K222275 · DeepHealth, Inc. · QIH · Dec 16, 2022 · Radiology
Device Facts
Record ID
K222275
Device Name
Saige-Density
Applicant
DeepHealth, Inc.
Product Code
QIH · Radiology
Decision Date
Dec 16, 2022
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Breast density category
Deep learning
—
81.28% accuracy (95% CI: 78.42, 83.84)
—
—
Multi-site retrospective study: 796 mammogram cases (6,170 images) from five US breast imaging centers
5 (expert radiologists)
Indications for Use
Saige-Density is a software application intended for use with compatible full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems. Saige-Density provides an ACR BI-RADS Atlas 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. Saige-Density produces adjunctive information. It is not a diagnostic aid.
Device Story
Saige-Density is SaMD processing screening/diagnostic digital mammograms (FFDM/DBT) via deep learning; generates study-level breast density category (ACR BI-RADS 5th Edition A-D). Input: single x-ray mammogram study (2D DICOM files). Output: structured report (SR) DICOM object and secondary capture (SC) DICOM summary report. Used in clinical settings by interpreting radiologists; viewed on mammography workstations. Provides adjunctive information; not a diagnostic aid. Does not replace physician review; clinical decisions should not rely solely on device output.
Clinical Evidence
Retrospective multi-site study of 796 cases (6,170 images) from five US centers. Ground truth established by consensus of five expert radiologists. Primary endpoint: accuracy of density category assignment. Four-class accuracy: 81.28% (95% CI: 78.42, 83.84). Two-class (dense vs. non-dense) performance showed 87.8% sensitivity/specificity for non-dense and 95.2% for dense categories. No overlap between training and testing datasets.
Technological Characteristics
Software-only device; deep learning algorithm. Compatible with FFDM and DBT systems. Outputs DICOM SR and SC objects. Moderate level of concern. No hardware components.
Indications for Use
Indicated for female patients 35 years of age or older undergoing mammography to aid interpreting physicians in assessing breast tissue composition.
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).
{0}------------------------------------------------
December 16, 2022
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
DeepHealth, Inc. % B. Nathan Hunt VP, Quality Assurance and Regulatory Affairs 1000 Massachusetts Avenue CAMBRIDGE MA 01238
#### Re: K222275
Trade/Device Name: Saige-Density Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: November 7, 2022 Received: November 7, 2022
#### Dear B. Nathan Hunt:
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
{1}------------------------------------------------
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 mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
# Yanna S. Kang -S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team 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
{2}------------------------------------------------
# Indications for Use
510(k) Number (if known) K22275
Device Name Saige-Density
Indications for Use (Describe)
Saige-Density is a software application intended for use with compatible full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems. Saige-Density provides an ACR BI-RADS Atlas 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. Saige-Density produces adjunctive information. It is not a diagnostic aid.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|
| <div> <span> <b> X </b> </span> <span> Prescription Use (Part 21 CFR 801 Subpart D)</span> </div> | <div> <span> Over-The-Counter Use (21 CFR 801 Subpart C)</span> </div> |
#### CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
#### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{3}------------------------------------------------
Image /page/3/Picture/0 description: The image contains the logo for DeepHealth. On the left side of the logo is a stack of three squares, each slightly offset from the one below it. To the right of the squares is the word "DeepHealth", with "Deep" in a dark red color and "Health" in gray.
