
Medical AI company
Gleamer
Paris-founded radiology AI company offering the Gleamer Copilot suite for X-ray, CT, mammography and MRI, plus AI reporting tools.
Company overview
Gleamer develops AI products for medical imaging grouped under Gleamer Copilot. Its applications include BoneView for bone trauma on X-ray, ChestView, BoneAge, BreastView, LungCT and MRI tools from its acquisitions of Pixyl and Caerus Medical. It also offers AI reporting products (Voice, AutoReport).
- Headquarters
- Paris, France
- Founded
- 2017 source
- Company type
- AI Technology Developer
In its own words: “Gleamer delivers next-generation AI solutions for medical imaging, empowering radiologists to diagnose faster, with greater accuracy, across every modality.” source
AI products and platforms
- BoneViewAI-powered
AI fracture detection on radiographs
Product page ↗ - ChestViewAI-powered
AI for thoracic pathologies on X-ray
Product page ↗ - Pixyl.Neuro.MSAI-powered
MRI AI for multiple sclerosis (from Pixyl acquisition)
Product page ↗ - AutoReportAI-powered
AI reporting assistant
Product page ↗
Technology categories
Clinical applications
- fracture detection
- radiology reporting
Regulatory information
- BoneView obtained Class IIa CE marking source
- BoneView received FDA 510(k) clearance source
- ChestView and BoneMetrics received FDA 510(k) clearance source
- BoneAge and BoneMetrics are MDR CE-certified 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 Gleamer
How does an AI scribe work?
An AI scribe typically works in four steps:
- Capture: a phone, computer or room microphone records the consultation after the patient is informed or consents.
- Transcription: speech recognition converts audio to text and separates speakers.
- Summarization: a language model extracts relevant content and drafts a note in the chosen format, such as a SOAP note, sometimes with codes or orders suggested.
- Review: the clinician edits and signs the draft, which is then filed in the EHR, often through an integration.
Each step can introduce errors, so final responsibility for the note stays with the clinician. Ask vendors whether audio is stored, whether data is used to train models, and how fabricated or missing content is measured.
How is radiology AI different from general diagnostic imaging software?
General diagnostic imaging software stores, displays and manipulates images, for example PACS viewers, 3D reconstruction or measurement tools operated by the user. Radiology AI adds automated analysis that produces its own output, such as a detection, triage flag or quantitative result.
The distinction matters for evaluation:
- Viewing and processing tools are judged mainly on image quality, usability and integration.
- AI analysis tools also need evidence on accuracy, populations studied, failure modes and monitoring.
Both can be regulated medical devices depending on intended use; in the US many image management and processing functions fall under device classifications. Many vendors now combine the two, so ask which functions are AI-based and which are covered by a regulatory authorization.
What is “hallucination” in generative AI tools used in healthcare?
Hallucination is when a generative AI tool produces content that sounds plausible but is false or unsupported by its inputs, such as a symptom, medication or exam finding that was never mentioned in a consultation.
It matters most in documentation, summarization and question-answering tools, because errors can be copied into the medical record. Common safeguards are:
- Clinician review and sign-off of every generated note or summary.
- Links from generated text back to the source audio or record.
- Vendor testing that measures omission and fabrication rates in realistic conditions.
- Clear policies on what the tool must not be used for.
WHO's guidance on large multi-modal models highlights inaccurate or false outputs as a key risk.