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Doctor Evidence Search Tool “Evidence Finder” Adds Sakana Namazu

Sakana AI

Sakana Namazu is now inside a doctor search tool from Iris. It scored 96.4% on a medical licensing exam, but the real point is faster access to sources doctors can trust.

Based on reporting by Sakana AI — read the original for the full story.

Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error

Sakana AI’s Japanese-tuned LLM, Sakana Namazu, is being plugged into Evidence Finder, a doctor-facing evidence search tool from Iris. The service pulls papers from sources like PubMed, shows where its answers come from, and includes a Verify function to check whether cited papers really exist.

That workflow matters because doctors are short on time and the medical literature never stops moving. New papers, guidelines, comparisons — all of it has to be searched, read, and weighed while the clinician is still doing the actual job.

The setup here splits the work between Iris and Sakana AI. Iris’ own search and verification algorithms pick the relevant papers and handle the evidence side; Namazu takes over the part that compares, combines, and turns that material into an answer. Sakana says that made the model fit naturally into a path that gets physicians to primary sources quickly.

The company says the decision was driven by Namazu’s Japanese-language ability and its performance in medicine. An evaluation model using Namazu on Evidence Finder scored 96.4% on the 120th Japanese medical licensing exam in February 2026. Iris says that is the highest public score among domestic foundation models designated by Japan’s Ministry of Economy, Trade and Industry GENIAC, based on materials available as of September 1, 2026.

Sakana also argues that the result is not just about raw knowledge. Medical questions need domain knowledge, reasoning, and the ability to understand Japanese intent clearly enough to present it in a form doctors can check. The company says Namazu keeps the base model’s math, general reasoning, and coding ability on major benchmarks, while improving Japanese instruction following, translation, and Japan-specific context handling.

There’s a broader bet underneath all of this. Sakana wants to make strong open models usable in Japan without wrecking what made them strong in the first place, and it’s also pushing for inference to stay domestic. The immediate win is a better doctor tool; the larger pitch is that “Japanese AI” should mean more than fluent Japanese.

My take — AI-written commentary, not fact-checked reporting

This is the right kind of AI story: boring on the surface, useful underneath. Doctors do not need a chatbot with confidence issues; they need a system that gets them to the source without wasting half a morning. The industry keeps selling magic, while the better business is usually just less friction and fewer bad answers.

Read more about this at: Sakana AI

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