MERAI Lab: MeVis and Radboudumc ICAI Lab

Background

Healthcare costs are globally rising. The workload of radiology departments has substantially increased and is still increasing, and, as a result, radiologists are under large pressure and risk of burn-out. The imminent implementation of lung cancer screening and the rapid increase in the availability of novel cancer treatments such as immunotherapy will result in a continued increase of imaging.

Mission and aims

MERAI Lab is a collaboration between Radboudumc and MeVis Medical Solutions with the mission to create world-leading AI-supported software solutions for healthcare. We aim to create AI solutions in the lung oncology field that will improve the accuracy in the interpretation of the increasing amount of imaging that is performed for screening, optimal treatment selection and treatment monitoring, and reduce the time needed to report these scans. Both combined will improve the cost-effectiveness of our healthcare system. For responsible use of the developed AI algorithms, it is essential that we guarantee robust and trustworthy AI solutions that reach performance close to human experts.

Please also see the MERAI Lab website.

Funding

MERAI Lab is part of the ROBUST AI programme and receives funding from the Dutch Research Council (NWO), the Dutch Ministry of Economics and Climate and MeVis Medical Solutions AG.

News

People

Bogdan Obreja

Bogdan Obreja

PhD Candidate

Michel Vitale

Michel Vitale

PhD Candidate

Lisa Klok

Lisa Klok

PhD Candidate

Shaurya Gaur

Shaurya Gaur

Master Student

Kristina Avramova

Kristina Avramova

Bachelor Student

Colin Jacobs

Colin Jacobs

Associate Professor

Marianne Boenink

Marianne Boenink

Professor

Radboudumc

Mira Vegter

Mira Vegter

Assistant Professor

Radboudumc

Publications

  • M. Vitale, M. Boenink, M. Vegter and C. Jacobs, "Norms for Responsible AI-enabled Population Screening", European Society for Philosophy of Medicine and Healthcare, 2024.
  • F. van der Graaf, N. Antonissen, E. Scholten, M. Prokop and C. Jacobs, "Assessing the agreement between privacy-preserving Llama model and human experts when labelling radiology reports for specific significant incidental findings in lung cancer screening", Annual Meeting of the European Society of Thoracic Imaging, 2024.
  • B. Obreja, K. Venkadesh, W. Hendrix, Z. Saghir, M. Prokop and C. Jacobs, "Deep Learning for estimating pulmonary nodule malignancy risk: How much data does AI need to reach radiologist level performance?", European Congress of Radiology, 2024.
  • F. van der Graaf, N. Antonissen, Z. Saghir, M. Prokop and C. Jacobs, "External validation of the Sybil risk model as a tool to identify low-risk individuals eligible for biennial lung cancer screening", European Congress of Radiology, 2024.