Wouter van Elmpt

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

2020 · 78 Zit. · Physics and Imaging in Radiation Oncology

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example

2024 · 10 Zit. · Radiotherapy and Oncology

Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?

2024 · 9 Zit. · JCO Clinical Cancer Informatics

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study

2023 · 7 Zit. · Frontiers in Oncology

External Validation of a Bayesian Network for Error Detection in Radiotherapy Plans

2021 · 7 Zit. · IEEE Transactions on Radiation and Plasma Medical Sciences

Can Modified TTRIPOD and PROBAST Guidelines Be Used to Assess Transparency and Risk of Bias of Studies on AI-Driven Segmentation and Treatment Planning in Radiotherapy?

2023 · 0 Zit. · SSRN Electronic Journal

304: A research-based implementation strategy for AI in radiotherapy: can implementation science help?

2024 · 0 Zit. · Radiotherapy and Oncology

OC-0474 Bias and reporting quality of artificial intelligence models in radiotherapy treatment planning

2021 · 0 Zit. · Radiotherapy and Oncology

A Research-Based Implementation Strategy for AI in Radiotherapy: Can Implementation Science Help?

2024 · 0 Zit. · SSRN Electronic Journal

MO-0308 AI-based radiotherapy treatment planning quality assurance: A multi-institutional study

2023 · 0 Zit. · Radiotherapy and Oncology