Google DeepMind Scientist Uses AI to Chase Cures
Vivek Natarajan, a research scientist at Google DeepMind, told Business Insider that his father's multi-decade struggle with Parkinson's disease motivated him to focus on medical applications of AI. Per Business Insider, Natarajan works on projects intended to help doctors and scientists propose experiments and accelerate the search for new treatments. He described this direction as emerging from personal experience: "I asked this question to myself, 'okay, where is AI going to generally have the most impact?' And to me, that answer felt like medicine and science," Natarajan told Business Insider. The article also reports that Natarajan worked at Meta by 2017 before moving toward healthcare-focused AI research.
What happened
Vivek Natarajan, a research lead at Google DeepMind, told Business Insider that his father's 35-year career and subsequent decline from Parkinson's disease shaped his decision to apply AI to medicine. Business Insider reports that Natarajan aims to develop AI tools that can help doctors and scientists propose experiments and accelerate the search for new treatments. Business Insider quotes Natarajan: "I asked this question to myself, 'okay, where is AI going to generally have the most impact?' And to me, that answer felt like medicine and science." The article states that Natarajan worked at Meta by 2017 as deep learning moved from research into industry.
Natarajan's work at Google DeepMind
Natarajan leads research at the intersection of AI, science, and medicine at Google DeepMind, per Google Research. His projects include medical foundation models such as Med-PaLM and the conversational diagnostic system AMIE, and multi-agent systems such as the AI co-scientist - a Gemini-based system designed to act as a virtual scientific collaborator, per Google Research and press coverage. The Air Street Press RAAIS 2026 conference program lists Natarajan as a featured researcher presenting on biomedical AI applications.
Industry pattern
AI-driven drug-discovery and biomedical research work typically centers on hypothesis generation, experimental prioritization, and target identification - with human-in-the-loop validation required before lab steps. Practitioners in this space report that progress depends on data quality, domain collaboration, and reproducibility: the link between a model suggestion and a validated experimental outcome remains technically demanding.
What to watch
Track publications, open-source tools, and benchmarked workflows from Google DeepMind's biomedical AI group, as well as progress on datasets, synthetic-data methods, and platforms that enable reproducible model-driven experiment proposals at scale. The Business Insider profile documents one practitioner's path and motivations; it does not announce a new product or technical release from Google DeepMind.
Scoring Rationale
A practitioner profile documenting one researcher's motivation and general research direction at Google DeepMind. Relevant to practitioners exploring AI in healthcare and drug discovery but does not announce a new model, benchmark, dataset, or validated discovery. Pulled from 5.6 to 5.0, placing it in the Solid tier appropriate for a single-source personal profile with no new technical announcement.
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