The Clinical Trial Risk Tool is a browser-based tool which uses Natural Language Processing (NLP) to analyse clinical trial protocols. We are pleased to announce the publication of a technical paper on the tool.
A BibTex citation is as follows:
@article{wood2023clinical,
title={Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness},
author={Wood, Thomas A and McNair, Douglas},
journal={Gates Open Research},
volume={7},
number={56},
pages={56},
year={2023},
publisher={F1000 Research Limited}
}
Thomas Wood presents the Clinical Trial Risk Tool at the Clinical AI Interest Group at Alan Turing Institute The Clinical AI Interest group is a community of health professionals from a broad range of backgrounds with an interest in Clinical AI, organised by the Alan Turing Institute. In the group’s November 2025 meeting, the talk was given by Dr Jeff Hogg, Programme Director, MSc AI Implementation (Healthcare), University of Birmingham and Clinical Innovation Officer in AI, University Hospitals Birmingham NHSFT, titled AI Readiness for Health and Care Provider Organisations.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Multi-Arm & Multi-Stage (MAMS) Clinical Trials Design Tips The design of clinical trials is increasingly challenged by the Rising Costs, limited availability of eligible patient populations, and the growing demand for timely therapeutic evaluation. Traditional parallel-group designs, which typically compare a single intervention to a control, are often insufficient to meet these pressures in terms of speed, efficiency, and resource utilization.
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