Retrospective clinical data was used to validate the performance of updated software algorithms (lung lobe segmentation and PERCIST Liver Reference Region placement) by comparing the subject device's performance against predicate device performance and expert reader reference standards.
Agreement with semi-automatic expert reader evaluation; intersection with suspicious uptake masks
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Lung lobe segmentation
—
Dice coefficient > 0.8 or ASSD < 10 mm for new organs; Dice coefficient within +/- 0.03 of predicate for unchanged organs; Dice coefficient >= predicate for improved organs
Average Dice coefficient higher for each lobe than predicate
Training and tuning cohorts (size not specified)
—
Independent cohort of 20 patients (50% new, 50% from predicate testing cohort)
—
PERCIST Liver Reference Region placement
—
Dice coefficient > 0.8 or ASSD < 10 mm
Met acceptance criteria; better agreement with semi-automatic expert reader method than predicate; 4 cases of intersection with suspicious uptake vs 13 in predicate
Training and tuning cohorts (size not specified)
—
129 subjects for intersection analysis; 20 patients for Dice/ASSD evaluation
2 (expert readers)
Indications for Use
syngo.via molecular imaging (MI) workflows comprise medical diagnostic applications for viewing, manipulation, quantification, analysis and comparison of medical images from single or multiple imaging modalities with one or more time-points. These workflows support functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR). syngo.via MI workflows can perform harmonization of SUV (PET) across different PET systems or different PET reconstruction methods. syngo.via MI workflows are intended to be utilized by appropriately trained health care professionals to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The images and results produced by the syngo.via MI workflows can also be used by the physician to aid in radiotherapy treatment planning.
Device Story
Multi-modality post-processing software; server-client architecture; installed on standard IT hardware. Inputs: DICOM PET, SPECT, CT, MR data. Transforms: automated/manual data import; image processing; segmentation; quantification (SUV, PERCIST, lung lobes, liver reference). Outputs: processed images, quantitative metrics, reports. Used in clinical settings by radiologists and nuclear medicine technologists. Workflow: preprocessing, evaluation, reading, reporting. Benefits: aids disease management, radiotherapy planning, and diagnostic accuracy through standardized quantification and segmentation.
Clinical Evidence
No clinical data. Bench testing only. Performance evaluation of lung lobe segmentation (n=20) used Dice coefficient (DSC) and ASSD; results met acceptance criteria (DSC > 0.8 or ASSD < 10mm). PERCIST liver reference placement evaluated against expert reader semi-automatic VOI positioning (n=20) and intersection analysis with suspicious uptake (n=129); subject device showed fewer intersections (4 vs 13) than predicate.
Technological Characteristics
Software-only medical device; server/client architecture; DICOM connectivity. Features: PET/SPECT/CT/MR data processing, automated segmentation (AI/ML-based), SUV harmonization, and quantification. No physical materials or energy sources. Software version: VC10 (MI Workflows), VE70 (Scenium), VB30 (syngo MBF).
Indications for Use
Indicated for appropriately trained healthcare professionals to manage diseases (oncology, cardiology, neurology, organ function) via viewing, manipulation, quantification, analysis, and comparison of PET, NM, CT, and MR images. Includes radiotherapy treatment planning support.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
Predicate Devices
syngo.via MI Workflows; Scenium; syngo MBF (K242275)
Reference Devices
syngo.via MI Workflows; Scenium; syngo MBF (K232000)
Submission Summary (Full Text)
{0}
FDA U.S. FOOD & DRUG ADMINISTRATION
July 3, 2025
Siemens Medical Solutions USA, Inc.
Clayton Ginn
Regulatory Affairs Professional
2501 North Barrington Road
Hoffman Estates, Illinois 60192
Re: K251528
Trade/Device Name: syngo.via MI Workflows; Scenium; syngo MBF
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: May 19, 2025
Received: May 19, 2025
Dear Clayton Ginn:
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.
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"
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
{1}
K251528 - Clayton Ginn
Page 2
(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 System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 systems (QS) regulation (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.
{2}
K251528 - Clayton Ginn
Page 3
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-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,

Daniel M. Krainak, Ph.D.