1000 Massachusetts Ave Cambridge. MA 02138 Phone: 424-832-1480 www.deep.health
## 510(k) Summary DeepHealth, Inc. Saige-Density (K222275)
In accordance with 21 CFR 807.92 the following summary of information is provided, on this date, December 15, 2022:
#### 1. 510(k) SUBMITTER
DeepHealth, Inc. 1000 Massachusetts Avenue Cambridge, MA 02138 Tel: 424-832-1480
#### Contact Person:
B. Nathan Hunt Vice President, Quality Assurance and Regulatory Affairs DeepHealth, Inc. 1000 Massachusetts Avenue Cambridge, MA 02138 Tel: 424-832-1480 nhunt@deep.health
#### Date Prepared:
December 15, 2022
#### 2. DEVICE
Trade Name of Device: Saige-Density
Common or Usual Name: Medical Image Software
Regulation Name and Number: Medical Image Management and Processing System (21 CFR 892.2050)
Regulation Class: II
Product Code: QIH
#### 3. PREDICATE DEVICE
Trade Name: Densitas densityai™
Common Name or Usual Name: Medical Image Software
Regulation Name and Number: Picture Archiving and Communication System (21 CFR 892.2050)
Regulation Class: II
Product Code: LLZ
510(K) No.: K192973
{4}------------------------------------------------
Image /page/4/Picture/0 description: The image contains the logo for DeepHealth. On the left side of the logo, there is a stack of three squares, each slightly offset from the one below it. To the right of the squares, the word "DeepHealth" is written in a combination of red and gray letters. The word "Deep" is in red, while the word "Health" is in gray.
1000 Massachusetts Ave Cambridge. MA 02138 Phone: 424-832-1480 www.deep.health
#### 4. DEVICE DESCRIPTION
Saige-Density is Software as a Medical Device that processes screening and diagnostic digital mammograms using deep learning techniques and generates outputs that serve as an aid for interpreting radiologists in assessing breast density. The software takes as input a single x-ray mammogram study and processes all acceptable 2D image DICOM files (FFDM and/or 2D synthetics) and generates a single study-level breast density category. Two DICOM files are outputted as a result: 1) a structured report (SR) DICOM object containing the case-level breast density category and 2) a secondary capture (SC) DICOM object containing a summary report with the study-level density category. Both output files contain the same breast density category ranging from "A" through "D" following Breast Imaging Reporting and Data System (BI-RADS) 5ª Edition reporting guidelines. The SC report and/or the SR file may be viewed on a mammography viewing workstation.
#### 5. INDICATIONS FOR USE
Saige-Density is intended for use with compatible full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems. Saige-Density provides an ACR BI-RADS Atlas 5th Edition breast density category to aid interpreting physicians in the assessment of breast tissue composition. Saige-Density produces adjunctive information. It is not a diagnostic aid.
#### Intended User Population
The intended users of Saige-Density are interpreting physicians qualified to read mammography exams.
#### Intended Patient Populations
The device is intended to be used on female patients thirty-five (35) years of age or older undergoing mammography.
#### Warnings and Precautions
Saige-Density is an adjunct tool and is not intended to replace a physician's own review of a mammogram. Decisions should not be made solely based on analysis by Saige-Density.
#### 6. PREDICATE DEVICE COMPARISON
Saige-Density and the predicate device have similar indications for use, patient population, technical characteristics, and principles of operation. The differences between Saige-Density and the predicate device do not alter the suitability of the subject device for its intended use, and do not raise different questions of safety or effectiveness.
The devices are intended to be used by physicians to aid in the assessment of breast density for mammograms. The devices are not intended to be used as a replacement of a physician's own clinical judgment.
The design of Saige-Density is similar to that of the predicate device. Both devices are compatible with FFDM and DBT mammograms, utilize deep learning to produce an equivalent main output of a patient-level breast density category, and are not diagnostic aids that are intended to be used by physicians interpreting either screening or diagnostic mammograms. As both devices use
{5}------------------------------------------------
Image /page/5/Picture/0 description: The image shows the logo for DeepHealth. The logo consists of a stack of three squares on the left, followed by the word "DeepHealth" in a combination of red and gray. The word "Deep" is in red, while the word "Health" is in gray.
1000 Massachusetts Ave Cambridge. MA 02138 Phone: 424-832-1480
proprietary algorithms, there are assumed differences in the algorithmic components, as well as minor differences in the specific formats of the outputs provided to users.
Non-clinical and clinical testing has been completed ensuring that the differences do not affect the safety and effectiveness of the proposed subject device.
#### 7. PERFORMANCE DATA
Saige-Density is a software device and has been determined to be of Moderate Level of Concern. Verification testing included software unit testing, software integration testing, and system testing. Testing confirmed that the software, as designed and implemented, satisfies the software requirements.
