Visiopharm

At a glance

Pricing

Enterprise per-module.

HIPAA

Not disclosed

SOC 2

Not disclosed

EHRs

—

Founded

—

HQ

DK

Bottom line

FDA-cleared IHC tissue analysis (Ki67, ER/PR/HER2).

Free tier available.

Overview

Pricing

What it costs

Free tier only; no paid plans publicly disclosed.

Source: vendor pricing page. Verified May 23, 2026.

Compliance + integration

What deploys cleanly

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

Peer-reviewed coverage

What the literature says

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

Inter-rater agreement of HER2-low scores between expert breast pathologists and the Visiopharm digital image analysis application (HER2 APP, CE2797).

Parry S, Zabaglo L, Shaaban AM, et al.· J Pathol Clin Res· 2025Observational

Inter-observer concordance data for the HER2 category as assessed by a group of 16 specialist breast pathologists on 50 diagnostic core biopsies was compared with that produced by digital image analysis (DIA) using the HER2 APP, CE2797 (VP APP; Visiopharm, Hoersholm, Denmark). Comparing pathologists' consensus scores and DIA scores, 36 cases (73.5%) agreed. Fleiss' kappa statistic was 0.433 (indicative of moderate agreement). Cohen's weighted kappa was used to compare the scores of individual raters to consensus scores; for all 50 cases the kappa scores had a range between 0.412 and 0.854; th…

Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial.

van Dooijeweert C, Flach RN, Ter Hoeve ND, et al.· Nat Cancer· 2024RCT

Pathologists' assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and a…

Head-to-Head Comparison of 2 Artificial Intelligence Tools for Detecting Lymph Node Metastases in Whole-Slide Pathology Images Within and Beyond Their Intended Use.

Flach RN, Samuels M, Ter Hoeve ND, et al.· Mod Pathol· 2025Observational

The increasing diagnostic workload in pathology, driven by rising cancer incidences, highlights the need for scalable, cost effective solutions. Artificial intelligence (AI) has shown promise in supporting lymph node (LN) metastasis detection, a key prognostic factor in cancer staging. However, the current Conformité Européene In Vitro Diagnostics--certified AI tools are often limited to specific tumor types, reducing their cost efficiency and clinical use. This study evaluates the performance of 2 Conformité Européene In Vitro Diagnostics-certified AI tools-Visiopharm Met…

AI for pathologists: a universal lymph node metastasis detection app that enhances efficiency while preserving diagnostic accuracy.

Vazzano J, Challa B, Arole V, et al.· J Pathol Clin Res· 2026

Increasing workload combined with the shortage of pathologists is the leading cause of diagnostic errors and delays. Nonetheless, in clinical practice, pathologists often spend hours on tedious tasks such as counting mitoses and searching for lymph node micro-metastasis, which may yield unreliable results. The advent of digital pathology and the development of artificial intelligence (AI) applications (app) for image analysis have opened new possibilities for improving the efficiency and accuracy of pathologists. However, the perceived black box nature of AI has led to skepticism among many p…

Implementation of artificial intelligence to automate physical disector in a fractionator design for quantification of stem cell-derived neurons.

Overgaard A, Molnár K, Jurtz VI, et al.· Comput Biol Med· 2026

Accurate quantification of stem cell-derived dopaminergic neurons is essential for advancing cell therapy strategies in Parkinson's disease (PD). Traditional manual stereological methods, while robust, are time-consuming and subject to interobserver variability, limiting their scalability for preclinical and translational studies. This study presents the development and validation of an artificial intelligence (AI)-assisted physical fractionator workflow for unbiased and efficient quantification of human embryonic stem cell (hESC)-derived ventral midbrain dopaminergic (vmDA) neurons in a Park…

See all on PubMed