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Explore how Sycai Medical is advancing early abdominal cancer detection using artificial intelligence. In this Clinical AI Spotlight, CEO Sara Toledano explains how opportunistic screening analyses routine radiology scans to identify early lesions in the pancreas, liver, and kidneys, helping clinicians detect cancer sooner and improve patient outcomes.

Early detection remains one of the biggest challenges in oncology, particularly for pancreatic cancer, which is often diagnosed at advanced stages. At Sycai Medical, artificial intelligence is being used to address this challenge by identifying early signs of disease in routine radiology scans.

In a recent interview done by Cátedra CAMELIA (Plexus en Inteligencia Artificial aplicada a la Medicina Personalizada de Precisión), based at CiTIUS, University of Santiago de Compostela. Sara Toledano, CEO and co-founder of Sycai Medical, shared how the company’s technology works and why opportunistic screening could significantly improve cancer detection.

Key Highlights

• Sycai Medical is developing AI technology for early detection of abdominal cancers, particularly pancreatic, liver and kidney cancer.
• The platform uses opportunistic screening, analysing routine radiology scans even when they were taken for unrelated reasons.
• The AI detects lesions using deep learning and compares previous scans to analyse lesion evolution over time.
• The system can flag abnormalities that radiologists may not be actively searching for, helping identify cancers earlier.
• Sycai already has CE certification for pancreas, enabling commercialisation in Europe.

Opportunistic Screening for Abdominal Cancer

Sycai Medical has developed an AI solution designed to detect lesions in abdominal imaging even when the scan was performed for unrelated reasons.

 Sara explains:
“What we have created at Sycai is an opportunistic screening system to improve the early diagnosis of abdominal cancer in general and pancreatic cancer in particular.”

The technology processes medical images in several stages. First, the software normalises radiology images to ensure consistency across different types of scans. A deep learning algorithm then identifies and characterises potential lesions, while an additional layer of analysis retrieves previous scans of the patient to evaluate how lesions may have evolved over time.

Detecting What No One Is Looking For

One of the most significant aspects of Sycai’s technology is its ability to identify abnormalities that might otherwise remain unnoticed.

Sara highlights:
“We are not helping the radiologist search for cancer when they are already looking for it. We detect it when nobody is looking.”

For example, a CT scan performed for a completely unrelated reason may still contain early signs of cancer in another organ. Sycai’s system flags these findings, alerting clinicians to lesions that were not expected.

“A scan might be performed for something unrelated, and Sycai can say: Look, there is a lesion here that nobody was expecting or searching for.’”

Supporting Radiologists Through AI

Rather than replacing clinicians, the technology acts as an additional analytical layer that supports radiologists in their workflow.

By identifying suspicious lesions and analysing their progression across previous scans, the system can help improve diagnostic accuracy, efficiency, and early detection.

The Future of AI in Radiology

Artificial intelligence is rapidly transforming medical imaging. While large companies are investing heavily in AI technologies, smaller startups often move faster when developing specialised solutions.

Sara notes that the future of radiology will likely involve integrated AI platforms capable of analysing multiple organs and conditions within a single system, rather than separate tools for each use case.

Driving Innovation in Healthcare

Sycai Medical reached a major milestone with CE certification, enabling its AI technology for pancreas to be commercialised across Europe. While adoption in healthcare can be gradual, the company has already collaborated with hospitals, radiologists, and clinical experts during validation phases to demonstrate the potential of its solution in real clinical environments.

As Sara Toledano explains,

“Innovation in medicine requires validation, funding, data, and doctors who are willing to adopt and test new technologies.” 

With continued collaboration between clinicians, researchers, and industry partners, solutions like those developed by Sycai Medical have the potential to significantly improve early cancer detection and patient outcomes.