Imagine a world where Artificial Intelligence (AI) helps scientists understand aging better than ever before. This field isn't short on data—it's overflowing with it! And that's where AI comes in. Tools like Large Language Models (LLMs) can evaluate geroprotective interventions, those designed to prevent or slow the aging process.

But hold on, don't just trust any AI tool blindly. We need to make sure their responses are correct, useful, and comprehensive. Plus, they should explain themselves clearly and consider how one thing affects another (causality). They should also be able to handle different types of data (interdisciplinarity), follow guidelines (standards), interpret long-term data (longitudinal), and understand how our bodies age (aging biology).

You might think, "That's a lot to ask! " But it's necessary. Instead of just comparing data from standard databases, AI should look at biomarkers and outcomes, figuring out how they change over time.

So, how do we make AI do all this? We can combine LLMs with Knowledge Graphs and use specific workflows. Retrieval-Augmented Generation is a great example. But remember, benching AI's performance is crucial. We don't want to rely on AI tools without knowing how well they work, right?

In the end, we're not just looking for answers; we're seeking advice on longevity interventions. Informed use of LLMs could make all the difference.

AI in Aging: A Peek into the Future

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