Validation of the software was performed using a retrospective study as described below. The data used in the validation testing was obtained from different clinical sites than those used to develop the Saige-Density algorithm. DeepHealth ensured that there was no overlap between the data used to train and test the Saige-Density algorithm. The data used to train the Saige-Density algorithm consisted of four datasets across various geographic locations within the US, including racially diverse regions such as New York City and Los Angeles.
#### Standalone Performance Testing:
A multi-site retrospective study was conducted to evaluate the standalone performance of Saige-Density on DBT and FFDM mammograms. The primary objective was to quantify the accuracy of Saige-Density's density category outputs with respect to a consensus of five expert radiologists. The statistical analysis was performed by an independent biostatistician.
A total of 796 mammogram cases, representing 6,170 images, were retrospectively collected from five breast imaging centers in the United States. Importantly, the collection sites selected for the pivotal study did not overlap with those used previously to collect data for training or testing the Saige-Density AI algorithm. The cases were gathered from unique female patients 35 years of age or older and included different modalities (DBT and FFDM), manufacturers (Hologic and GE), and exam types (screening and diagnostic). Each mammogram included in the study had one ground truth status for density: A (Fatty), B (Scattered Fibroglandular), C (Heterogeneously Dense), or D (Extremely Dense) as defined by ACR's BI-RADS 5th Edition guidelines. Ground truth was established for each case as the consensus of five expert radiologists' breast density categories on the same set of cases, and calculated as the median of the reported categories for each case.
Saige-Density's accuracy was computed as the percentage of cases that Saige-Density assigned the same density category to as ground truth. Saige-Density's accuracy on four-class categorization (density categories A, B, C, or D) was 81.28% (95% CI: 78.42, 83.84). Results for four-class and two-class (nondense: A, B; dense: C,D) categorization with respect to ground truth are summarized in Figures 1 and 2, respectively.
{6}------------------------------------------------
Image /page/6/Picture/0 description: The image contains the logo for DeepHealth. On the left side of the logo, there is a stack of three squares, each slightly offset from the one below it. The squares are a dark red color. To the right of the squares, the word "DeepHealth" is written in a sans-serif font. The word "Deep" is dark red, while the word "Health" is gray.
1000 Massachusetts Ave. Cambridge, MA 02138 Phone: 424-832-1480 www.deep.health
Image /page/6/Figure/2 description: The image shows a confusion matrix with four categories labeled A, B, C, and D on both axes. The matrix displays the counts and percentages of predicted versus actual classifications. For example, category A has 62 true positives (77.5%) and 18 false negatives (22.5%). Category D has 74 true positives (97.4%) and 1 false negative (1.3%).
# Saige-Density
Image /page/6/Figure/4 description: The image is a title for a figure. The title reads "Figure 1. Four-class confusion matrix for all pivotal cases." The title is written in a clear, sans-serif font and is left-aligned. The text suggests that the figure is a confusion matrix, which is a table used to evaluate the performance of a classification model.
Image /page/6/Figure/5 description: The image shows a confusion matrix with two classes: "nondense" and "dense". The matrix displays the counts and percentages for each combination of predicted and actual classes. For the "nondense" class, 351 instances are correctly classified (87.8%), while 49 instances are misclassified as "dense" (12.2%). For the "dense" class, 377 instances are correctly classified (95.2%), while 19 instances are misclassified as "nondense" (4.8%).
# Saige-Density
Figure 2. Two-class confusion matrix for all pivotal cases.
{7}------------------------------------------------
Image /page/7/Picture/0 description: The image shows the logo for DeepHealth. The logo consists of a stack of three squares on the left, followed by the word "DeepHealth" in a combination of red and gray. The word "Deep" is in red, while the word "Health" is in gray.
1000 Massachusetts Ave. Cambridge, MA 02138 Phone: 424-832-1480 www.deep.health
### 8. CONCLUSION
The non-clinical and clinical testing conducted to support this submission confirm that Saige-Density is safe and effective. The minor differences, including technological differences, between Saige-Density and the predicate do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. Therefore, the information presented in this 510(k) submission demonstrates that Saige-Density is substantially equivalent to the predicate device.
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.