Generative AI’s most noble mission: Improving and saving lives

Michael J. Fox says it perfectly: “Family is not an important thing. It’s everything.” That’s exactly how I feel. As a technology professional, seeing how artificial intelligence (AI) and generative AI/large language models can improve and save lives makes me think about the significant difference this can have on families and communities worldwide–including mine. It’s one of technology’s most profound and noble moments.AI is ideally suited to life sciences. Here’s why. First, the problem of chronic disease is staggering in size. The CDC estimates that in the US alone, “90% of the nation’s $4.1 trillion spent in annual healthcare expenditures are for people with chronic and mental health conditions.^1″

Preventing and treating these diseases is important work—both to improve people’s lives and well-being and to heal the economy.Second, solving chronic disease requires a better understanding of the human genome—that’s the job of life sciences. And it’s a complicated and elusive job. The human genome, or DNA, is 99.9% identical across people. However, according to the National Human Genome Research Institute²,

“the 0.1% genomic differences come from variations among the nearly 3 billion bases (or “letters”) in our DNA.” A variant could be present anywhere among those 3 billion letters, creating incredible complexity. Yet, finding these differences is important to decoding health as those variables often determine who develops which diseases. 

So, while understanding and decoding complex human DNA is the key to mitigating or curing diseases, it mandates working with massive and highly sophisticated data sets. That’s what AI does very well—with the scale, speed, and accuracy that cannot be replicated manually. And when it comes to curing diseases, speed can be a lifesaver.

How generative AI and AI can help

Improving patient treatments: As a leader in precision medicine, the Translation Genomics Research Institute, or TGen, has seen the power of high-performance computing, fast processing, analytics, and AI bring next-level speed and capabilities in fighting disease. In 2005, it took six years to sequence the full human genome. Today, that same sequence can be completed in 24 hours³.

Using that speed and intelligence together with various data sets and use cases, TGen translates lab discoveries into better patient treatments at an unprecedented pace. 

Accelerating drug discovery:

Traditionally, drug discovery has been a costly and time-consuming process. Generative AI and large language models are accelerating that process by predicting potential drug candidates and molecular structures, such as proteins. To do so requires analyzing the vast datasets in genomics to uncover insights that aid in disease and drug discoveries, which saves time and money. McKinsey estimates that research and development gains from generative AI can save 10-15% of costs. This is significant because worldwide spending on life sciences research and development is estimated at $328 billion per year⁴.

As part of the drug discovery process, clinical trials can be accelerated with AI models that identify patient candidates based on genetic profiles. Once the clinical trials are underway, data analysis using AI models helps researchers make better decisions about drug efficacy and safety.

Researchers can also benefit from AI’s natural language processing capabilities to rapidly analyze substantial amounts of medical literature. This helps researchers save time and costs, stay current, and potentially improve outcomes.

Personalizing medicine:

Generative AI can rapidly synthesize patient data from numerous sources, such as genetic data, clinical information, and medical literature, analyze it, and produce personalized treatment plans. In addition, AI models help predict patient responses to specific therapies, helping to customize and optimize treatments.

Automating medical imaging and diagnoses:

Medical images like X-rays, CT scans, and MRIs can be analyzed with generative AI and AI models for faster, more accurate diagnoses. In turn, this enables earlier detection of potential diseases, which can improve patient outcomes.

Further, AI-powered natural language processing can assist in the organization and capture of written and verbal patient medical records, reducing the administrative load on healthcare providers and creating a structured data approach that can be used to identify trends and facilitate discoveries.

Enabling data and AI to save lives

The use cases for AI and generative AI in life sciences are life changing. However, the first step—getting the right kind of storage infrastructure—helps to ensure AI’s final mile. Many organizations today have data storage systems that were not built to handle AI, which can halt AI processing. Instead, modern storage solutions must provide capabilities such as distributed storage, data compression, and efficient data indexing, all of which enable the speed and scale that AI requires.

As I think about my parents, my extended family, and my future, I am grateful for the role of technology in revolutionizing life sciences and providing such positive potential for our individual and collective well-being. For me, working with life sciences organizations around the world to advance generative AI and AI has taken on a personal and extraordinarily profound purpose.

You might be interested in Dell’s upcoming 30-minute online event about “AI Anywhere on Data Everywhere” on December 7th at 11 a.m. CT. I’ll be there and would love to have you join me.

Learn more about unstructured data storage solutions and how they can enable AI technology.

View the TGen customer case study.

Explore healthcare and life sciences AI solutions from Intel.

^[1] https://www.cdc.gov/chronicdisease/about/costs/index.htm

^[2] https://www.genome.gov/about-genomics/fact-sheets/Diversity-in-Genomic-Research

^[3] https://www.dell.com/en-us/dt/case-studies-customer-stories/tgen.htm#collapse

^[4] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier