‘Solve all diseases,’ you say?

“攻克所有疾病”，你确定吗？

Google DeepMind CEO Demis Hassabis made a bold claim at this year’s I/O keynote. Not so fast! 在今年的 I/O 大会上，Google DeepMind 首席执行官 Demis Hassabis 发表了一番大胆的言论。且慢！

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.” 在今年 Google I/O 主题演讲的尾声，Google DeepMind 首席执行官 Demis Hassabis 面无表情地宣称，公司希望“重塑药物研发流程，目标是有朝一日攻克所有疾病”。

This is the sort of statement that the phrase “big, if true” was coined for. 这正是那种让人想说“如果属实，那可真是了不得”的言论。

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. Hassabis 真正描述的其实是“Gemini for Science”，这是一套旨在鼓励研究人员进行探索并实现新发现的实验性 AI 工具集。

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. 在《Optimizer》专栏中，我经常对 AI 健康领域持批评态度，但 Hassabis 的这番话确实需要更多的背景解读。良好的科学传播——即既能让外行易于理解，又不会无意中传播错误信息——正变得越来越困难。

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. 参加 I/O 大会的研究人员肯定明白，这一说法是指 AI 的进步极大地缩短了医学新发现所需的时间。但对于普通人（甚至可以说，对于科学传播者）来说，这听起来可能像是“Gemini 将治愈所有疾病，因为这就是 AI 的力量”。然而，现实世界中的医学突破并非如此运作。

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. 几十年来，AI 一直是医学研究和发现不可或缺的一部分。可穿戴设备使用的算法？那是 AI。非侵入式可穿戴检测功能的发现？那是机器学习的功劳。生成式 AI 虽然是该研究领域相对较新的参与者，但它展现出了令人难以置信的前景。

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. 作为工作的一部分，我经常与临床研究人员交流，多年来消费级健康科技的许多突破在一定程度上都要归功于 AI 的进步。例如，一项元分析发现，AI 在缩短新冠疫苗研发周期方面发挥了重要作用，全世界都从中受益。然而，该研究也指出，在利用 AI 进行此类应用时，算法偏见、数据隐私和全球公平获取等方面仍存在重大的伦理、后勤和监管挑战。

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.) 在主题演讲中，Hassabis 提到了 Google 的 AlphaFold 和 AlphaGenome 项目。前者帮助研究人员更好地理解蛋白质结构。这一点至关重要，因为蛋白质在无数生物过程中扮演着多种角色。更好地理解蛋白质——甚至设计新型合成蛋白质——可能是解锁癌症治疗的关键。（最近，科学家们发现了 1,700 种可能实现这一目标的蛋白质。）

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. 传统上，发现新蛋白质、了解其功能以及它们如何与其他分子相互作用是一个耗时数年的过程。像 AlphaFold 这样的工具可以极大地缩短这一时间。在现实案例中，研究人员已经利用该模型协助开发疟疾疫苗、发现导致低密度脂蛋白（即“坏胆固醇”）的关键蛋白质，并了解了另一种导致早发性帕金森病的蛋白质，以及其他应用。

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. 与此同时，AlphaGenome 是另一个帮助研究人员预测人类 DNA 序列突变的模型。该模型的潜力在于它可能帮助研究人员理解某些疾病的成因，尽管 Google 在《自然》杂志的一项研究中指出，该模型存在重要的局限性。例如，该模型尚未经过验证，也并非为个人基因组预测而设计，且难以捕捉细胞和组织特异性的模式。对于研究人员来说，这些是重要的细微差别，但对于其他人来说，这些通常难以理解。

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. 在许多方面，Hassabis 在台上所说的话并不是针对你我。此外，还有一个重要的背景：这些 AI 模型和 Gemini for Science 工具并不会在未来三、五年甚至十年内神奇地根除癌症或所有以前“无法解决”的疾病。这类事情很可能至少需要 20 年，甚至更久。你可能会觉得这时间太长了——尤其是考虑到这对目前患病的亲人或你自己的寿命意味着什么。但就严谨的科学研究而言，这已经是一个雄心勃勃且激进的估计了。

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, but it’s extremely human to do so. 但在一个还要发布数以亿计其他 AI 代理和功能的发布会上，你确实没有时间去解释这些。问题在于，这些言论传播范围广，影响深远。对于我们大多数人来说，到目前为止，AI 健康体验还很糟糕，充斥着重复的指标摘要、幻觉和繁琐的引导。我们不应该将研究人员使用的 AI 工具与消费级 AI 健康功能混为一谈，但人类往往很难避免这样做。