Why it's Notable:

• Bayer has expanded its partnership with Google Cloud which is poised to be a ‘key unlock and value addition’ to Bayer’s ambitions in the AI space. Bayer intends to leverage this partnership to enhance its capabilities in drug discovery, clinical trials and radiology. Bayer will leverage Google’s Tensor Processing Units (TPUs), specialised hardware for accelerating machine learning tasks, to perform large-scale quantum chemistry calculations, potentially unlocking new insights in drug development. Furthermore Bayer plans to use Google Cloud's Vertex AI and Med-PaLM 2 tools to streamline clinical trials through analysis of large datasets to generate insights to advance R&D, while also using AI to automate regulatory documentation to speed up timelines. Finally Bayer envisions using AI to transform radiology, leveraging Google Cloud's capabilities to enhance accuracy of radiological findings.

• BMS outlined plans to leverage generative AI to enhance efficiencies when it comes to documentation. According to an interview with Chief Digital and Technology Officer Greg Meyers, BMS recognizes its industry's heavy reliance on documents, especially in clinical trials and research. Generative AI is being used to query tens of thousands of documents, providing fast and highly accurate answers to complex questions that would otherwise take days to resolve. BMS are also employing generative AI to speed up the process of converting tabular data in clinical trials to narrative form, in a matter of minutes as opposed to days. Furthermore generative AI is being explored in drug design and discovery.

Industry Implications:

• Advancements in AI present an opportunity to enhance efficiencies in pharma. The recent activity in AI within pharmaceutical companies carries several key implications for the industry. AI is accelerating drug discovery processes by analysing vast datasets and assisting in the identification of potential drug candidates, as well as enabling more personalised medicine approaches through analysis of patient and genetic data. AI is also improving the efficiency and accuracy of clinical trials by rapidly deciphering data, identifying correlations, and automating aspects of the regulatory documentation process, which can reduce trial costs and timelines. It is also increasing productivity of researchers and developers through automation of routine tasks.

• However promising AI appears, it requires a sustainable approach to development and implementation. Pharma companies need to attract and retain the right talent, and invest in training to ensure its workforce are effectively equipped to harness AI technologies. Partnerships with big tech companies like Google have become more common and will continue to be leveraged to access advanced AI technologies and expertise. There also needs to be appropriate ethical and regulatory frameworks in place to ensure data privacy, transparency, and responsible AI use, including addressing biases. AI in pharma is still a relatively new concept, but with the right approach, the potential value add to both the industry and to patients is significant.  

Read the full story on Bayer here

Read the full story on  BMS  here