Medical AI company
Mindpeak
Hamburg-based developer of AI for tissue analysis in cancer diagnostics and drug development, with CE-IVD pathology products.
Company overview
Mindpeak develops AI software that turns tissue images into quantitative data for cancer diagnostics and biomarker discovery. It serves diagnostic laboratories and biopharma through the Mindpeak Intelligence Platform and product lines including peakDx, peakDiscover, peakPredict and peakDeploy. The company has a US office in Cambridge, Massachusetts.
- Headquarters
- Hamburg, Germany
- Founded
- 2018 source
- Company type
- AI Technology Developer
In its own words: “Mindpeak brings together engineers, pathologists, and clinical experts with one goal: advancing precision in cancer diagnostics.” source
AI products and platforms
- Mindpeak Intelligence PlatformAI-powered
AI platform for tissue-based biomarker analysis in pathology.
Product page ↗ - peakDxAI-powered
AI product line for diagnostic laboratories.
Product page ↗
Technology categories
Clinical applications
- IHC biomarker quantification
- AI biomarker discovery
Regulatory information
- 11 CE-IVD products; IVDR certification announced (per company site) source
As stated by the company or the cited regulator page. Authorisation is specific to a product, version and market; confirm the current status with the company and the regulator. About medical device regulation
Official sources
Last verified 29 October 2026 against the sources above.
Questions about Mindpeak
What are whole-slide images in digital pathology?
A whole-slide image (WSI) is a high-resolution digital scan of an entire glass pathology slide, which can be viewed and navigated on screen at different magnifications, much like a microscope.
WSIs are very large files, often gigabytes per slide, so digital pathology needs fast scanners, substantial storage, network capacity and viewing software. They are the input for pathology AI.
FDA describes a whole slide imaging system as an automated digital slide creation, viewing and management system intended as an aid to the pathologist to review and interpret digital images of surgical pathology slides. Formats vary between scanner vendors, and DICOM has been extended to support whole-slide imaging, which helps interoperability between scanners, archives and AI tools.
What is AI image reconstruction and denoising?
AI image reconstruction and denoising use deep learning to produce clearer images from raw scanner data or to reduce noise and artifacts, for example in CT or MRI. Vendors often position these tools as supporting lower-dose or faster scanning protocols.
Unlike detection software, these tools change the image the clinician reads rather than adding a finding. Questions to ask include:
- Is the function built into the scanner or a separate software product?
- Which scanners, protocols and body regions is it authorized for?
- How was diagnostic image quality evaluated, and could fine detail be lost or altered?
These functions are typically regulated as part of the imaging device or as image processing software, depending on the product and market.
What is AI-powered medical image analysis?
AI-powered medical image analysis uses machine learning, often deep learning, to process images and return outputs such as detected regions, segmentations, measurements, quantitative scores or classifications.
It is used beyond radiology, including in cardiology (echocardiography), pathology (whole-slide images), dentistry (intraoral and panoramic radiographs), ophthalmology (retinal photographs and OCT) and dermatology. Outputs may be shown as overlays, structured results or notifications.
Because the software analyzes medical images, in the US it does not meet the criteria for non-device clinical decision support, and image analysis with a medical purpose is commonly regulated as a device. The Medical Imaging AI category in the Medical AI directory covers these companies.