We have improved the Clinical Trial Risk Tool in the last 6 months, making it more user friendly and taking on board the feedback that we’ve received. We’ve improved the accuracy of the machine learning components too. The tool now outputs its key figures such as risk levels and estimated cost in easily readable cards, so you can see at a glance the key takeaways from your protocol: The risk factors are now organised into collapsible categories, so you can explore them easily without an information overload.
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.
Over the years, the overall cost of the drug development process has been exponentially increasing, prompting the adoption and use of adaptive clinical trial design software. Though there are practical difficulties and barriers in implementing clinical trial solutions, these problems are adequately addressed to overcome these issues as they arise. With advancements in software technologies, further improvements are being made to the software’s adaptive clinical trial design. Despite these progresses, just only a handful of well-established software with various types of clinical trial adaptations is currently available.
A clinical trial protocol is a document which serves as the step-by-step playbook for running the trial. The clinical trial protocol guides the study researchers to run the clinical trial effectively within a stipulated period. The prime focus of the clinical trial protocol is to ensure patients’ safety and data security. [1, 2] As the clinical trial protocol is an essential document for the seamless execution of the clinical trial, reviewing (peer-reviewing) the protocol is essential to ensure the scientific validity/viability/quality of the protocol.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction As we move toward 2025, clinical trial regulations are undergoing significant transformation. This shift is being fueled by technological advancements, changing healthcare needs, and an increasing emphasis on transparency and patient safety. In this post, we will explore the key clinical trial regulations shaping the clinical trial landscape, the challenges professionals face, and the strategies they must adopt to navigate this ever-evolving environment.
Thomas Wood has recently joined the Clinical Trial Files podcast with Karin Avila and Taymeyah Al-Toubah, discussing the inception of the Clinical Trial Risk Tool, what impact AI can make in clinical trials, and what Alan Turing would make of it all. This is an episode dedicated to Alan Turing’s 113th birthday on 23 June 2025. You can find the episode on Spotify Apple Podcasts Amazon Music Podcast Index Fountain Podcast Addict Podverse.
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Before launching a clinical study, even the most promising idea must be vetted for feasibility. In other words, can this trial be executed successfully in the real world? Feasibility assessments examine practical factors like available patients, site capabilities, timelines, and budget. This step is crucial. A majority of trials encounter delays or enrollment shortfalls (by some estimates, 70–80% of trials) [1], driving up costs and risking failure.
Risk assessment is a critical process in clinical trials that involves thorough and systematic evaluation of potential risks to the study volunteers, biases affecting the results and data integrity/protection. The process requires the use of risk assessment forms to identify, evaluate, and mitigate risks and ensure patient safety and data integrity.[1] The scope of this article is to discuss whether the utilization of clinical trial assessment templates is worth the time of the study sponsors/investigators.