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
{3}
syngo.via MI Workflows
| 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. | K251528 | ? |
| Please provide the device trade name(s). | | ? |
| syngo.via MI Workflows;
Scenium;
syngo MBF | | |
| Please provide your Indications for Use below. | | ? |
| syngo.via molecular imaging (MI) workflows comprise medical diagnostic applications for viewing,
manipulation, quantification, analysis and comparison of medical images from single or multiple imaging
modalities with one or more time-points. These workflows support functional data, such as positron
emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed
tomography (CT) or magnetic resonance (MR). syngo.via MI workflows can perform harmonization of SUV
(PET) across different PET systems or different PET reconstruction methods.
syngo.via MI workflows are intended to be utilized by appropriately trained health care professionals to aid
in the management of diseases, including those associated with oncology, cardiology, neurology, and organ
function. The images and results produced by the syngo.via MI workflows can also be used by the
physician to aid in radiotherapy treatment planning. | | |
| Please select the types of uses (select one or both, as
applicable). | ☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
{4}
SIEMENS Healthineers
# 510(k) Summary
K251528
## 1. Identification of the Submitter
**Submitter / Primary Contact Person**
Clayton Ginn
Regulatory Affairs
clayton.ginn@siemens-healthineers.com
+1 (865) 898-2692
**Secondary Contact Person**
Brian Wui
Regulatory Affairs
hansong.wui@siemens-healthineers.com
+1 (865) 367-4337
**Applicant Name and Address**
Siemens Medical Solutions, Inc. USA
2501 North Barrington Road
Hoffman Estates IL, 60192, USA
Establishment Registration Number: 1423253
**Date of Preparation**
May 15th, 2025
## 2. Device Name and Classification
| Product Trade Name: | syngo.via MI Workflows; Scenium; syngo MBF |
| --- | --- |
| Common Name: | Medical image management and processing system |
| Classification Name: | Automated Radiological Image Processing Software |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.2050 |
| Device Class: | Class II |
| Product Code: | QIH |
## 3. Predicate Devices
**Primary Predicate Device:**
| Product Trade Name: | syngo.via MI Workflows; Scenium; syngo MBF |
| --- | --- |
| 510(k) Number | K242275 |
| Clearance Date | 08/30/2024 |
Siemens Medical Solutions USA, Inc.
{5}
SIEMENS Healthineers
| Common Name: | Medical image management and processing system |
| --- | --- |
| Classification Name: | Automated Radiological Image Processing Software |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.2050 |
| Device Class: | Class II |
| Product Code: | QIH |
## Reference Predicate Device:
| Product Trade Name: | syngo.via MI Workflows; Scenium; syngo MBF |
| --- | --- |
| 510(k) Number | K232000 |
| Clearance Date | 11/28/2023 |
| Common Name: | Medical image management and processing system |
| Classification Name: | Automated Radiological Image Processing Software |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.2050 |
| Device Class: | Class II |
| Product Code: | QIH |
## 4. Device Description
syngo.via MI Workflows (including Scenium and syngo MBF applications) is a multi-modality post-processing software only medical device intended to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The syngo.via MI Workflows applications are part of a larger syngo.via client/server system which is intended to be installed on common IT hardware. The hardware itself is not seen as part of the syngo.via MI Workflows medical device.
The syngo.via MI Workflows software addresses the needs of the following typical users of the product:
- Reading Physician / Radiologist – Reading physicians are doctors who are trained in interpreting patient scans from PET, SPECT and other modality scanners. They are highly detail oriented and analyze the acquired images for abnormalities, enabling ordering physicians to accurately diagnose and treat scanned patients. Reading physicians serve as a liaison between the ordering physician and the technologists, working closely with both.
Siemens Medical Solutions USA, Inc.
{6}
SIEMENS Healthineers
- Technologist – Nuclear medicine technologists operate nuclear medicine scanners such as PET and SPECT to produce images of specific areas and states of a patient’s anatomy by administering radiopharmaceuticals to patients orally or via injection. In addition to administering the scan, the technologist must properly select the scan protocol, keep the patient calm and relaxed, monitor the patient’s physical health during the protocol and evaluate the quality of the images. Technologists work very closely with physicians, providing them with quality-checked scan images.
The software has been designed to integrate the clinical workflow for the above users into a server-based system that is consistent in design and look with the base syngo.via platform and other syngo.via software applications. This ensures a similar look and feel for radiologists that may review multiple types of studies from imaging modalities other than Molecular Imaging, such as MR.
The syngo.via MI workflows software supports integration through DICOM transfers of positron emission tomography (PET) or nuclear medicine (NM) data, as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).
