The DIA brings together researchers, pharma companies and innovative tech companies to addresses the challenges facing the pharma sector today. With the prevalence of AI across the business sector, it was no surprise that AI was a hot topic at two recent conferences: DIA Europe held in Vienna, and DIA RSIDM held in Washington.
AI has gone from being seen as a futuristic concept to a practical tool that can be put to use to help make processes quicker and more efficient across the whole process of getting new life saving drugs to market. But what are the top AI Use Cases in Pharma?
Here are three key areas where AI is changing the pharma industry:
Top AI Use Cases in Pharma no. 1: Drug Discovery
with big data analytics, to model scenarios at high volumes that would not be possible using only human labour and analysis. Examples include the modelling of different types of cancer cells to work out what conditions allowed the disease to develop, and using this information to try and create new treatments.
Top AI Use Cases in Pharma no. 2: Selection of Patients for Clinical Trials
Once the new drug has been created and passed certain phases of testing, it needs to be tested on real patients. These trials must have enough volume to have a big enough sample size to draw conclusions and it can often be hard for pharma companies to find patients for new and specific drugs they have developed. In this case, AI can match specific drugs to larger databases of patients quicker than relying on people building the connections themselves.
Top AI Use Cases in Pharma no. 3: Automation of Pharmaceutical Reporting
The Regulatory Submissions, Information and Document Management forum organised by the DIA, particularly highlighted the heavy (but absolutely necessary) burden of reporting for pharmaceutical companies who are bringing new drugs to market. Each stage of the drug discovery process must be reported on before the drug can be approved by regional authorities such as the FDA in the US or EMA in Europe. The ability to take the data of clinical studies and automatically generate parts of the necessary reports makes this reporting process faster and allows pharmaceutical companies to further streamline the process of getting drugs to market.
Natural Language Generation plays a critical role in automating the writing of reports in the Pharma sector. Pharma companies around the world are using Yseop to help with their report writing, for example using data from clinical trials to generate sections of the CSR report. Using AI to automate pharma reports frees up medical writers’ time, allowing them to move away from data crunching reams of information and instead focus on more high value analysis and adding technical insight to reports. To learn more about how this works in reality, why not have a look at our latest guide, Overcoming Bottlenecks with Drug Discovery and Development which focuses on the practical difference that AI & NLG can make in CSR reporting.