This post originally appeared on Fast Data Science’s blog on LinkedIn.
Clinical trials, the backbone of medical science advancement, often grapple with high costs, complexity, and lengthy timelines. Fast Data Science presents Fast Clinical AI, a game-changing solution that harnesses the power of Natural Language Processing (NLP) and predictive modelling to tackle these challenges head-on.
Fast Clinical AI automates the extraction of critical information from trial protocols, significantly reducing manual efforts. This tool identifies risk factors and predicts costs and enrolment criteria, ensuring efficient trial planning and execution.
By identifying potential risks early in the trial process, Fast Clinical AI allows researchers to take proactive measures, minimising the chances of trial delays or failures. This proactive approach ensures that trials are faster and more reliable.
Fast Clinical AI helps predict and manage the costs associated with clinical trials, ensuring better resource allocation. Reducing manual efforts and streamlining processes significantly reduces the time required to bring new treatments to market.
Explore how Fast Clinical AI can revolutionise your clinical trials. Visit here for more details. Learn more about clinical trial cost modelling with NLP and AI here. Connect with us today to see how we can support your clinical trial efforts. Let’s innovate together!
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Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of a clinical trial is strongly dependent on the structure and coordination of the teams managing it. Given the high stakes and significant impact of every decision made during the trial, it is essential for each team member to collaborate efficiently in order to meet strict deadlines, comply with regulations, and ensure reliable results.
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Clinical trial protocols are detailed master-plans of a study – often 100–200 pages long – outlining objectives, design, procedures, eligibility and analysis. Reading them cover-to-cover can be daunting and time-consuming. Yet careful review is essential. Protocols are the “backbone” of good research, ensuring trials are safe for participants and scientifically valid [1]. Fortunately, there are systematic strategies to speed up review and keep it objective.
Introduction People have asked us often, how was the Clinical Trial Risk Tool trained? Does it just throw documents into ChatGPT? Or conversely, is it just an expert system, where we have painstakingly crafted keyword matching rules to look for important snippets of information in unstructured documents? Most of the tool is built using machine learning techniques. We either hand-annotated training data, or took training data from public sources. How We Trained the Models inside the Clinical Trial Risk Tool The different models inside the Clinical Trial Risk tool have been trained on real data, mostly taken from clinical trial repositories such as clinicaltrials.
