Toward the end of this year’s Google I/O keynote, Google DeepMind CEO Demis Hassabis declared, with a completely deadpan face, that the company hopes to “reimagine the drug discovery process with the goal of one day solving all disease.”

What Hassabis was really describing was Gemini for Science, a collection of experimental AI tools designed to encourage researchers to explore and make new discoveries.

I’m often critical of AI health in Optimizer, but Hassabis’ statement is one that deserves a lot more contextualization. Good science communication — something that is digestible enough for the layperson, that doesn’t unintentionally promote misinformation — has become increasingly difficult. Surely the researchers in the I/O audience understood the claim to mean that advances in AI have dramatically reduced the time it takes to make new medical discoveries. But for the average person (and arguably, even science communicators), it probably sounded like “Gemini is going to cure every disease because that is the power of AI.” This is just not how medical breakthroughs work in the real world.

For decades, AI has been an integral part of medical research and discovery. The algorithms that wearables use? That’s AI. Discoveries for noninvasive, wearable detection features? Machine learning, baby. Generative AI is a relatively newer entrant into this area of research, but it holds incredible promise. As part of my job, I often speak with clinical researchers, and many of the breakthroughs in consumer health tech over the years are due in part to AI advances. For example, this meta review found that AI played a major role in reducing the development timeline for the covid-19 vaccinations. That’s something that the entire world benefited from. However, the review also found that significant ethical, logistical, and regulatory challenges remain in using AI like this with regard to algorithmic bias, data privacy, and equitable global access.