Q&A: A Bold Vision for `Hyperscaling Healthcare`

How do you define “hyperscaling” of healthcare?

 Today, some biopharmaceutical developers are using AI-enabled technology to instantly interpret the data they generate and in the context of similar studies published by other researchers. Clinicians can order diagnostic tests for patients that will offer prognoses and treatment recommendations based on data gathered from the experiences of thousands of other patients. But for every patient to benefit from these technologies, we need to hyperscale them by making them them more broadly available and easier to use than they are now.

 There are three critical elements of hyperscaling. First, we must enhance the ability of researchers to gather and interpret data much faster than was possible in the past. Then we must expand access to the massive computing power necessary for effective data collection and processing. Finally, we must make it easy for healthcare professionals to generate novel insights from the data that will translate to improvements in patient care.

 What are the biggest challenges to realizing this vision?

 Equipment makers have already developed cutting-edge instruments that allow researchers to delve deeper into biology or pathology than they could in the past, thanks to AI and other advanced technologies now built into them. But some of these systems are so complex that only a small number of specially trained people can effectively use the technology. Another challenge is that the data these new systems generate can be overwhelming, making it difficult for healthcare innovators and providers to derive usable insights.

 What are some examples of how AI is benefiting biopharmaceutical researchers now?

 AI is enabling “agentic workflows” that instantly adapt to new information and circumstances. These workflows enable researchers to quickly interpret the values and images they are generating. AI incorporated into microscopes, for example, improves cell segmentation by allowing researchers to measure the fluorescence in cells in real time. This ability facilitates advanced processes, such as the automatic detection and imaging of rare events. In mass spectrometry, AI is helping researchers optimize methods and workflow efficiencies. New software solutions can take datasets obtained from mass spectrometry and quickly translate them into actionable insights, and quantitation workflows can be streamlined with automation.

 How can patients benefit from advances in data science?

 One example of how advances in data science are improving patient care is the development of better companion and complementary diagnostics. For example, tools that are used to diagnose breast cancer can help determine the best treatment based on the genetics of the patient and the tumor. Adding AI to diagnostic tools allows oncologist to interpret the data in the context of data from thousands of other patients with the same diagnosis. That can improve the oncologist’s ability to select a treatment regimen that’s most likely to be effective. This is an area of intense development that should yield many exciting diagnostic tools as AI capabilities continue to improve.

 What changes need to happen in the biopharma industry for your vision of hyperscaling to become reality?

 First, all of this new technology must be broadly accessible to everyone working in healthcare. That means AI tools must be highly intuitive. Improvements in large language models can help create more intuitive interfaces between operators and instruments.

 Ultimately, for AI to truly enhance biopharmaceutical discovery and change patients’ trajectories, we must bring together usable data from a wide range of sources. That will require integration unlike anything the industry has attempted before. It won’t be easy, but it will be worth it for the value it will bring to patients.