More than productivity: What’s next for AI in pharma and healthcare

When we talk about generative AI, many healthcare and pharma leaders understandably express excitement about potential productivity gains. But AI has the power to deliver even more. While productivity should be a focus, we believe generative AI’s real value will emerge when it helps produce better insights that lead to improved decision-making.

I spoke at an AWS conference about this next, transformational stage of AI in healthcare and pharma. While nearly every organization will soon leverage generative AI to improve productivity, using it for insights and decision-making will be a true differentiator. Here are a few areas outside of automating tasks where we believe AI can soon have a positive impact:

Identifying drug molecules: It stands to reason AI could reduce the significant effort that is currently needed to identify molecules in pharma R&D.

Drug discovery: It’s intriguing to ponder generative AI’s ability to extract and link between t-cell phenotypes and associated genetic allocations.

Clinical trials: Writing clinical trial protocols takes a lot of time. Can AI speed up this process?

Regulatory submissions: We all know generative AI and large language models are adept at writing and only figure to get better. It will be interesting to see in the coming years how they help pharma companies generate the necessary documents required by regulatory bodies such as the U.S. Food and Drug Administration.

Predicting patient drop-off: It’s not difficult to see a world where generative AI can produce synthetic data that can in turn be deployed to predict when patients may drop off a clinical trial.

Data moats: Generative AI makes it possible to leverage and setup data moats for a competitive edge, enabling companies to discern better insights from large amounts of varied data.

What will it take to develop a long-term capability around generative AI? Cloud infrastructure is critical, but your AI team also needs to build orchestration- and agent-based architectures that can scale, while setting the right foundation models to enable generative AI. It may be helpful to explore working with a tech service partner, such as AWS, to develop the right tech stack of generative AI services and machine learning tools to unlock the value of your data.

To begin taking advantage of AI’s potential, it’s important to decide what type of generative AI model you want to use. One option is to deploy one of the publicly available models we’ve all heard of, and these can be optimized for your organization. The other is to create your own model and fine tune it to your specifications. Additional considerations you should keep in mind as you plan to leverage generative AI:

• Using the right cloud infrastructure
• Building AI capabilities and finetuned models that can scale
• Writing effective prompts and who should manage that process
• Having the right people with the right skill sets in place
• Keeping data private and secure
• Measuring AI’s impact with the right key performance indicators
• Integrating generative AI with existing traditional AI capabilities

If you want to learn more about how you can take full advantage of AI’s transformative benefits, while using it responsibly, watch this presentation from AWS re:Invent.