
Medical AI company
DeepTek
Pune-based radiology AI company offering chest X-ray AI for TB screening, the Augmento deployment platform and AI-assisted teleradiology.
Company overview
DeepTek develops medical imaging AI and an AI deployment platform for radiology. Its chest X-ray AI is offered for public-health TB screening and clinical use, and Augmento Enterprise is its platform for deploying AI in radiology workflows. The company also provides AI-driven diagnostic reporting and teleradiology services.
- Headquarters
- Pune, India
- Founded
- 2017 source
- Company type
- AI Technology Developer, Platform Provider
In its own words: “DeepTek - AI radiology company in India offers radiology AI marketplace of solutions & services to make quality diagnostics affordable & accessible globally” source
AI products and platforms
- DeepTek Chest X-ray AI (CXR Analyzer)AI-powered
AI analysis of chest X-rays for TB screening and clinical findings
Product page ↗ - Augmento EnterpriseAI-powered
AI deployment platform for radiology workflow
Product page ↗
Technology categories
Clinical applications
- TB screening
- radiology reporting
- teleradiology
Markets served
- India
Relationships
Relationships are shown as stated on the cited page; the other party may not confirm them.
Regulatory information
- Chest X-ray AI solution received CE certification under EU MDR as a Class IIb medical device source
- Augmento receives US FDA clearance 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 DeepTek
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 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 triage or worklist prioritization in radiology?
AI triage software analyzes incoming imaging studies and flags those that may contain a time-sensitive finding so clinicians can review them sooner, for example by moving a study up the worklist or sending a notification.
In the US, FDA has a device classification for radiological computer-aided triage and notification software (21 CFR 892.2080). As described in that regulation, such a device notifies clinicians of time-sensitive images, does not mark the finding on the original image, does not remove cases from the reading queue, and operates in parallel with the standard of care.
Triage is therefore not a diagnosis: every study still needs to be read. When evaluating triage tools, ask about the target findings, notification workflow and how missed cases are handled.