On 29 May, Thomas Wood presented a webinar on how AI and Natural Language Processing (NLP) can transform clinical trials in the pharmaceutical industry.
During the event, Thomas talked about
Thomas Wood also discussed the Clinical Trial Risk Tool, developed by Fast Data Science for clients in the pharma industry (CTOs, trial sponsors) to be able to use NLP and AI to predict trial risk and anticipate costs.
Download the pitch deck for the Clinical Trial Risk Tool
Fast Data Science - London
Fast Data Science is a leading data science consultancy firm providing bespoke machine learning solutions for businesses of all sizes across the globe and specialising in AI in pharma. With a focus on innovation and collaboration, Fast Data Science empowers businesses to leverage the transformative power of data.
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Thomas Wood presents the Clinical Trial Risk Tool before the November meeting of 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.
Fast Data Science will appear at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin On 20 May 2026, La Touche Training is running the Expert Witness Conference 2026, at the Radisson Blu Hotel, Golden Lane, Dublin 8, Ireland. This is a full-day event combining practical workshops and interactive sessions, aimed at expert witnesses and legal professionals who want to enhance their expertise. The agenda covers critical topics like recent developments in case law, guidance on report writing, and techniques for handling cross-examination.
Guest post by Alex Nikic In the past few years, Generative AI technology has advanced rapidly, and businesses are increasingly adopting it for a variety of tasks. While GenAI excels at tasks such as document summarisation, question answering, and content generation, it lacks the ability to provide reliable forecasts for future events. GenAI models are not designed for forecasting, and along with the tendancy to hallucinate information, the output of these models should not be trusted when planning key business decisions. For more details, a previous article on our blog explores in-depth the trade-offs of GenAI vs Traditional Machine Learning approaches.
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