SHERPA research consortium initiates seven clinical studies to validate AI-based assistive technologies for minimally invasive brain and cancer treatments
PhilipsPhilips(US:PHG) Globenewswire·2026-03-03 09:00

Core Insights - Royal Philips has initiated the SHERPA research consortium to conduct seven clinical studies focused on AI- and robotics-assisted workflows for minimally invasive treatments of brain aneurysms and liver tumors, addressing the challenges posed by staff shortages and the complexity of procedures [2][5][8]. Group 1: Project Overview - The SHERPA project is a four-year initiative with a total budget of EUR 21.5 million, co-funded by the European Union Innovative Health Initiative and industry partners, involving 16 partners from seven European countries [3][6]. - The project aims to develop AI-powered technologies for imaging, data visualization, procedure planning, clinical decision support, and patient pathway orchestration to ease the workload of interventional (neuro)radiologists [2][8]. Group 2: Clinical Studies and Technologies - The clinical studies will focus on AI-driven aneurysm detection, risk prediction, and precise treatment planning for brain aneurysms, as well as advanced imaging and robotic-assisted biopsy technologies for liver and lung tumors [10][13]. - During the first year, the consortium successfully developed AI algorithms for identifying brain aneurysms needing treatment and optimizing patient selection for liver tumor ablation, along with robotic technology to enhance procedure precision [9][10]. Group 3: Industry Context and Challenges - The World Health Organization predicts a shortage of 600,000 physicians in the European Union by 2030, with significant shortages in interventional radiology, highlighting the need for innovative solutions to improve patient access and relieve pressure on healthcare professionals [5][6]. - The demand for image-guided, minimally invasive procedures is outpacing the growth of the specialized workforce, necessitating the integration of technology to support interventional radiologists in managing complex tasks while maintaining patient care [4][5].