FDA clearance means a specific AI device is authorized for the exact intended use described in its label—not that every AI recurrence prediction is accurate, that the test improves survival, or that treatment decisions can be made without an oncologist. For a 510(k)-cleared device, the FDA finds substantial equivalence to a legally marketed predicate and reviews supporting safety and performance information. The population, data inputs, outcome being predicted, and role in care determine what the result means.
Clearance applies to a device and an indication
The FDA regulates a medical device and its labeled intended use, not “AI” as an abstract technology. A clearance therefore has boundaries. It identifies who the device is for, what information it may analyze, what outcome it estimates, and how clinicians are expected to use the result.
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For example, a tool cleared for adults with hormone-receptor-positive (HR-positive), HER2-negative stage I–IIIA breast cancer cannot automatically be treated as validated for triple-negative disease, HER2-positive disease, men, metastatic cancer, or a different stage. A score that estimates recurrence-free survival is not interchangeable with one that estimates distant-metastasis risk. Those distinctions are clinical, not marketing fine print.
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What a 510(k) clearance actually evaluates
Most readers encounter “FDA-cleared” in connection with the 510(k) pathway. The FDA describes 510(k) review as comparative: the applicant must show that its device is substantially equivalent to a legally marketed predicate device. Substantial equivalence does not require the new product to be identical to the predicate. It means the differences do not raise different questions of safety and effectiveness, and that the device performs appropriately for its intended use.
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The FDA’s own distinction is: “The 510(k) review standard is comparative, whereas the PMA standard relies on an independent demonstration of safety and effectiveness.” A 510(k) submission can include analytical, clinical, software, cybersecurity, and other performance information appropriate to the device. Clearance is an authorization to market for the reviewed use; it is not a blanket certification that an algorithm is universally accurate or beneficial for every patient.
Clearance, approval and other FDA pathways
| Term | What it means | What a patient should infer |
|---|---|---|
| 510(k) cleared | The device is substantially equivalent to a legally marketed predicate, with supporting safety and performance information. | Use is authorized only within the cleared labeling and conditions. |
| De Novo classified | The FDA creates a new device classification for certain novel devices without a suitable predicate when general or special controls can provide reasonable assurance of safety and effectiveness. | The device may be novel, but the classification still defines its intended use and controls. |
| PMA approved | The FDA independently determines that a high-risk device is safe and effective based on the submitted evidence. | “Approved” is a different, generally more demanding pathway than 510(k) clearance. |
In ordinary conversation, people sometimes use “FDA-approved” for any FDA-regulated product. For accuracy, use “cleared” for a 510(k) device and reserve “approved” for a PMA device or another authorization that actually uses that term.
What current AI breast-cancer examples claim
There is no single product called an “FDA-cleared AI recurrence test.” At least two 2026 announcements describe different devices, data types and endpoints.
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| Device | Cleared population and use | Inputs | Endpoint and output | Validation information reported by the company | Turnaround and clinical utility |
|---|---|---|---|---|---|
| TumorSight Risk | Adult women with HR-positive, HER2-negative stage I–IIIA breast cancer; a prognostic indicator to be considered with other clinicopathologic factors. | Routinely collected breast MRI, pathology and clinical data analyzed by cloud software. | Recurrence-free survival at five and ten years; a 0–100 score plus low, intermediate or high categories. | SimBioSys reported a blinded, prospectively designed retrospective, multi-institutional validation of 2,129 patients from Mayo Clinic, Emory University, Stanford Medicine and Mercy Medical Cedar Rapids. | Not stated in the announcement; evidence that using the score changes treatment or improves outcomes was not established. |
| ArteraAI Breast | Early-stage HR-positive, HER2-negative invasive breast cancer; stratifies distant-metastasis risk. | Digitized histopathology images and clinical variables. | Low- or high-risk distant-metastasis classification. | Validation cohort size and design were not stated in the cited announcement. | Not stated; clinical utility beyond risk stratification was not established in the announcement. |
TumorSight Risk: how to read the reported numbers
SimBioSys announced 510(k) clearance of TumorSight Risk on October 15, 2026. The company reported that its high-risk group had a 3.3-fold greater recurrence risk than its low-risk group. It also reported that each 10-point increase in the 0–100 score was associated with a 22% relative increase in recurrence risk.
The company’s estimated five-year recurrence risk ranged from 2.2% for scores of 0–10 to 13.6% for scores of 58–100. Its estimated ten-year risk ranged from 6.2% to 34.2% across those reported score groups. These are company-reported validation findings, not guarantees for an individual patient. A relative increase is not the same as an absolute increase: a 22% relative change applied to a low baseline risk produces a different number of additional events than the same relative change applied to a high baseline risk.
The score is prognostic. It estimates how recurrence-free survival differed among groups in the validation data; it does not diagnose a recurrence, prove that recurrence will happen, or show that choosing a particular chemotherapy, endocrine therapy or radiation plan because of the score will improve an individual’s outcome.
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ArteraAI Breast: a different question and a different specimen
Artera announced FDA clearance of ArteraAI Breast on May 6, 2026. The company describes a system that combines digitized pathology images with clinical variables to classify distant-metastasis risk in early-stage HR-positive, HER2-negative invasive breast cancer.
“Distant metastasis” is not synonymous with every form of recurrence, and pathology-image input is not the same as MRI input. Consequently, a result from ArteraAI Breast cannot be combined numerically with a TumorSight Risk score, and neither announcement establishes that one device is superior for every patient or decision.
What clearance does not establish
- It is not a guarantee. A low-risk category lowers estimated risk relative to a higher-risk category in the studied population; it does not make recurrence impossible.
