
The Clinical Trial Risk Tool has been featured in a guest column by Thomas Wood, director of Fast Data Science, in Clinical Leader, titled A Tool To Tackle The Risk Of Uninformative Trials, in cooperation with Abby Proch, Executive Editor at Clinical Leader.
In this article, Thomas Wood discusses the issue of uninformative clinical trials in pharmaceutical research. Uninformative trials fail to provide meaningful results, either because they don’t address key questions or are poorly designed. These trials waste resources, expose participants to unnecessary risks, and hinder progress in medical knowledge.
Wood uses the definition of “uninformative trials” from Zarin et al (2019)[1] as trials that do not deliver informative results, even though the investigated treatment may be effective or ineffective. An informative trial, according to experts, must meet five conditions: addressing an important question, providing meaningful evidence, being feasible, conducted scientifically, and reporting methods and results promptly.
The Clinical Trial Risk Tool was created to prevent such trials by providing features like a clinical trial budget estimator based on real-world cost data. The development of the first version of the tool was published in Gates Open Research[2]. In future, the tool could retrieve past trials with similar endpoints or inclusion/exclusion criteria to improve protocol design. It could also generate personalised feedback for different stakeholders, such as medical professionals, financial planners, and patient advocates.
Fast Data Science’s Clinical Trial Risk Tool offers a solution by allowing users to upload trial protocols, where AI analyzes the document for risk and cost factors. The tool estimates trial costs and identifies risks, helping to avoid uninformative outcomes early in the design process.
Unleash the potential of your NLP projects with the right talent. Post your job with us and attract candidates who are as passionate about natural language processing.
Hire NLP ExpertsMany companies and organisations have large datasets that are stored in a very unstructured format. For example, you could work for a US based healthcare provider or insurer and have patient records stored in a free text format such as HL7 files or PDFs. A building regulator, land registry, or mortgage provider may have texts and accompanying diagrams from thousands of building inspections or land title deeds. A patent attorney’s office may have records of patent applications in PDF format.

On 20 May, I attended the Expert Witness Conference in Dublin, Ireland, organised by La Touche Training. It was an eye opening event with a mixture of lawyers and expert witnesses in different fields from Ireland and abroad. The event was chaired by Mr Justice Michael Peart, with a keynote address by the Honourable Mr Justice David Barniville, President of the High Court of Ireland.

Fast Data Science at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin Links to guidance on legal AI issued by legal authorities and other organisations Official guidance UK: Artificial Intelligence (AI) Guidance for Judicial Office Holders, 31 October 2025. https://www.judiciary.uk/wp-content/uploads/2025/10/Artificial-Intelligence-AI-Guidance-for-Judicial-Office-Holders-2.pdf
What we can do for you