The ARCHITECT Uric Acid test system is a device intended to measure uric acid in serum, plasma, and urine. Measurements obtained by this device are used in the diagnosis and treatment of numerous renal and metabolic disorders, including renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
Device Story
In vitro diagnostic assay for ARCHITECT c8000 System; measures uric acid in human serum, plasma, or urine. Uses two-part (R1/R2) uricase methodology. Uricase oxidizes uric acid to allantoin, producing hydrogen peroxide (H2O2). H2O2 reacts with 4-aminoantipyrine and HMMPS via peroxidase to form quinoneimine dye. Absorbance change at 604 nm proportional to uric acid concentration. R1 contains ascorbic oxidase to reduce ascorbic acid interference. Used in clinical laboratories; results interpreted by physicians to manage renal/metabolic conditions.
Clinical Evidence
Bench testing only. Performance evaluated per CLSI guidelines (EP5-A2, EP6-A, EP7-A2, EP9-A2, EP17-A). Precision studies (N=80) showed total CVs < 2.1% for urine and < 4.2% for serum. Linearity demonstrated across the measuring range (r > 0.999). Method comparison (N=103 serum, N=103 urine) against the predicate yielded slopes of 0.95 and correlation coefficients of 0.9955. Interference testing confirmed non-significant bias (within ±10%) for common endogenous substances including ascorbic acid, bilirubin, glucose, and hemoglobin.
Technological Characteristics
In vitro diagnostic reagent kit. Uricase-based enzymatic colorimetric assay. Two-part (R1/R2) liquid reagent configuration. Includes ascorbic oxidase for interference reduction. Photometric detection at 604 nm. Designed for use on ARCHITECT c8000 automated clinical chemistry analyzer.
Indications for Use
Indicated for the quantitative measurement of uric acid in serum, plasma, and urine to aid in the diagnosis and treatment of renal and metabolic disorders such as renal failure, gout, leukemia, psoriasis, and starvation, and for monitoring patients receiving cytotoxic drugs.
Regulatory Classification
Identification
A uric acid test system is a device intended to measure uric acid in serum, plasma, and urine. Measurements obtained by this device are used in the diagnosis and treatment of numerous renal and metabolic disorders, including renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k102568
B. Purpose for Submission:
New Device
C. Measurand:
Uric Acid
D. Type of Test:
Quantitative, enzymatic, colorimetric assay
E. Applicant:
Abbott Laboratories Diagnostics Division
F. Proprietary and Established Names:
Architect Uric Acid
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| KNK | Class I, reserved | 21 CFR §862.1775, uric acid test system | Clinical Chemistry (75) |
H. Intended Use:
1. Intended use(s):
See indications for use below:
2. Indication(s) for use:
Architect Uric Acid test system is a device intended to measure uric acid in serum, plasma, and urine. Measurements obtained by this device are used in the diagnosis and treatment of numerous renal and metabolic disorders, including
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renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
3. Special conditions for use statement(s):
For prescription use only
4. Special instrument requirements:
Abbott ARCHITECT c8000
I. Device Description:
The uric acid reagent kit is packaged as two ready to use reagents, R1 and R2. The following reactive ingredients are presented in the following table:
| R1 | Reactive Ingredients | Concentration |
| --- | --- | --- |
| | 5-Chloro-2-methyl-4-isothiazoline-3-one | 0.5 mmol/L |
| | Ascorbic Oxidase | 3500 U/L |
| | HMMPS | 100 mmol/L |
| R2 | 4-Aminoantipyrine | 4 mmol/L |
| | 5-Chloro-2-methyl-4-isothiazoline-3-one | 0.5 mmol/L |
| | Peroxidase | 2000 U/L |
| | Uricase | 880 U/L |
Inactive Ingredients: R2 contains sodium azide (0.05%) as a preservative.
