Vasi Philomin is VP of Generative AI at AWS. He leads generative AI efforts, including Amazon Bedrock and Amazon Titan.
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The intersection of generative AI and healthcare/life sciences is a prime opportunity for innovation. After all, these industries rely on enormous amounts of data, and generative AI excels at absorbing and summarizing large volumes of text and visual data.
With generative AI, clinicians and researchers can use natural language queries to get the information they need to do their work better and faster. Generative AI can also leverage new insights from the data already residing within healthcare systems, pharmaceutical companies, research labs and other data-rich facilities.
As proof of the power of generative AI in healthcare and life sciences, consider a couple of recent stories:
• Researchers at the University of Toronto used generative AI to design a potential new cancer drug in thirty days.
• Pfizer built VOX, a generative AI solution that uses large language models to accelerate research, predict product yield and help it deliver more medicines to patients. (Disclosure: Pfizer collaborated with AWS on this project and used AWS cloud services.)
These efforts would have been impossible without generative AI and its ability to synthesize vast amounts of data—speeding complex, research-intensive projects. Both show how generative AI goes far beyond the capabilities of large-scale analytics and traditional machine learning. Both show how the adoption of generative AI promises to impact our health on an individual and community level.
In this article, let’s look at some of the other ways that generative AI can impact the healthcare and life sciences industries.
Healthcare: Improving Outcomes
We are all human, susceptible to viruses, diseases and other illnesses that generative AI can help identify and combat more quickly and effectively—making these advances all the more vital. Here are some examples of the many promising opportunities that are emerging in the key areas of healthcare and life sciences:
• Transforming clinical documentation. Many clinicians spend twice as much time on administrative tasks versus time with patients. Generative AI can speed up the time-consuming burden of clinical documentation by transcribing and summarizing patient-clinician conversations and generating preliminary clinical notes.
• Enabling early detection and diagnosis. Generative AI can scan massive volumes of visual information—including X-rays, CT scans and MRIs—and help spot patterns and anomalies. In this use case and many others, AI assists medical professionals, augmenting the expertise of medical professionals.
• Helping personalize treatment. Medical records provide extensive data (e.g., genomic data, lifestyle factors) that can be assimilated with generative AI, enabling clinicians to create highly targeted, personalized outreach and treatment plans based on each patient’s specific situation and needs.
Life Sciences: Inspiring Innovation
The field of life sciences is notoriously complex, research-heavy and costly. Generative AI offers the potential to streamline research—helping discover new drugs and enable highly personalized outreach and treatments. Consider a few use cases:
• Assisting drug research and design. Generative AI can help speed drug research by assisting the painstaking process of creating antibiotics, new drugs and vaccines—reducing costs and getting to market faster. Startup Evolvere Biosciences, for example, is researching protein structure to redesign antibacterial medicines—while minimizing resistance, side effects and dosage levels. (Disclosure: Evolvere Biosciences uses generative AI on AWS.) Insilico Medicine has also recently begun phase II trials with an AI-designed drug candidate.
• Accelerating clinical trials. AI can help pharmaceutical companies manage the enormous amounts of data and documentation generated during the long, complicated clinical trial process. And generative AI can help clinicians find the best trials for their patients.
• Fostering innovation. Ultimately, life sciences innovation comes down to unlocking new insights from research data. Generative AI helps drive innovation by speeding up research and development, enabling more innovations in less time.
Getting Started With Generative AI
Early grassroots research and experimentation are evolving quickly into more far-reaching implementations and specialized use cases.
As these use cases translate into real-world production, several key requirements will need to be answered upfront for generative AI to show success: How can you protect your data? What should data hygiene look like? How do you maintain costs?
There are also other challenges facing organizations working in this space. As I wrote in my recent article, “Putting Generative AI to Work For Your Organization," success will require organizations to think critically and customize generative AI applications using the organization’s unique data and knowledge.
So, how will generative AI end up transforming healthcare and life sciences when all is said and done? We’ll see. It’s still early days, of course.
But judging by the diversity of the uses explored and envisioned by clinicians and technologists in healthcare and life sciences, it’s clear that the impact of generative AI will be felt on a very human level—because our bodies and lives will be the ultimate beneficiaries of the advances and innovations that it enables.
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