Vivek NATARAJAN

google deepmind

The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the run up to our 10th annual event on June 12th 2026 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!

At RAAIS we have a focus on translating cutting edge technology and research into production-grade products for real-world problems.

The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the run up to our 10th annual event on June 12th 2026 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!

Vivek is a Research Scientist at Google DeepMind leading research at the intersection of AI, science and medicine. His work focuses on a question that is rapidly becoming central to applied AI: what does it take to build systems that are not only impressive in demos, but genuinely useful in expert domains like healthcare and scientific discovery? In medicine especially, performance means more than fluency. It means reasoning under uncertainty, handling complex interactions, and meeting a far higher bar for trust and usefulness.

From medical benchmarks to clinical capability

Vivek is the lead researcher behind Med-PaLM (Nature, 2023) and Med-PaLM 2 (Nature Medicine, 2025), the first AI systems to obtain passing and expert-level scores respectively on US Medical Licensing Examination questions.

These results mattered for more than the benchmark itself. Medicine is one of the clearest examples of a domain where surface-level language ability is not enough. A model has to retrieve specialist knowledge, reason carefully, and communicate in a way that reflects the stakes of the setting. Med-PaLM helped show that medical capability could become a serious target for general-purpose AI systems, not just a niche research curiosity.

This was an important step for the field. It shifted the conversation from whether language models could be adapted to medicine at all, to how they should be evaluated, where they might be useful, and what standards they need to meet.

Project AMIE and the move toward real clinical interaction

Vivek also co-leads Project AMIE, a research programme aiming to build and democratise medical superintelligence. Over the past year, AMIE has shown promising potential in controlled settings across primary care, specialty care, and complex diagnostic challenges, both as a standalone system and as an assistive tool for clinicians.

This line of work matters because real healthcare is not a single-turn question answering task. It is a sequence of interactions shaped by ambiguity, incomplete information, follow-up questions, and changing hypotheses. A clinically useful system needs to do more than generate a plausible answer. It needs to engage with the process of care.

That makes AMIE especially relevant to the RAAIS audience. It reflects the broader shift from models that perform well on static tasks to systems that can operate across richer, more realistic workflows.

AI for science as well as medicine

Vivek recently also co-led the development of the AI co-scientist, a virtual AI collaborator designed to augment scientists, help uncover original knowledge, and accelerate the clock speed of scientific discovery.

This expands the scope of his work in a revealing way. The goal is no longer only to build systems that can answer expert questions, but systems that can support expert practice itself. In medicine, that means helping with clinical reasoning. In science, it means participating in the generation and testing of new ideas.

That is one of the most important frontiers in AI right now: moving from systems that organise existing knowledge to systems that may help produce new knowledge.

Vivek’s background

Prior to Google, Vivek worked on multimodal assistant systems at Facebook AI Research. He is also part of the faculty for executive education at the Harvard T.H. Chan School of Public Health in a part-time capacity.

That background helps explain the arc of his work. It sits at exactly the point where frontier model capability meets high-consequence real-world use - a place where applied AI becomes harder, more interesting, and much more important.