Charting a new course in healthcare: early-stage AI algorithm registration to enhance trust and transparency

Autor: Michel E. van Genderen, Davy van de Sande, Lotty Hooft, Andreas Alois Reis, Alexander D. Cornet, Jacobien H. F. Oosterhoff, Björn J. P. van der Ster, Joost Huiskens, Reggie Townsend, Jasper van Bommel, Diederik Gommers, Jeroen van den Hoven

Publikováno v: npj Digital Medicine, Vol 7, Iss 1, Pp 1-4 (2024)

AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we

Externí odkaz: https://doaj.org/article/3cde30344e134274be59fb84291d5dd8

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Autor: Diederik Gommers, Davy van de Sande, Joost Huiskens, Robert E. R. Veen, Michel E van Genderen, Jim M. Smit, Jacob J. Visser, Edwin van Unen, Oliver Hilgers BA, Jasper van Bommel

Publikováno v: BMJ Health & Care Informatics, Vol 29, Iss 1 (2022)

Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation traj

Externí odkaz: https://doaj.org/article/bfb6a81796cf41298dc5ba7ba0dbf451

Short-Term Outcomes of Secondary Liver Surgery for Initially Unresectable Colorectal Liver Metastases Following Modern Induction Systemic Therapy in the Dutch CAIRO5 Trial

Autor: Karen Bolhuis, MD, Lodi Grosheide, MD, Nina J. Wesdorp, MD, Aysun Komurcu, MSc, Thiery Chapelle, MD, PhD, Cornelis H. C. Dejong, MD, PhD, Michael F. Gerhards, MD, PhD, Dirk J. Grünhagen, MD, PhD, Thomas M. van Gulik, MD, PhD, Joost Huiskens, MD, PhD, Koert P. De Jong, MD, PhD, Geert Kazemier, MD, PhD, Joost M. Klaase, MD, PhD, Mike S. L. Liem, MD, PhD, I. Quintus Molenaar, MD, PhD, Gijs A. Patijn, MD, PhD, Arjen M. Rijken, MD, PhD, Theo M. Ruers, MD, PhD, Cornelis Verhoef, MD, PhD, Johannes H. W. de Wilt, MD, PhD, Cornelis J. A. Punt, MD, PhD, Rutger-Jan Swijnenburg, MD, PhD, for the Dutch Colorectal Cancer Group Liver Expert Panel

Publikováno v: Annals of Surgery Open, Vol 2, Iss 3, p e081 (2021)

Objective:. To present short-term outcomes of liver surgery in patients with initially unresectable colorectal liver metastases (CRLM) downsized by chemotherapy plus targeted agents. Background:. The increase of complex hepatic resections of CRLM, te

Externí odkaz: https://doaj.org/article/5e754ce85cf045fbad154fecb26942d0

Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report

Autor: Diederik Gommers, Davy van de Sande, Michel E. Van Genderen, Joost Huiskens, Robert E. R. Veen, Yvonne Meijerink, Jasper van Bommel

Publikováno v: BMJ Health & Care Informatics, Vol 28, Iss 1 (2021)

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient mon

Externí odkaz: https://doaj.org/article/553c1984cea24f189405a3000932677e

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer

Autor: Boris V. Janssen, Severano Verhoef, Nina J. Wesdorp, Joost Huiskens, Onno J. de Boer, Henk Marquering, Jaap Stoker, Geert Kazemier, Marc G. Besselink

Publikováno v: Janssen, B V, Verhoef, S, Wesdorp, N J, Huiskens, J, de Boer, O J, Marquering, H, Stoker, J, Kazemier, G & Besselink, M G 2022, ' Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer : A Scoping Review ', Annals of Surgery, vol. 275, no. 3, pp. 560-567 . https://doi.org/10.1097/SLA.0000000000005349
Annals of Surgery, 275(3), 560-567. LIPPINCOTT WILLIAMS & WILKINS

OBJECTIVE: To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC. SUMMARY OF BACKGROUND DATA: Patients with PDAC could benefit from better selection for systemi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44590384f9aeeec643b30986f7a0b812
https://doi.org/10.1097/sla.0000000000005349