Although data is automatically imported into the server based on predefined configurations through the hospital IT system, data can also be manually imported from external media, including CD, external mass storage devices, etc.
The Siemens syngo.via platform and the applications that reside on it, including syngo.via MI Workflows, are distributed via electronic medium. The Instructions for Use is also delivered via electronic medium.
syngo.via MI Workflows includes 2 workflows (syngo.MM Oncology and syngo.MI General) as well as the Scenium neurology software application and the syngo MBF cardiology software application which are launched from the OpenApps framework within the MI General workflow.
# 5. Indications for Use
syngo.via molecular imaging (MI) workflows comprise medical diagnostic applications for viewing, manipulation, quantification, analysis and comparison of medical images from single or multiple imaging modalities with one or more time-points. These workflows support functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR). syngo.via MI workflows can perform harmonization of SUV (PET) across different PET systems or different PET reconstruction methods.
syngo.via MI workflows are intended to be utilized by appropriately trained health care professionals to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The images and results produced by the syngo.via MI workflows can also be used by the physician to aid in radiotherapy treatment planning.
Siemens Medical Solutions USA, Inc.
{7}
SIEMENS Healthineers
# 6. Indications for Use Comparison to the Predicate Device
The indications for use are the same between the subject device and the primary predicate device.
# 7. Comparison of Technological Characteristics with the Predicate Device
syngo.via MI Workflows with software version VC10, Scenium with software version VE70, and syngo MBF with software version VB30 software provide the same technological characteristics in terms of materials, energy source, and control mechanisms when compared to the legally marketed predicate device since all devices are software only devices.
The software features have been modified in comparison to the predicate device to support enhanced device functionality.
The intended use, indications for use, and fundamental scientific technology for the subject device remains unchanged from the predicate device. No features present from the predicate device have been de-scoped.
At a high level, the subject and predicate devices are based on the following same technological elements:
- Data Supported (PET, SPECT, CT, MR)
- Server/Client architecture
- Workflow Activities (preprocessing, evaluation and reading, reporting and storage)
- Feature Licensing Structure
- SUV values calculated
The following technological differences exist between the subject device and predicate devices.
syngo.via MI Workflows VC10:
## MI General
- Improved Lung Segmentation in Anatomy Segmentation and Auto Lung 3D
- VQ Ratio and Subtraction Images for Auto Lung 3D
- General Masking Tool for PET/SPECT/GNM
- Deauville Score from MM Oncology
- Improved Liver Reference Region Placement
- CT Oncology Extensions
- Basic Oncology Tools: Assisted Perpendicular (RECIST/WHO) and Nodule Marker (Lesion quantification / lung lesion segmentation)
- Lung CAD
- Layout editing and User Presets updates
- Organ Processing Updates
- CBF Image Creation and Display
- GSA Liver
- Cardiac Shunt
Siemens Medical Solutions USA, Inc.
{8}
SIEMENS Healthineers
MM Oncology
- Extended Large Matrix CT Data Support
- Advanced Density Tool Enhancements for CT Segmentations
- SPP 2.0 Extended Support (DE Preprocessing, VNC/lodine Fusion, DE Layouts)
Scenium VE70:
- No changes
syngo MBF VB30:
- No changes
Any differences in technological characteristics do not raise different questions of safety and effectiveness. Testing and validation are completed. Test results show that the subject devices are comparable to the predicate devices in terms of technological characteristics and safety and effectiveness and therefore are substantially equivalent to the predicate devices.
## 8. Non-Clinical and/or Clinical Test Summary & Conclusions
The following performance data were provided in support of the substantial equivalence determination.
### Non-Clinical Testing
‘Enhanced’ software documentation per FDA’s guidance document “Content of Premarket Submissions for Device Software Functions” issued in June, 2023 is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. The testing supports that all software specifications have met the acceptance criteria. Verification and validation testing substantiates all requirement and functional specifications, including specifications related to device hazards, and supports the claim of substantial equivalence.
### Lung and Lung Lobe Segmentation
In addition to verification and validation testing, performance evaluation was conducted in order to ensure the safety and effectiveness of the lung lobe segmentation algorithm compared to the predicate device. The lung lobe segmentation algorithm was re-trained with additional data and is utilized within the Auto Lung 3D and Anatomy Segmentation features of the MI General workflow.
