Global biomedical content represents critical data for pharmaceutical companies, healthcare and insurance providers and publishers. Such content cannot be easily handled by business applications because it is unstructured and varies across different data sources. With speed and accuracy necessities in the medical field, organizations must find a way to overcome this barrier to understanding language.
A hybrid approach to artificial intelligence, combining the strengths of both natural language understanding (NLU) and machine learning (ML), provides an ideal solution that mimics the human-like comprehension of biomedical content such as clinical trials, real-world data, medical reports, literature, and social media, to name a few.
This capability can help to accelerate drug discovery and development, innovate faster and increase access to healthcare. This session will start with a demonstration of hybrid NL capabilities which goes beyond entity extraction to relationship identification. It will then be followed by a study on the importance of the human dimension when utilizing AI to combine ten years of clinical trial data with real-world data, literature, and social media data. The session will then conclude with key considerations to win with AI and NLU in the gene therapy space.
In this webinar, you will learn:
- How NLU can accurately transform clinical trials data, real-world data and scientific literature into knowledge and insight.
- How to recognize the operational challenges that may stand in the way of your project’s success, to prevent data-driven projects from being derailed.
- What the key considerations are to winning with AI and NLP across therapeutic areas.
