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Infervision

by Infervision  ·  founded 2016

Last verified

AI for CT lung nodules, CT stroke, surgical planning and RECIST follow-up, with FDA clearances for these CT products.

Visit Infervision (opens in new tab)Read our methodology

At a glance

Pricing
No public price.
HIPAA
Not disclosed
SOC 2
Not disclosed
EHRs
—
Founded
2016
On this page

Independent score  ·  By our public rubric

57/100Solid choice

Editorial rating 4.0 / 5

How it’s computed →
  • Regulatory & Compliance
    18/28

    FDA cleared (6 K-numbers on record)

  • Clinical Integration
    0/6.6

    No EHR integrations listed

  • Evidence Strength
    12.7/26

    4 peer-reviewed papers

  • Vendor & Market
    14.4/18

    Mid-tier funding and adoption

  • Sentiment & Transparency
    2.5/5

    Pricing on request, no public amounts

▸ Show all 11 dimensions

Regulatory & Compliance

  • FDA clearance18/18

    FDA cleared (6 K-numbers on record)

  • HIPAA / SOC2 / BAA0/10

    No public HIPAA/SOC2/BAA attestation

Clinical Integration

  • EHR integrations (count)0/4

    No EHR integrations listed

  • Top-3 EHR coverage (Epic / Oracle / Athena)0/2

    None of the top-3 EHRs covered

Evidence Strength

  • Peer-reviewed papers13/18

    4 peer-reviewed papers

  • RCT / meta-analysis / systematic review0/8

    No RCT, meta-analysis, or systematic review

Vendor & Market

  • Funding & adoption signal8/12

    Mid-tier funding and adoption

  • Years in market6/6

    Founded 2016 (10 years)

Sentiment & Transparency

  • Pricing transparency3/5

    Pricing on request, no public amounts

Scored November 2, 2026 · Public scoring method v1.2.0

AI for CT lung nodules, CT stroke, surgical planning and RECIST follow-up, with FDA clearances for these CT products.

No public price.

Evidence record

FDA decisions
Show 1 more
Peer-reviewed

4 studies on PubMed: 4 other, published 2022 to 2026. Read the literature summary

Verification
  • Score computed November 2, 2026, method 1.2.0
  • Review written November 2, 2026
Cite this record

MedAI Verdict, Infervision record, https://medaiverdict.com/tools/infervision, version November 2, 2026

Change history
View every change to this record

Bottom line

Infervision sells InferRead for CT lung nodules and CT stroke triage, InferOperate Suite for 3D surgical planning and InferCare for RECIST follow-up. FDA's records list six clearances, from InferRead Lung CT.AI in July 2020 (K192880) to a second InferOperate Suite entry in July 2026 (K260973). The chest X-ray product, InferRead DR Chest, has no FDA record under Infervision's applicant names. A hospital building a CT lung nodule workflow or a thoracic surgery planning service fits the cleared range. Chest X-ray AI under a US clearance is not part of what the record offers.

What do the CT products do?

Infervision says InferRead CT Lung detects pulmonary nodules (pure ground glass, solid, mixed and suspected tumor), analyses lobe density and volume, registers follow-up scans with volume doubling time and produces a structured report in a browser viewer. Quantification of nodule growth, emphysema and coronary calcium is marked "For investigational use only" on the product page. The vendor claims 99% sensitivity with AI assisted reading, citing a linked publication, and a 33% cut in reading time from a talk by Wake Radiology.

Infervision says InferRead CT Stroke triages hemorrhage on head CT. FDA lists InferRead CT Stroke.AI (K211179, August 2021) as triage and notification. The Lung CT page shows FDA, NMPA and UKCA badges and no CE mark. The vendor calls the products "PACS and RIS friendly", on premise or in the cloud.

What is known about the chest X-ray product?

A 2025 PLOS Global Public Health study (PMID 40531944) by independent academic authors evaluated InferVision CAD, the InferRead DR Chest product, for tuberculosis screening in Lusaka, Zambia. At a cutoff of 0.12, sensitivity was 90.3% (86.3 to 93.5) and specificity 39.2% (36.8 to 41.7), with an AUC of 0.81. FDA has no entry for it under Infervision's applicant names.

Who is the company and what does it charge?

Infervision says it was founded in 2016, and its company page does not state a headquarters. The vendor claims deployments including Wake Radiology, Sanford Health research and TB screening in Kyrgyzstan and Zimbabwe. It publishes no price, and the site lists a contact address for enquiries.

The verdict

Thoracic surgery teams get planning software with two InferOperate Suite entries, and CT lung screening programs get nodule detection with clearances going back to July 2020. For chest X-ray AI the picture is the Zambia tuberculosis study (PMID 40531944) and no FDA record under Infervision's applicant names.

Editorial review last generated November 2, 2026. Synthesized from clinician sentiment, peer-reviewed coverage, and our editorial silo picks. Refined by hand where vendor facts change.

What it costs

All tiers are custom-quoted. The vendor does not publish per-provider list pricing.

TierMonthlyAnnualNotes
EnterpriseNot statedNot statedNo public price.

Source: vendor pricing page. Last checked November 2, 2026.

What deploys cleanly

Carries FDA 510(k) per vendor documentation. Independent attestation review is the buyer's responsibility before clinical deployment.

Who builds it

Infervision (Infervision) was founded in 2016, putting it 10 years into market.

What the literature says

4 peer-reviewed studies indexed on PubMed evaluate Infervision in clinical contexts. The most relevant are shown below, ranked by editorial relevance score combining title match, study design, recency, and journal tier.

Spatial classification of clinical T1a-bN0 non-small cell lung cancer in superior segment based on three-dimensional reconstruction: optimizing surgical margin and operative strategy. (opens in new tab)
Yang Y, Ren Q, Wang Z, et al.· J Thorac Dis· 2026
In segmentectomies of the superior segment (S) for early-stage non-small cell lung cancer (NSCLC), inadequate surgical margins remain a critical challenge. This study aims to establish a three-dimensional (3D) spatial classification system for Stumors to analyze margin adequacy and optimize surgical planning. This retrospective cohort study included 699 patients with clinical T1a-bN0 SNSCLC who underwent thoracoscopic surgery between 2019 and 2023. Tumors were classified into three categories using the "InFerVision" software. S-I had a margin sphere located above the intersegmental plane. S-I…
AI-assisted clinico-quantitative imaging nomogram for preoperative malignancy risk in solid and part-solid pulmonary nodules ≤ 3 cm: development and internal validation. (opens in new tab)
Ruan Y, Long C, Cao W, et al.· Front Oncol· 2026
To develop and internally validate an artificial intelligence (AI)-assisted clinico-quantitative imaging prediction model that combines automatically extracted quantitative imaging features with clinical data to preoperatively assess individualized malignancy risk in solid and part-solid pulmonary nodules (PNs) measuring ≤ 3 cm. This retrospective study analyzed data from 951 consecutive patients who underwent surgical resection for PNs ≤ 3 cm (210 benign, 741 malignant). Quantitative CT features, including nodule size, minimum and maximum computed tomography attenuati…
Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence. (opens in new tab)
Schwyzer M, Messerli M, Eberhard M, et al.· Diagn Interv Imaging· 2022
The purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm. A chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fu…
Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study. (opens in new tab)
Meng Q, Yu P, Yin S, et al.· Quant Imaging Med Surg· 2023
Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. This multicenter study consisted of an internal and external cohort of pati…

See all on PubMed (opens in new tab)

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