signal to detect the presence of arrhythmias. These ECG analysis outputs from the subject and predicate device all represent potential findings to be reviewed and interpreted by a qualified healthcare professional, and do not represent complete diagnoses. Both devices are software-only API libraries and are intended to be incorporated into other Alive Cor-designed medical devices ("target device"). Both the subject and predicate device process and analyze recorded ECGs, the output of which are stored, transferred, and displayed by the target device.
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The subject device includes all the software outputs of the predicate device with updates in algorithm design and provides several new analysis outputs which are within the general intended use shared with the predicate device. Like the predicate device, the subject device's ECG analysis functionalities with respect to arrhythmia detection are prescription features.
As such, the subject device has the same intended use and technological characteristics as the predicate device. Differences between the subject device and the predicate device do not raise different questions of safety or effectiveness from the predicate device, and performance testing has demonstrated that the subject device meets its performance specifications and is as safe and as effective as the predicate device for their intended use. Therefore, KardiaAI is substantially equivalent to the predicate device.
| Feature | AliveCor KardiaAI<br>(Subject Device) | AliveCor KardiaAI (K181823)<br>(Predicate Device) |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Product Code | DQK, Computer, Diagnostic,<br>Programmable | DQK, Computer, Diagnostic,<br>Programmable |
| | DPS, Electrocardiograph | DPS, Electrocardiograph |
| Regulation | 21 CFR§870.1425, Programmable<br>diagnostic computer | 21 CFR§870.1425, Programmable<br>diagnostic computer |
| | Class II | Class II |
| Feature | AliveCor KardiaAI<br>(Subject Device) | AliveCor KardiaAI (K181823)<br>(Predicate Device) |
| Indications for<br>use | KardiaAI is a software analysis library<br>intended to assess ambulatory<br>electrocardiogram (ECG) rhythms from<br>adult subjects (when prescribed or used<br>under the care of a physician). The<br>device supports analyzing data recorded<br>in compatible formats from any<br>ambulatory ECG devices such as event<br>recorders, or other similar devices. The<br>library is intended to be integrated into<br>other device software. The library is not<br>intended for use in life supporting, or<br>sustaining systems, or ECG monitors, or<br>cardiac alarm, or OTC use only devices.<br>The KardiaAI library provides the<br>following capabilities:<br>• Filtering ECG noise,<br>• Reporting heart rate measurement<br>from ECGs,<br>• Detecting noisy ECGs,<br>• Reporting ECG rhythm analysis for<br>the presence of sinus rhythm, atrial<br>fibrillation, bradycardia, and<br>tachycardia; for ECGs detected as<br>sinus rhythm, detecting normal sinus<br>rhythm, sinus rhythm with wide<br>QRS, sinus rhythm with premature<br>ventricular contractions (PVC), and<br>sinus rhythm with supraventricular<br>ectopy,<br>• Detecting QRS complexes in an<br>ECG,<br>• For ECGs detected as sinus rhythm,<br>classifying individual beats as a PVC<br>or non-PVC beat, and<br>• Generating an average beat from an<br>ECG<br>The device is not intended for use in<br>patients who have pacemakers, ICDs, or<br>other implanted electronic devices | KardiaAI is a software analysis library<br>intended to assess ambulatory<br>electrocardiogram (ECG) rhythms from<br>adult subjects.<br>The device supports analyzing data<br>recorded in compatible formats from<br>any ambulatory ECG devices such as<br>event recorders, or other similar devices.<br>The library is intended to be integrated<br>into other device software.<br>The library is not intended for use in life<br>supporting, or sustaining systems, or<br>ECG monitors, or cardiac alarm, or<br>OTC use only devices.<br>KardiaAI provides the following<br>capabilities:<br>• ECG noise filtering,<br>• heart rate measurement from ECGs,<br>• detection of noisy ECGs, and<br>• ECG rhythm analysis for detecting<br>the presence of normal sinus rhythm,<br>atrial fibrillation, bradycardia, and<br>tachycardia (when prescribed or used<br>under the care of a physician). |
| Target<br>population | Adults (over 18) | Adults (over 18) |
| Components | Software only | Software only |
| Feature | AliveCor KardiaAI<br>(Subject Device) | AliveCor KardiaAI (K181823)<br>(Predicate Device) |
| Software<br>Functionalities | An interface that provides tools to<br>process and analyze ECGs through<br>various algorithms The automated proprietary ECG<br>algorithms provide supportive<br>information for ECG diagnosis. The<br>library can be accessed by directly<br>connecting to the KardiaAI's<br>Application Programming Interface | An interface that provides tools to<br>process and analyze ECGs through<br>various algorithms The automated proprietary ECG<br>algorithms provide supportive<br>information for ECG diagnosis. The<br>library can be accessed by directly<br>connecting to the KardiaAI's<br>Application Programming Interface |
| Compatible<br>ECG Devices | Triangle System (K183319) KardiaMobile System (K182396) Omron Model BP7900 Blood<br>Pressure Monitor + EKG (K182579) | Triangle System (K183319) KardiaMobile System (K182396) KardiaBand System (K171816) Omron Model BP7900 Blood<br>Pressure Monitor + EKG (K182579) |
#### Comparison of Technological Characteristics with the Predicate Device [807.92(a)(6)]
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### Comparison of Technological Characteristics with the Predicate Device [807.92(a)(6)]
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Comparison of Technological Characteristics with the Predicate Device [807.92(a)(6)]
#### Performance Data [807.92(b)]
All necessary testing was conducted on KardiaAI to support a determination of substantial equivalence to the predicate device.
#### Nonclinical Testing Summary [807.92(b)(1)]
Nonclinical testing, similar to that conducted to support the predicate device, was conducted to assess algorithm performance and to verify that KardiaAI performs as intended.
Software testing was conducted for KardiaAI. Specifically, algorithm performance testing was assessed using an AliveCor proprietary ECG database. Additional comparative testing was also performed on databases from the ANSVAAMI EC57. All analysis outputs were found to meet their performance specifications. Specifically, for the algorithm outputs which are present in both KardiaAI and the predicate device, comparative testing was conducted, and it was found that the subject device demonstrated equivalent performance to the predicate device. The results of the testing demonstrate that KardiaAI performs to its specifications and meets its intended use, which is substantially equivalent to that of the predicate device.
In addition, a human factors usability study was conducted in accordance with recommendations in IEC 62366-1:2015, "Medical devices - Part 1: Application of usability engineering to medical devices", and FDA Guidance, "Applying Human Factors and Usability Engineering to Medical Devices", issued February 3, 2016. The results of the study demonstrated that users can use the device and understands its outputs based on labeling, and further understand appropriate actions if symptoms are present, such as when to seek medical care.
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510(k) Summary for KardiaAI AliveCor, Inc.
### Conclusions [807.92(b)(3)]
KardiaAI has the same intended use as the predicate device, and any differences in technological characteristics do not raise different questions of safety or effectiveness. Differences between the subject device and the predicate device have been tested to ensure that the device meets its intended use. The results of nonclinical testing specifically demonstrate that KardiaAI meets its intended use which is equivalent to that of the predicate device. Therefore, KardiaAI is substantially equivalent to the predicate device.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.