This month, I presented the Clinical Trial Risk Tool at the Plotly Dash in Action webinar. I was interviewed by Plotly’s Community Manager Adam Schroeder. You can watch the relevant part of the webinar below.
The Clinical Trial Risk Tool was one of four interactive apps presented as part of the webinar. The speakers at the webinar were:
Screenshot of Matteo Trachsel’s Thermoplan dashboard which calculates the carbon footprint of coffee machine usage.
Screenshot of Agah Karakuzu’s dashboard which allows neuroscientists to assess the reproducibility of T1 values across different sites and vendors where researchers used the same research protocol. (T1 is the time it takes water molecules in the brain to return to their original state following a magnetic pulse).
If you would like to cite the tool alone, you can cite:
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 doi: 10.12688/gatesopenres.14416.1.
A BibTeX entry for LaTeX users is
@article{Wood_2023, doi = {10.12688/gatesopenres.14416.1}, url = {https://doi.org/10.12688%2Fgatesopenres.14416.1}, year = 2023, month = {apr}, publisher = {F1000 Research Ltd}, volume = {7}, pages = {56}, author = {Thomas A Wood and Douglas McNair}, title = {Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness}, journal = {Gates Open Research} }
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Designing a high-quality clinical trial protocol is critical for the success of any study. A protocol is the blueprint that outlines every aspect of a trial. In an ideal world, a flawless protocol would require no revisions and include only essential elements. In reality, however, the average protocol undergoes 2–3 amendments and often contains excessive data collection and overly complex entry criteria.
Clinical trials have long been the foundation of medical breakthroughs, but traditional methods often stumble over slow timelines, high costs, and difficulties in finding the right participants. Artificial intelligence (AI) — a technology ready to transform this landscape by making trials faster, more affordable, and smarter. The accelerating adoption of AI in clinical trials signals a major shift in healthcare research. It is already making significant strides in transforming clinical trials.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan In clinical trials, a staggering 80% encounter delays during the startup phase and 37% struggle to meet enrollment targets. Read more Key clinical trial statistics. These figures highlight a critical, yet often underemphasized, aspect of clinical trials—the feasibility process. The feasibility process is essential for assessing the practicality of a clinical trial’s design, ensuring the study is prepared to tackle the challenges that may arise.
