By engaging with the right medical experts as early as possible in a drug’s lifecycle, pharmaceutical companies can gain enduring competitive edge. However, despite medical affairs departments continually undergoing profound change and growth, studies show 50% of new drug launches fail to meet company expectations. Could embracing AI in life sciences help to tackle this trend?
4 min read
When patients suffer unintended reactions to medicines, it can be both dangerous for the individual and costly to society. However, what if medical professionals could use machine learning to forecast adverse drug reactions (ADRs) and minimise risks to patients?
Topics: life sciences 3 Pillars Important Problems
2 min read
Clinical research data plays an essential role in the pharmaceutical industry, but can also eat up resources in terms of time and money. However, by utilising machine learning in clinical trials, efficiency can be drastically improved, enabling faster and more cost-effective drug development and better patient outcomes.
Topics: machine learning life sciences
2 min read
When we consider the role of artificial intelligence in life sciences, it already has a proven track record as an effective tool to support pharmaceutical research. However, companies are also moving fast to explore the transformational impact it can have on their commercial operations, such as sales and marketing.