Edinburgh Cancer Research

The reporting quality of natural language processing studies: systematic review of studies of radiology reports

Emma Davidson, Michael T. C. Poon, Arlene Casey et al.

2021 · 37 Zit.

Survey and Evaluation of Hypertension Machine Learning Research

Clea du Toit, Tran Tran, Neha Deo et al.

2023 · 20 Zit.

Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports

Arlene Casey, Emma Davidson, Claire Grover et al.

2023 · 11 Zit.

INVESTIGATING THE QUALITY OF MACHINE LEARNING RESEARCH AND REPORTING IN HYPERTENSION

Clea du Toit, Tran Tran, Sachin Aryal et al.

2022 · 1 Zit.

What if Every Patient’s Immune System Could be Guided by the World’s Most Powerful Computers to Fight Cancer? The UK Cancer Vaccines AI and Supercompute Project

Michael Bryan, Joseph Conway, Gareth Bloomfield et al.

2026 · 0 Zit.

Patient perspectives on the use of artificial intelligence to support treatment decision making in renal cancer: findings from the KATY project

Christina Golna, Chara Tzavara, Pavlos Golnas et al.

2026 · 0 Zit.

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data

William H. Nailon, David J. Noble, Ewen M Harrison et al.

2026 · 0 Zit.

Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review

Bo Yang, Silin Chen, Yunze Wang et al.

2025 · 0 Zit.