- It is not a diagnosis. Risk prediction is different from detecting cancer or proving that a tumor has returned.
- It is not a treatment recommendation by itself. The label may call for use alongside clinicopathologic information. Stage, tumor grade, lymph-node status, biomarkers, treatment received, age, other illnesses and patient preferences still matter.
- It is not proof of clinical utility. Clearance and prognostic discrimination do not automatically show that acting on the result changes care in a way that improves survival, quality of life or other patient outcomes.
- It is not interchangeable across populations or endpoints. A model validated in HR-positive, HER2-negative stage I–IIIA disease should not be assumed to apply to another receptor status, stage, sex or treatment setting.
The FDA reported that it had authorized more than 1,600 AI-enabled medical devices for U.S. marketing as of September 2026. That broad total includes many specialties and device purposes; it is not a count of breast-cancer recurrence tests and says nothing by itself about the performance of a particular product.
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How AI recurrence tools compare with genomic assays
Established genomic assays analyze gene activity in tumor tissue. The National Cancer Institute describes Oncotype DX (21 genes), MammaPrint (70 genes) and Breast Cancer Index (11 genes) as tests that, in specified populations and decision contexts, may help estimate spread or recurrence and inform treatment.
FDA product classification also describes a class II 510(k) gene-expression system for distant-recurrence risk in stage I or II, node-negative tumors smaller than 5 cm, used by physicians with other clinical factors. That description is an example of how narrowly a cleared indication can be written; it is not permission to generalize the test to every breast cancer.
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|---|---|---|
| Primary input | MRI, digitized histopathology, clinical variables, or a defined combination. | Gene-expression measurements from tumor tissue. |
| Typical endpoint | May be recurrence-free survival or distant-metastasis risk, depending on the device. | Recurrence, distant spread or treatment-benefit context defined by the assay and its labeling. |
| Population | Must match the specific cleared indication, such as HR-positive, HER2-negative stage I–IIIA disease. | Each assay has its own stage, node, receptor and treatment-context criteria. |
| Output | A numerical score and risk categories, or a low/high classification. | An assay-specific score or risk category tied to the assay’s evidence. |
| Validation to examine | Imaging or pathology quality, institutions, cohort design, calibration and endpoint definition. | Specimen handling, gene panel, population, prospective or retrospective design and treatment decision studied. |
| Clinical utility | Not established solely by FDA clearance; ask whether outcome-improving use has been demonstrated. | Depends on the specific assay and decision; predictive evidence is not identical to prognostic evidence. |
The practical choice is not “AI versus genes” in the abstract. It is which validated test, if any, addresses the patient’s actual decision, disease features and available specimen, and whether its result is likely to change management.
How a patient should use an AI risk result
- Confirm the exact device and indication. Ask for the product name, the cleared population, the endpoint and whether your stage, receptor status, sex and treatment setting match.
- Verify the inputs. Find out whether the result used MRI, pathology images, clinical variables or a combination, and whether the images or tissue met the required quality criteria.
- Request absolute risks and time horizons. Ask what the reported percentage means at five and ten years, which validation population supplied the estimate, and whether the number is an absolute risk or a relative comparison.
- Place the score beside standard factors. Review tumor size, grade, lymph nodes, receptor and HER2 results, genomic information if available, treatments already given and other health conditions.
- Ask what decision would change. A useful result should answer a defined question, such as whether additional therapy is worth discussing. If no treatment or follow-up option would differ, the score may add information without adding a practical benefit.
- Document uncertainty. Ask how missing data, borderline categories, image artifacts or a result outside the validated population affect confidence, and whether a second pathology or oncology review is appropriate.
Questions to take to the oncology visit
- What is the device’s full FDA-cleared intended use, and do I meet every part of it?
- Is this result estimating recurrence-free survival, distant metastasis or another endpoint?
- What are my absolute five- and ten-year risks, not just my category or relative risk?
- How closely did the validation patients resemble me in stage, age, receptor status, grade and treatment?
- What evidence shows that changing treatment based on this result improves outcomes?
- Would an established genomic assay address the same decision, or a different one?
- Could this score conflict with my pathology, imaging or clinical findings, and how would we resolve that conflict?
- What are the consequences of acting as though the result were wrong in either direction?
Common interpretation errors
“Cleared” means “approved for everyone”
It does not. The label’s population and workflow are part of the authorization. Using a cleared device outside those boundaries may be clinically unvalidated even if the software technically accepts the data.
A high score predicts an inevitable recurrence
It does not. Risk groups describe different probabilities in a studied cohort. They do not determine an individual fate.
A low score means treatment can be skipped
It does not. A prognostic estimate alone does not demonstrate that omitting chemotherapy, endocrine therapy, radiation or surveillance is safe for a particular patient.
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Different endpoints are treated as the same
Recurrence-free survival, any recurrence and distant metastasis answer different questions. Always identify the endpoint before comparing products or percentages.
More decimal places mean more certainty
A 0–100 score can look precise even when the underlying estimate has uncertainty and depends on calibration in a particular population. The category, confidence limits when available and applicability to the individual matter more than visual precision.
The practical bottom line
FDA clearance is meaningful authorization for a named device’s labeled use, supported by a regulatory review of safety and performance. For a 510(k) device, the central standard is substantial equivalence to a legally marketed predicate—not an independent promise that the AI improves survival. TumorSight Risk and ArteraAI Breast illustrate why the exact indication matters: they use different inputs and address different outcomes. Treat any score as one piece of a multidisciplinary risk assessment, compare it with appropriate genomic or clinical information, and make treatment decisions with the oncology team rather than from the clearance label alone.
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