Maastricht University

Clinical research professionals are increasingly using AI for drug discovery, data analysis, and patient care, such as AI tools for stroke diagnosis and treatment, machine learning models for trial data interpretation, and AI-driven drug development processes.

Authors Daniel E. Coral, Femke Smit, and Ali Farzaneh contributed equally. Affiliations include Lund University Diabetes Centre, Maastricht University, Erasmus MC University Medical Center, University Medical Center Mainz, Novo Nordisk A/S, Université de Lille, George Washington University, Vrije Universiteit Brussel, Institut d’Investigació Biomèdica de Girona, University of Girona, CIBER Fisiopatología de la Obesidad y Nutrición, Dr. Josep Trueta University Hospital, Harvard T.H. Chan School of Public Health, University of Exeter, and University of Dundee.

The 32nd Semmelweis Symposium, focusing on network medicine, featured 35 global speakers discussing network science, drug repurposing, and AI in healthcare. Keynotes highlighted the potential of network medicine to revolutionize disease prevention, diagnosis, and treatment.

Attachment promoting compounds (APCs) like vitronectin and laminin significantly enhance bovine satellite cell (bSC) proliferation and purity on microcarriers (MCs) in serum-free media. Cytodex 1, Synthemax II, and CellBIND MCs supported bSC growth, with Cytodex 1 showing the best results. Laminin allowed bSC attachment and growth on previously unsuccessful Plastic MCs. Vitronectin optimized for sustainability can be used solely as a coating on Cytodex 1 MCs, enhancing cell attachment and proliferation.

Radiomics in CRC precision medicine: 2013-2023 bibliometric analysis shows China leads in publication volume, U.S. in citations. Key themes include deep learning, texture analysis, and AI integration for improved CRC management.

UAB’s Hugh Kaul Precision Medicine Institute and Maastricht University receive ARPA-H award for up to $8,716,812 for Biomedical Data Fabric program, aiming to democratize access to biomedical data science and improve diagnostics/treatment for cancer and rare disorders.

The TRIPOD+AI update provides harmonised guidance for reporting prediction model studies, superseding TRIPOD 2015, to accommodate AI and machine learning methods. The new 27-item checklist aims for complete, accurate, and transparent reporting, facilitating study appraisal and model implementation.

The study by Elise Le Flohic highlights the serious consequences of unrecognized nonadherence in clinical trials, including potential health risks, financial losses, and flawed drug development outputs. Traditional monitoring methods are inadequate, and adherence-informed analysis is crucial for accurate results.