Drug Name Recogniser Google Sheets plugin update We have updated the Drug Name Recogniser Google Sheets™ plugin to cover molecular mass, chemical formula and SMILES strings.
AI Companies Revolutionizing the Pharma Industry Artificial intelligence (AI) has emerged as a transformative force in the pharmaceutical industry. By accelerating drug discovery, optimizing clinical trials, and enabling precision medicine, AI is revolutionizing how treatments are developed and delivered. With the ability to analyze enormous datasets and simulate biological processes, AI-driven solutions are shortening the time from research to market while improving accuracy. In this blog, we explore the companies leading the way in AI-powered drug development, their notable projects, and how AI is reshaping the future of healthcare.
Fast Data Science updates Drug Named Entity Recognition Python library We’re excited to announce a major update to our popular Drug Named Entity Recognition (NER) Python library! This new version (v2.0.0) brings several improvements to make finding drug information in text (named entity recognition) even easier and more accurate.
How Can AI Be Used in Clinical Trials? Clinical trials are essential for developing new drugs and therapies, but they are often slow, costly, and complex. The traditional process involves recruiting participants, managing extensive data, ensuring regulatory compliance, and analyzing outcomes—all of which can take years and significant resources. Artificial Intelligence (AI) is transforming clinical trials by making them faster, more efficient, and more accurate. AI algorithms can quickly analyze electronic health records (EHRs) to identify eligible participants, significantly reducing recruitment time and costs. During the trial, AI can monitor patients in real-time, using wearable devices and mobile apps to continuously collect data, detect adverse events early, and enhance patient safety.
On 29 May, Thomas Wood presented a webinar on how AI and Natural Language Processing (NLP) can transform clinical trials in the pharmaceutical industry.
Modelling risk and cost in clinical trials with NLP Fast Data Science’s Clinical Trial Risk Tool Clinical trials are a vital part of bringing new drugs to market, but planning and running them can be a complex and expensive process. A key part of this planning is accurately estimating the cost and risk of a trial. Traditionally, this has involved a team of experts manually sifting through lengthy clinical trial protocols, often hundreds of pages long.
Fast Clinical AI We are developing a natural language processing (NLP) tool, Fast Clinical AI, for identifying risk, cost, recruitment and enrolment criteria, and consent complexities of clinical trials using natural language processing, which can be integrated to data sources from trial management platforms, allowing pharma companies to leverage AI in their operations.
On 7 June, 2023, at 6pm UK time (1pm EDT), we will be presenting in the webinar Dash in Action: Image Processing, Forecasting, NLP. This is a community showcase hosted by Plotly, a company in Canada which makes the graphing software Plotly.
We’re proud to announce that the Clinical Trial Risk Tool has been selected as a winner of the Plotly Dash Example Apps Challenge (2023), out of 25 amazing apps submitted by the community!
We designed the Clinical Trial Risk Tool, a clinical trial risk assessment tool using AI and NLP to quantify the risk of a trial ending uninformatively. Get in touch with us if you need custom AI strategy consulting for healthcare.
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