J. Substantial Equivalence Information:
1. Predicate device name(s):
Abbott On-Market Uric Acid
2. Predicate 510(k) number(s):
K981766
3. Comparison with predicate:
Similarities and Differences
| Characteristic | Candidate device: Architect Uric Acid | Predicate device: Abbott On-Market Uric Acid (k981766) |
| --- | --- | --- |
| Intended Use | Same | For the quantitation of uric |
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| | | acid in human serum, plasma, or urine |
| --- | --- | --- |
| Product Type | Same | Clinical Chemistry |
| Methodology | Same | Uricase |
| Where Used | Same | Clinical Laboratories |
| Assay Protocol | Same | Competitive |
| Calibration Curve Type | Same | 3-point |
| Specimen Type | Same | Serum (including serum separator tubes) or plasma (collected in lithium heparin, lithium heparin separator tubes, and sodium heparin). 24-hour urine specimens are preferred. |
| Platform | ARCHITECT c8000 System | ARCHITECT cSystems and AEROSET System |
| Components | Reagent Kit: Uric Acid is supplied as a liquid, ready-to-use, two-reagent kit which contains: R1 Contains: 5-Chloro-2-methyl-4-isothiazoline-3-one 0.5 mmol/L; Ascorbic Oxidase 3500 U/L; HMMPS 100 mmol/L. R2 contains: 5-Chloro-2-methyl-4-isothiazoline-3-one 0.5 mmol/L; 4-Aminoantipyrine 4 mmol/L; Peroxidase 2000 U/L; Uricase 880 U/L. | Reagent Kit: Uric Acid is supplied as a liquid, ready-to-use, single-reagent kit which contains: R1 contains: 4-Aminoantipyrine 0.5 mmol/L; TBHB 1.75 mmol/L; Uricase > 120 U/L; Peroxidase > 500 U/L; TRIS Buffer 50 mmol/L. |
| Measuring Interval | Serum: 1.0 mg/dL to 33.1 mg/dL Urine: 5.0 mg/dL to 250.0 mg/dL | Serum: 0.3 mg/dL to 33.1 mg/dL Urine: 1.0 mg/dL to 433.8 mg/dL |
# K. Standard/Guidance Document Referenced (if applicable):
CLSI documents:
Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline (C28-A3)
Evaluation of Precision Performance of Clinical Chemistry Devices; Approved Guideline (EP5-A2)
Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical
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Approach (EP6-A)
Interference Testing in Clinical Chemistry; Approved Guideline (EP 7-A2)
Method Comparison and Bias Estimation Using Patient Samples; Approved Guideline (EP9-A2)
Protocols for Determination of Limits of Detection and Limits of Quantitation (EP17-A)
## L. Test Principle:
The uric acid assay is a two-part reaction. Uric acid is oxidized to allantoin by uricase with production of hydrogen peroxide (H2O2). The H2O2 reacts with 4-aminoantipyrine (4-AAP) and N-(3-sulfopropyl)-3-methoxy-5-methylanaline (HMMPS) in the presence of peroxidase (POD) to yield a quinoneimine dye. The resulting change in absorbance at 604 nm is proportional to the uric acid concentration in the sample. The two-part (R1/R2) configuration of this assay allows reduction of interference from ascorbic acid by inclusion of ascorbic oxidase in the R1 portion of the assay.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
Precision was performed for 20 days using one lot of reagent, one lot of calibrators, and one lot of controls following procedures outlined in CLSI EP5-A2. All testing was performed on the ARCHITECT c8000. Two serum samples, two serum controls, and two urine controls were tested in replicates of five, in two runs per day for 8 days (N=80). Results are summarized in the following tables:
20 day precision for serum and urine controls
| Matrix | Level | N | Mean (mg/dl) | Within-run | | Total precision | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | SD | %CV | SD | %CV |
| Serum Controls | 1 | 80 | 4.49 | 0.02 | 0.49 | 0.03 | 0.68 |
| | 2 | 80 | 9.14 | 0.04 | 0.41 | 0.05 | 0.52 |
| Urine Controls | 1 | 80 | 9.09 | 0.09 | 0.99 | 0.19 | 2.10 |
| | 2 | 80 | 17.48 | 0.11 | 0.64 | 0.18 | 1.02 |
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Precision for serum and urine samples
| Matrix | Level | N | Mean (mg/dl) | Within-run | | Total precision | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | SD | %CV | SD | %CV |
| Serum Samples | 1 | 80 | 1.23 | 0.01 | 0.70 | 0.05 | 4.18 |
| Urine Sample | 1 | 50 | 242.10 | 1.26 | 0.5 | 2.03 | 0.80 |