Quantitative evaluation of the segmentation results was performed using the commonly used overlap measure Dice coefficient (DSC). The algorithm was tested retrospectively on an independent cohort of 20 patients that were not part of training or tuning cohort. The test cohort was augmented compared to the predicate to include new subjects. Half of the patients in the test cohort were new and the other 50% were randomly selected from the predicate testing cohort. Performance was compared between the predicate and updated algorithms. In summary:
I. No overlap of patients between training, tuning, and test cohorts.
II. Relevant test cohort parameters are as follows:
- ~50% male patients
- Slice thickness <=5 mm
Siemens Medical Solutions USA, Inc.
{9}
SIEMENS Healthineers
- 50% of patients were from the US
- All patients from Siemens Scanner
- Adults, age > 21
Acceptance Criteria for organ segmentation in syngo.via MI Workflows:
- For new organs, the average Dice coefficient per organ shall be greater than 0.8 or the average symmetric surface distance (ASSD) per organ less than 2 voxel of worst slice thickness, i.e. 10 mm.
- For unchanged organs, the average Dice coefficient per organ shall be within +/-0.03 of the average Dice coefficient per organ of the predicate algorithm.
- For improved organs, the average Dice coefficient per organ shall be greater or equal than the average Dice coefficient per organ of the predicate algorithm.
The average Dice coefficient for the 20 subjects was higher for each lobe in the subject device than in the predicate device, although not greater than a +0.03 difference for all lobes.
The anatomy segmentation feature supports segmentation for a wide variety of organs. Since each organ utilizes a different model, the Dice-score on other organs (other than lungs and lung lobes) that were not retrained remained unchanged and this was verified by recalculating the Dice score with the new algorithm.
## PERCIST Liver Reference Region Placement
Additionally, performance evaluation was conducted for the updated PERCIST Liver Reference Region placement. The algorithm takes as input the PET/CT image together with a binary liver mask and returns the coordinates of the reference region center.
The performance specifications for the binary liver mask used as input to the algorithm are detailed below. This AI/ML segmentation algorithm is the same segmentation algorithm as utilized in the Anatomy Segmentation feature of the reference predicate device (K232000).
The test data consisted of 20 patients. The patients were obtained from clinical partners in Europe and USA. The data was randomly selected using the following stratification:
- No overlap of patients between training, tuning, and test cohorts
- Adults, Age > 21
- ~50% male patients
- Slice thickness <= 5 mm
- All subject from Siemens Scanner
The acceptance criteria for the liver and all other organs supported by the anatomy segmentation feature is an average Dice coefficient greater than 0.8 or an average symmetric surface distance (ASSD) less than 10 mm. The liver met both criteria.
The performance evaluation for the updated PERCIST Liver Reference Region placement is detailed below.
Siemens Medical Solutions USA, Inc.
{10}
SIEMENS Healthineers
In the first analysis conducted, the reference standard used to evaluate the subject device method performance consisted of liver VOI positioning obtained semi-automatically by two expert readers. The subject device algorithm was then compared to the reference standard and shown to yield results in better agreement with semi-automatic evaluation by expert readers compared with the method of placement used in the predicate device.
The second analysis conducted focused on PET/CT scans presenting foci with suspicious tracer uptake either in the liver or with spill over to the liver, evaluating the subject and predicate devices based on how often the PERCIST VOIs intersected the suspicious uptake masks identified by an expert reader. Out of 129 subjects included in the analysis, the subject device had fewer intersections (4 cases) compared to the predicate device (13 cases). As with the predicate device, the user can manually reposition the PERCIST liver reference region at any time.
## Clinical Testing
Clinical testing was not conducted for this submission.
## Conclusion
There are no differences in the Indications for Use, Intended Use, or Fundamental Technological Characteristics of the updated syngo.via MI Workflows software (including Scenium and syngo MBF) as compared to the currently commercially available syngo.via MI Workflows software (K242275).
Both the current and predicate devices are used for viewing, manipulation, quantification, analysis, and comparison of medical images from single or multiple imaging modalities with one or more time-points.
Additionally, the new features implemented within this release do not raise any new issues of safety and effectiveness as compared to the predicate device. The predicate devices were cleared based on the results of non-clinical testing including verification and validation. The subject device is also validated using the same methods as used for the predicate devices. The non-clinical verification and validation demonstrate that the subject devices should perform as intended in the specified use conditions.
Based on this information, as well as the documentation in support of the modifications, the subject devices with the modifications outlined in this application are substantially equivalent to the predicate devices.
Siemens Medical Solutions USA, Inc.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.