Natural language processing is becoming ever more important in research. The explosion in interest in LLMs is sparking many opportunities to analyse text data in healthcare, social sciences, and other areas.
We’re proud to announce that the Clinical Trial Risk Tool has been selected as a winner of the Plotly Dash Example Apps Challenge (2023), out of 25 amazing apps submitted by the community.
Funded by the Bill and Melinda Gates Foundation, this app uses natural language processing, Plotly Dash, and the spaCy and Scikit-Learn libraries to calculate the risk of a clinical trial failing to deliver informative results. It reads the trial protocol and identifies key features from the text which are fed into a risk model.
Join Plotly’s webinar to see the tool in action! https://tinyurl.com/4zd3emkr
You can try the new version of the app at: https://clinicaltrialrisk.org/tool
You can try version 1.0 of the app (using Plotly Dash) at: https://clinicaltrialrisk.org/tool/login?guest=true/.
Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 (https://doi.org/10.12688/gatesopenres.14416.1).
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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