An additional 5 day precision study was conducted for a high serum sample using spiked serum sample pool. The results are shown in the following table:
| Matrix | Level | N | Mean (mg/dL) | Within-run | | Total precision | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | SD | %CV | SD | %CV |
| Serum | 2 | 50 | 31.59 | 0.130 | 0.4 | 0.156 | 0.50 |
b. Linearity/assay reportable range:
A linearity study was performed following the CLSI EP6-A guideline. Samples were prepared by diluting a high patient sample with a low patient sample to obtain twelve different concentrations spanning the measuring range; ranging from $0.00 - 70.00\mathrm{mg / dL}$ for serum samples and $0.00 - 300.00\mathrm{mg / dL}$ for urine samples. Each serum and urine sample was tested in replicates of four using two lots of reagents on one instrument (One represented lot of reagent result was summarized below). The observed values were plotted against the expected values and an appropriate line fitted by standard linear regression. Results were summarized below:
| | Serum (lot 1) | Urine (lot 1) |
| --- | --- | --- |
| Correlation (r) | 0.9999 | 0.9996 |
| Slope | 0.9920 | 1.0105 |
| Intercept | 0.1677 | 0.6791 |
The linearity data support the sponsor's claim that the measuring range for serum is $1 - 33.1\mathrm{mg / dL}$ , and urine is $5 - 250\mathrm{mg / dL}$
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Calibrators were previously cleared- See k103403 for traceability, stability, and expected value information.
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# d. Detection limit:
The sponsor conducted a Limit of Blank (LoB), Limit of Detection (LoD) and a Limit of Quantitation (LoQ) study following the CLSI EP-17A guideline. To determine the limit of blank (LoB), zero level samples were prepared for serum and urine (4 aliquots each) with testing performed on two instruments, using two reagent lots, one lot of calibrators, and one lot of controls for a total of 160 replicates. Four low level serum (target concentrations 0.16, 0.18, 0.20, and $0.22\mathrm{mg / dL}$ ) and four low level urine samples (target concentrations 1.60, 1.80, 2.00, and $2.20\mathrm{mg / dL}$ ) were prepared. The low level samples also had testing performed on two instruments, using two reagent lots, one lot of calibrators, and one lot of controls for a total of 40 replicates each. LoQ was determined based on 5 separate runs over 3 days. LoQ for serum was determined at $0.22\mathrm{mg / dL}$ based on an interassay imprecision of $\leq 18.3\%$ , and LoQ for urine was $2.20\mathrm{mg / dL}$ based on an interassay imprecision of $\leq 6\%$
Results are summarized in the following tables:
| | Serum (mg/dL) | Urine (mg/dL) |
| --- | --- | --- |
| LoB | 0.02 | 0.07 |
| LoD | 0.06 | 0.24 |
| LoQ | 0.22 | 2.20 |
Sponsor's claimed measuring range for serum is $1 - 33.1\mathrm{mg / dL}$ , and urine is $5 - 250\mathrm{mg / dL}$
# e. Analytical specificity:
A study of common interfering substances was conducted. The results of the study were evaluated following recommendations by CLSI EP7-A2. Serum samples were prepared at target uric acid concentrations of 3 and $9\mathrm{mg / dL}$ . Urine uric acid samples were prepared at a target concentration of $12.5\mathrm{mg / dL}$ and $30\mathrm{mg / dL}$ . Various concentrations of interfering substances were spiked into the serum or urine sample pools. The sponsor states that interferences are considered to be non-significant if the bias between the spiked and non-spiked samples are within $\pm 10\%$ . Results are summarized in the following tables.
The following table summarizes serum uric acid specificity:
Endogenous Interferences
| | | Percent Recovery | |
| --- | --- | --- | --- |
| Interfering Substance | Interferent Concentration | 3.0 mg/dL Uric Acid | 9.0 mg/dL Uric Acid |
| Ascorbic Acid | 3.0 mg/dL | 95.3 | 97.8 |
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| Bilirubin* | 60 mg/dL | 92.3 | 98.1 |
| --- | --- | --- | --- |
| Glucose | 1000 mg/dL | 100.1 | 100.1 |
| Hemoglobin | 2000 mg/dL | 101.3 | 98.6 |
| Intralipid | 750 mg/dL | 92.7 | 95.4 |
* Bilirubin solutions were prepared by addition of half conjugated/half unconjugated bilirubin to human serum pools.
