AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
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RWE Use Summary
Key Tags
K182177 · Oct 26, 2018
Accipiolx
Maxq-Al , Ltd.
Retrospective clinical CT cases from over 30 US sites
A retrospective study was conducted to evaluate the sensitivity and specificity of the Accipiolx software by analyzing 360 clinical non-contrast head CT cases.
Training CT cases collected from multiple institutions and CT manufacturers including pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds
—
Retrospective study of 360 cases collected from over 30 US sites
>1 (expert neuroradiologist readers)
Indications for Use
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
Device Story
Accipiolx is a software workflow tool for acute care settings; operates in parallel to standard radiology workflows. Input: non-contrast head CT (NCCT) studies from PACS. Processing: Accipiolx Agent retrieves studies; MaxQ-Al Engine uses deep learning/computer vision to identify/classify hyperdensities consistent with acute intracranial hemorrhage (aICH). Output: case-level indicator sent to PACS/workstation for worklist prioritization. Usage: assists radiologists/clinicians in identifying potential hemorrhagic cases for urgent review. Benefits: facilitates rapid assessment of time-sensitive cerebrovascular events. Does not direct attention to specific image regions; not for stand-alone diagnosis; does not rule out hemorrhage.
Clinical Evidence
Retrospective study of 360 cases from >30 US sites. Ground truth established by concurrence of two expert neuroradiologists. Results: sensitivity 92% (95% CI: 87.29-95.68%) and specificity 86% (95% CI: 80.18-90.81%). Average processing time 4.1 minutes (95% CI: 3.8-4.3 minutes).
Technological Characteristics
DICOM-compliant software; consists of Accipiolx Agent (image management) and MaxQ-Al Engine (processing). Employs deep learning/computer vision algorithms. Performs skull stripping, registration, tissue segmentation, and hyperdensity threshold analysis. Operates within hospital radiology network infrastructure. Standalone software deployment.
Indications for Use
Indicated for adult patients undergoing non-contrast head CT scans in acute care settings to prioritize clinical assessment of cases with features suggestive of acute intracranial hemorrhage.
Regulatory Classification
Identification
Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
*e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
*e.g.,* poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
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MaxQ-Al Ltd. % Joshua Schulman, Ph.D. Vice President, Clinical, Regulatory and Quality Affairs 76 Yigal Alon Street, 5th Floor Tel Aviv, 6706701 ISRAEL
October 26, 2018
Re: K182177
Trade/Device Name: Accipiolx Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: II Product Code: OAS Dated: October 4, 2018 Received: October 4, 2018
Dear Dr. Schulman:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.html; 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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.
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/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Image /page/1/Picture/6 description: The image shows the name Michael D. O'hara -S in large font on the left side of the image. On the right side of the image is a digital signature with the name Michael D. O'hara -S. The digital signature also includes the date 2018.10.26 08:54:29 -04'00'.
Robert Ochs, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
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510(k) Number (if known) K182177
Device Name
Accipiolx
Indications for Use (Describe)
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------------------------------------------------------------------------|
| <div> <span> <span style="padding-left: 10px;">Prescription Use (Part 21 CFR 801 Subpart D)</span> </span> </div> |
| <div> <span> <span style="padding-left: 10px;">Over-The-Counter Use (21 CFR 801 Subpart C)</span> </span> </div> |
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## 510(k) SUMMARY
#### MaxQ-Al's Accipiolx
#### Submitter
MaxQ-Al Ltd. 76 Yigal Alon Street, 5th Floor Tel Aviv, Israel 6706701 Tel: +1 -617-765-0333 Contact Person: Joshua Schulman, Ph.D. Date Prepared: October 4, 2018
Name of Device: Accipiolx
#### Common or Usual Name/ Classification Name:
Radiological Computer Aided Triage and Notification Software
Regulatory Class: Class II
Regulatory Classification and Product Code: 21 C.F.R. § 892.2080; QAS
Predicate Device: ContaCT (Viz.AI), DEN170073
Reference Device: AiDoc Briefcase (K180647)
#### Device Description
Accipiolx is a software device designed to be installed within healthcare facility radiology networks to identify and prioritize non-contrast head CT (NCCT) scans based on algorithmically-identified findings of acute intracranial hemorrhage (alCH). The device, developed using computer vision and deep learning technologies, facilitates prioritization of CT scans containing findings of alCH. There are two main components of the software device: (1) the Accipiolx Agent and (2) the MaxQ-Al Engine. The Agent serves as an active conduit which receives head CT studies from a PACS and transfers them to the Engine. After successful processing of a case via the MaxQ-Al Engine, the Accipiolx Agent receives the Engine results and returns them to the PACS or workstation for use in worklist prioritization.
