Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
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Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
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VIEW ROLESThere is a huge amount of discussion going on, around the value – and potential pitfalls – of artificial intelligence, particularly since the arrival of chatGPT and other large language models. From my perspective, the concerns that we are hearing tend to be around the pros and cons of established tried and trusted technology to solve key challenges in drug development, compared to the potential of new innovations. And our experience is that the best approach is using a blend of technologies; bringing the right expertise to the table to know when to apply the most appropriate AI/ML method. And balancing cost, accuracy and convenience is important.
I always enjoy listening to what our clients have to say on the matter. Recently, I was chatting to an attendee in one networking session at our NLP Summit. This person works at a top 10 pharma that have been getting value from IQVIA’s Natural Language Processing technology for many years (almost since the founding days of Linguamatics), and they talked about the benefits of working with IQVIA for their NLP needs, compared to some of the up-and-coming opensource tools.
It was an interesting discussion and so I thought I’d share some of their key points. Maybe these resonate with you as you look for new solutions:
The speakers at our two Summit days evidenced that researchers, scientists and clinicians in healthcare and pharma are aware of the benefits of different NLP tools and know when to use the best tool for the task. For example, Chad Konchak from NorthShore University Health System talked about how they are leveraging analytics capabilities and a new AI initiative around natural language processing to aid in health equity; with the mission of reducing healthcare disparities in the care they provide. Jenny Uyei & Yuting Kuang (IQVIA) talked about using a combination of rules-based NLP in combination with machine learning for literature search and evidence synthesis projects such as synthesising relevant real world data on COVID-19 vaccine effectiveness, antiviral effectiveness, and long COVID. And Emilie Louvet (AstraZeneca) talked about using rules-based NLP and deep learning (e.g. BERT) to find relevant clinical trial endpoints from literature, to assist clinical trial study design.
If you’d like to learn more about IQVIA NLP and how we can help you find the best NLP tool for the task, please contact us, or listen to the talks from the Summit here.
IQVIA is using vast quantities of data in powerful new ways. See how we can help you tap into information from past trials, patient reported outcomes and other sources to accelerate your research.
Insights are trapped in mountains of text. NLP sets them free.
