Medical AI company
Amplifai Health
KAUST-backed Saudi startup developing TFscan, AI-powered thermal imaging for diabetic foot risk screening.
Company overview
Amplifai Health combines thermal imaging with AI analysis. Its TFscan product supports diabetic foot screening by identifying temperature patterns and producing explainable reports with a risk score aligned to IWGDF guidelines, alongside a clinical portal for patient management. The company also offers NUR, a thermal-imaging product for sports, recovery and wellbeing.
- Headquarters
- Saudi Arabia
- Company type
- AI Technology Developer, Research or Emerging Startup
- Status
- Startup
In its own words: “Our AI-powered thermal imaging enables fast, accessible and objective diabetic foot screening, so any care team can act early, with confidence.” source
AI products and platforms
- TFscanAI-powered
AI-powered thermal imaging application and clinical portal for diabetic foot risk screening
Product page ↗
Technology categories
Clinical applications
- diabetic foot screening
- risk scoring and reporting
Markets served
- Saudi Arabia
Relationships
- Partner KAUST (King Abdullah University of Science and Technology) source
Relationships are shown as stated on the cited page; the other party may not confirm them.
Official sources
- amplifaihealth.com
- amplifaihealth.com/tfscan
- thelens.kaust.edu.sa/amplifai-health-secures-sfda-approval-for-ai-powered-diabetic-foot-screening
Last verified 29 October 2026 against the sources above.
Questions about Amplifai Health
Does Medical AI have to explain how it reaches its outputs?
Not always in a technical sense, but transparency about the product is increasingly expected. Many machine learning models cannot give a simple step-by-step reason for each output, so regulators focus on clear information for users: intended use, inputs, how the model was developed and tested, performance, and known limitations.
In the US, one of the criteria for clinical decision support to fall outside device regulation is that a healthcare professional can independently review the basis for its recommendations. FDA has also published transparency principles for machine learning devices with Health Canada and MHRA.
For buyers, a vendor's ability to document how its model works and where it fails is often more useful than heat maps or confidence scores alone.
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.