Medi-Globe Launches mAI  Companion AI Assistant for Pancreatic  EUS

Key Takeaways

• First CE‑marked AI assistant for pancreatic EUS in Europe
• Real‑time lesion detection boosts diagnostic confidence and reduces missed lesions
• Trained on 5 million annotated images from hundreds of EUS videos
• Randomised trial with 57 endoscopists shows higher accuracy using AI
• Early detection improves treatment options and patient survival rates

Pulse Analysis

Pancreatic cancer remains one of the deadliest malignancies, with five‑year survival rates below 10 percent largely because the disease is diagnosed at an advanced stage. Endoscopic ultrasound (EUS) offers the highest resolution imaging for pancreatic assessment, yet its diagnostic yield depends heavily on operator experience and can miss subtle lesions. As healthcare systems worldwide grapple with early‑detection imperatives, integrating decision‑support technologies into EUS workflows has become a strategic priority for hospitals seeking to improve outcomes while managing specialist shortages.

Medi‑Globe’s mAI Companion addresses this gap by delivering real‑time, AI‑powered analysis of the head, body and tail of the pancreas during live procedures. Built on a dataset of more than five million expert‑annotated frames from hundreds of patient videos, the algorithm highlights both solid and cystic abnormalities, effectively serving as a digital second opinion. In a peer‑reviewed, randomized study with 57 endoscopists, the system raised diagnostic accuracy and cut missed‑lesion rates, leading to its MDR CE‑Mark certification—the first of its kind for pancreatic EUS in Europe.

The CE‑marked launch positions mAI Companion as a catalyst for broader AI adoption in interventional gastroenterology. Early deployments in top European centers signal confidence in the technology’s workflow integration and regulatory robustness, while the product’s rapid market entry demonstrates a viable pathway for future AI medical devices. As payers increasingly reward early cancer detection, hospitals that embed such intelligent assistants can expect improved quality metrics, stronger case‑mix, and a competitive edge in attracting referrals. Ultimately, the solution exemplifies how data‑driven tools can transform complex procedures into more consistent, life‑saving interventions.