Accipiolx works in parallel to and in conjunction with the standard care of workflow. After a CT scan has been performed, a copy of the study is automatically retrieved and processed by Accipiolx. The device performs identification and classification of objects consistent with alCH, and provides a case-level indicator which facilitates prioritization of cases with potential acute hemorrhagic findings for urgent review.
#### Intended Use / Indications for Use
Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx analyzes cases using an artificial intelligence algorithm to identify
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suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.
Accipiolx and the predicate device (ContaCT) have the same intended use and substantially similar indications for use. Both devices are assistive software tools for diagnosis, designed to analyze brain CT images for findings suggestive of a pre-specified clinical condition -- specifically, cerebrovascular (CV) events. The subject device (Accipiolx) supports the rapid assessment of acute intracranial hemorrhage (alCH), while the predicate device assesses findings potentially indicative of large vessel occlusion (LVQ). Since alCH and LVQ are aspects of the same differential diagnosis and treatment considerations, the clinical purpose of the two devices is substantially equivalent; thus, both devices have the same intended use. In addition, the AiDoc Briefcase was recently cleared for use in assessment of ICH and thus is a relevant reference device to further demonstrate substantial equivalence of the subject device.
## Summary of Technological Characteristics
The technological characteristics and mode of operation of the Accipiolx device are substantially equivalent to the predicate device.
Accipiolx and its predicate are DICOM-compliant software devices incorporated into the radiology infrastructure of a clinical center. Both employ algorithms developed through artificial intelligence methodologies to analyze brain CT images received from a CT scanner. Both software devices include an image management component and an image processing and analysis component (in Accipiolx, these are the Agent and the Engine, respectively).
Accipiolx was developed using a training CT cases collected from multiple institutions and CT manufacturers. This training process included pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds above which cases are considered positive.
In both the predicate and subject devices, the algorithm identifies applicable CT series' based on image parameters. In both devices, skull stripping and registration steps are performed, the relevant tissues are identified and segmented, and a feature identification process, which includes measures of hyperdensity compared to a pre-defined threshold, is performed. Findings above this threshold cause the software devices to generate a case-level identifier which is used for prioritization of cases based on a suspected cerebrovascular finding. With Accipiolx, the system output itself is the basis for prioritization within radiological workflow and viewing systems. In both cases, the procedure is performed in parallel to and in conjunction with the standard processing of image storage and availability for clinician assessment.
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A summary table comparing the key features of the subject and predicate devices is provided below.
| | Subject Device:<br>MaxQ Al Accipiolx | Predicate Device:<br>Viz.Al ContaCT |
|-------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------|
| Notification-only, parallel workflow tool | Yes | Yes |
| Intended user | Hospital networks and trained clinicians | Hospital networks and trained clinicians |
| Setting | Acute care | Acute care |
| Identify patients with a pre-specified clinical condition | Yes | Yes |
| Clinical condition | Cerebrovascular Event:<br>Intracranial hemorrhage | Cerebrovascular Event: Large vessel occlusion |
| Alert to finding | Yes; flagged for review | Yes; flagged for review |
| Independent of standard of care workflow | Yes; No cases are removed from worklist | Yes; No cases are removed from worklist |
| Modality | Non-Contrast CT | CT Angiogram |
| Artificial Intelligence algorithm | Yes | Yes |
| Limited to analysis of imaging data | Yes | Yes |
| Non-Diagnostic Preview | No | Yes |
| Aids prompt identification of cases with indicated findings | Yes | Yes |
| Output | Suspected hemorrhage / No suspected hemorrhage | Suspected large vessel occlusion |
| Where results are received | PACS / Workstation | PACS / Mobile application |
# Performance Testing
MaxQ-Al conducted a retrospective study to test the sensitivity and specificity of Accipiolx in processing non-contrast head CT cases with a high or low probability of intracranial hemorrhage (ICH). Device sensitivity and specificity was compared to ground truth established by concurrence of at least two expert neuroradiologist readers.
Analysis of 360 cases collected from over 30 US sites demonstrated system sensitivity and specificity of 92% (95% Cl: 87.29-95.68%) and 86% (95% Cl: 80.18-90.81%), respectively. These results exceeded the predefined performance goals for sensitivity and specificity.
During performance testing, average per-case processing time was 4.1 minutes (95% Cl: 3.8-4.3 minutes), which is comparable to the processing time reported by the reference BriefCase device.
Based on the software testing and clinical performance. Accipiolx has a safety and effectiveness profile that is similar to the predicate device for the proposed indications for use.
# Conclusion
The Accipiolx device is as safe and effective as the ContaCT device. Accipiolx has the same intended use and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences do not alter the intended prioritization and triage use of the device and do not affect its safety and effectiveness when used as labeled, and also do not raise any new or different questions of safety or effectiveness. Performance data demonstrate that the software functions as intended. Thus, the Accipiolx is substantially equivalent.
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.