Aiforia

At a glance

Pricing

Enterprise.

HIPAA

Not disclosed

SOC 2

Not disclosed

EHRs

—

Founded

—

HQ

FI

Bottom line

CE-IVDR Gleason grading + multi-organ AI image analysis.

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 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 Aiforia in clinical contexts. The most relevant are shown below, ranked by editorial relevance score combining title match, study design, recency, and journal tier.

A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?

Saft L, Vaara E, Ljung E, et al.· J Hematop· 2025

Bone marrow cytology plays a key role for the diagnosis and classification of hematological disease and is often the first step in the acute setting of unclear cytopenia. AI applications represent a powerful tool in digital image analysis and can improve the diagnostic workflow and accuracy. The aim of this study was to develop an algorithm for the automated detection and classification of hematopoietic cells in digitized bone marrow aspirate smears for potential implementation in the clinical laboratory. The AIFORIA create platform (Aiforia Technologies, Plc, Helsinki, Finland) was used to d…

Using artificial intelligence to improve cell therapy assays: automated quantitative image analysis of cells on matrices.

Bornschlegl AM, Dietz AB· Tissue Cell· 2025

As the field of cell therapy continues to advance, the combination of cells and directed delivery methods (such as three-dimensional scaffolds, cell printing etc.) continues to grow. These technologies require methods to accurately determine cell numbers and viability to enhance process optimization and develop appropriate release tests. Current methods have limited dynamic range and require substantial manual effort to produce results. Here we describe a simple fluorescent imaging-based method for counting live and dead cells in scaffold cultures that is consistent, automated, and quantitati…

Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.

Mola N, Weishaupt H, Krasontovitsch V, et al.· Sci Rep· 2025Observational

The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score&#x2009;<&#x2009;5%, eight regions per slide). Performance metr…

A machine learning model of lamina propria fibrosis in eosinophilic esophagitis for prediction of fibrostenotic disease.

Sivasubramaniam P, Shabaan A, Elhalaby R, et al.· J Pathol Inform· 2026

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease that can progress to fibrostenotic complications. Lamina propria fibrosis (LPF) plays a critical role in this progression but is difficult to assess reliably in routine biopsies. We aimed to develop and validate an artificial intelligence (AI) model to quantify LPF on hematoxylin and eosin (H&E)-stained slides and to evaluate its ability to predict fibrostenotic disease. We used a cloud-based platform (Aiforia Inc., Cambridge, MA, USA) to train a supervised AI model to recognize several histological features of EoE, including…

Development of an Artificial Intelligence Model to Aid in Measurement of Invasion, Comprehensive Histologic Subtyping, and Grading of Pulmonary Adenocarcinoma.

Boland JM, Stetzik L, Roden AC, et al.· Mod Pathol· 2026

The World Health Organization classification of pulmonary adenocarcinoma is complex, posing challenges for pathological reporting. Key difficulties include assessing invasive size in lepidic-predominant tumors and performing comprehensive histologic subtyping. Although these evaluations inform tumor stage, grade, and prognosis, they are time consuming and subjective, leading to interobserver variability. Artificial intelligence (AI) may help streamline these tasks and improve consistency. One representative hematoxylin and eosin slide was selected from each of 100 resected pulmonary adenocarc…

See all on PubMed