‘Solve all diseases,’ you say?

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they’re going to change your life. This week’s issue is a special early edition tied to The Verge’s Google I/O coverage. You can expect our next issue at its usual time next Friday. Opt in for Optimizer here. 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.” This is the sort of statement that the phrase “big, if true” was coined for. 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. In the keynote, Hassabis pointed to Google’s AlphaFold and AlphaGenome projects. The former helps researchers better understand protein structures. This is important because proteins play myriad roles in countless biological processes. Better understanding proteins — or even designing novel synthetic proteins — could be the key to unlocking cancer treatments. (Recently, scientists found 1,700 new proteins that might do just that.) Traditionally, to discover new proteins, what they do, and how they interact with other molecules was a yearslong process. Something like AlphaFold helps to dramatically reduce that timeline. In terms of real-life case studies, researchers have used this model to help develop malaria vaccines, discover a key protein behind LDL (or the “bad cholesterol”), and understand another protein behind early-onset Parkinson’s disease, among other applications. Gemini for Science is a group of AI tools meant to help researchers make new discoveries. Meanwhile, AlphaGenome is another model that helps researchers predict mutations in human DNA sequences. The potential for this model is that it may help researchers understand why certain diseases happen, though in a Nature study, Google has noted that there are important limitations. For instance, this model hasn’t been validated or even designed for personal genome prediction, and it struggles to capture cell- and tissue-specific patterns. These are important nuances for researchers, but something that typically will fly over the heads of everybody else. In many respects, what Hassabis was saying onstage wasn’t directed at you or me. And, some other important context, these AI models and Gemini for Science tools are not going to magically eradicate cancer or every previously “unsolvable” disease in the next three, five, or even 10 years. Something like this is more likely to take at least 20 years, probably more. You might think that’s a long time — especially in terms of what that means for a currently sick relative, or your own lifespan. But as far as rigorous scientific research goes, that’s an ambitious, aggressive estimate. But this isn’t exactly something you have time to explain at a keynote where you’re announcing forty bajillion other AI agents and features. The problem is that these statements travel far and have a wide-ranging impact. For the majority of us, AI health has been, thus far, a craptacular experience of regurgitated metric summaries, hallucinations, and tedious hand-holding. We shouldn’t necessarily conflate AI tools for researchers and consumer AI health features,