The following tables summarize urine uric acid specificity:
Endogenous Interferences
| | | Percent Recovery | |
| --- | --- | --- | --- |
| Interfering Substance | Interferent Concentration | 12.5 mg/dL Uric Acid | 30 mg/dL Uric Acid |
| Albumin | 50 mg/dL | 101.2 | 99.1 |
| Ascorbic Acid | 200 mg/dL | 94.9 | 95.8 |
| Conjugated Bilirubin | 60 mg/dL | 97.7 | 91.4 |
| Glucose | 1000 mg/dL | 100.1 | 99.7 |
| Hemoglobin | 2000 mg/dL | 102.4 | 99.1 |
Urine Preservative Interferences
| | | Percent Recovery | |
| --- | --- | --- | --- |
| Interfering Substance | Interferent Concentration | 12.5 mg/dL Uric Acid | 30 mg/dL Uric Acid |
| Boric Acid | 1000 mg/dL | 101.2 | 99.0 |
| 6N HCl | 1.0 mL/dL | 97.3 | 98.0 |
| 2.5N NaOH | 2.5 mL/dL | 102.5 | 99.6 |
f. Assay cut-off:
Not Applicable
2. Comparison studies:
a. Method comparison with predicate device:
A method comparison study was conducted using 103 human serum samples and 103 urine samples. All samples were tested on the Architect c8000 analyzer with the candidate device (Architect Uric Acid) against the predicate device (Abbott On-Market Uric Acid). Some samples were diluted and spiked to cover the hard-to-find sample range. The following table provides a summary of regression analysis:
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| Sample type | N | Range (mg/dL) | Slope | Intercept | Correlation Coefficient |
| --- | --- | --- | --- | --- | --- |
| Serum | 103 | 1.6 – 31.2 | 0.95 | 0.20 | 0.9955 |
| Urine | 103 | 10.3 – 247.6 | 0.95 | 1.34 | 0.9955 |
b. Matrix comparison:
For matrix comparison study, matched serum and heparinized plasma samples were assayed on the ARCHITECT c8000. A total of 20 sample sets of human specimens were collected in the following blood collection tube types: glass tube (serum), and plastic tubes – SST tube, lithium heparin, sodium heparin, and plasma lithium heparin separator. All the results generated from the plastic tubes (SST tube, lithium heparin, sodium heparin, and plasma lithium heparin with gel separator) had less than 5% differences when compared to the glass tube (control).
The sponsor states that their device can be used with serum and the following anticoagulants:
Lithium heparin, sodium heparin, and plasma lithium heparin with gel separator
3. Clinical studies:
a. Clinical Sensitivity:
Not applicable
b. Clinical specificity:
Not applicable
c. Other clinical supportive data (when a. and b. are not applicable):
Not applicable
4. Clinical cut-off:
Not applicable
5. Expected values/Reference range:
In the labeling the expected values are provided from Burtis CA, Ashwood ER,
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Bruns DE, eds. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, 4th ed. St. Louis, MO: Elsevier Saunders; 2006:2301.
Literature Reference Intervals for Uric Acid:
Serum:
Male: 3.5 – 7.2 mg/dL
Female: 2.6 – 6.0 mg/dL
Urine:
| Urine | Range (mg/day) | Range (mmol/day) |
| --- | --- | --- |
| Purine-free diet | | |
| Male | <420 | <2.48 |
| Female | Slightly lower | Slightly lower |
| Low purine diet | | |
| Male | <480 | 2.83 |
| Female | <400 | <5.90 |
| High purine diet | <1,000 | <5.90 |
| Average | 250 to 750 | 1.48 to 4.43 |
To convert results from mg/day to mmol/day, multiply mg/day by 0.0059.
It is recommended that each laboratory determine its own reference range based upon its particular locale and population characteristics.
## 24 Hour Urinary Excretion
To convert results from mg/dL to mg/day (24 hour urinary excretion)
Where:
V = 24 hour urine volume (mL)
C = analyte concentration (mg/dL)
24 hour excretion = [(V x c) / 100] mg/day
To convert results from mmol/L to mmol/day (24 hour urinary excretion)
Where:
V = 24 hour urine volume (mL)
C = analyte concentration (mmol/L)
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24 hour excretion = [(V x c) / 1000] mmol/day
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
O. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
10
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.