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We gave LFM2.5-VL-3B from @LiquidAI a generated skin-like image and let it choose tools. It mapped 6 regions, drew the contours, measured L04 at 8.8 × 8.1 mm, then picked what to review first. Local on a Mac Studio. Visual review, not diagnosis. 🧵
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Jev said contradict. Laya said agree. Same fictional note, same question. Active list: ramipril 5 mg daily. Signed discharge: stop ramipril. Both real calls are in the video. Across 4 authored notes and 16 prelabelled decisions: Jev 16/16, Laya 13/16.
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Four typed questions across four authored fictional notes, labels written before the calls. Jev 16/16, Laya 13/16. On the medication-conflict question alone: 4/4 vs 2/4. A useful diagnostic, not a clinical accuracy estimate.
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Jev ran through TypeSafe's hosted API. Laya's typed-decisions checkpoint ran locally on CPU, so this was not a speed contest. Nothing here tested patient records, native extraction, FHIR export, or treatment decisions. Laya is Apache 2.0. Jev is a hosted, closed model.
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『動くことと、院内で使うことは別』 OpenMedの告知が示しているのは、次の分担です。 ・手元(ローカル)で、文章中の医学用語の範囲と文脈を取る ・ネット上のサービス(ホスト型のJev)で、今ある所見か/過去の話かなどの状態(status)を付ける ・プログラムが下書きを組み立て、判断は出典の文章に戻れるようにする 例として挙がる対比は明確です。 「喘息」という言葉を見つけることと、それが小児期の既往でいまは解決済みだと付けることは、別の仕事だ、という切り分けです。 ここで見るべきは性能比較ではありません。 この分担が、日本のルールではどこまで「能力」で、どこから「許可」が要るか、です。 【Pros】 ・下書きをプログラム側で止められると、AIの出力と、人が確定した許可を分けやすい ・出典に戻れる設計は、監査と説明に向く(ただし最終責任は医師から移らない) ・状態付けだけを外に出し、文言抽出を手元に残せるなら、外に出す情報を減らせる余地がある ・文脈処理が「助言まで」と決まっていれば、診断を自動で起動しない前提と合いやすい 【Cons】 ・状態付けをホストに頼む限り、渡した文章や状態は外に出る。海外サーバなら、預ける場所と委託の説明が要る ・「構造化だからハルシネーションはない」は型の正しさの話であって、医学的に正しい保証ではない ・状態ラベルが診断や治療方針の支援に読めると、プログラム医療機器(SaMD)としての該当性が問題になりうる ・試作の承認のまま確定が通ると、デモの能力が本番の許可にすり替わる 薬機法では、同じ技術でも「何のために使うか」の表示で該当性が変わります。 「記録の整理支援」なら非該当の典型に近づき「診断・治療方針の支援」なら該当側へ寄ってくると思います。 医師法を鑑みると「AIを使っても最終判断の主体は医師」という整理も、下書き確定の人間承認とセットで見る必要がありますが、我々医師はどこまで理解してこれらに承認を与え、責任を負えるのか、よく議論されなければなりません。 デモで動くことと、院内に置いてよいことは別です。何が手元に残り、何が外に出て、誰が確定するかを先に一文で固定してからでないと、本番の臨床経路には乗せられません。 mhlw.go.jp/content/10601000/…
Starting today, we will evaluate Jev for medical AI. Finding “asthma” is one job. Knowing it belongs to resolved childhood history is another. OpenMed supplies local spans + context; hosted Jev assigns status; code controls the draft. Every decision links to its source.
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Starting today, we will evaluate Jev for medical AI. Finding “asthma” is one job. Knowing it belongs to resolved childhood history is another. OpenMed supplies local spans + context; hosted Jev assigns status; code controls the draft. Every decision links to its source.
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OpenMed retweeted
Love this run. MiniCPM5-2B on a local Mac, reconciling an old med list against a later note, and correctly keeping naproxen in history instead of the current export — with every graph edge tied back to source. This is exactly the kind of careful, on-device clinical workflow we hoped the model would support. Thanks for putting it to work 🙌 @OpenMed_AI
We gave MiniCPM5-2B from @OpenBMB an old medication list and a newer note saying naproxen was stopped. With OpenMed 2.5, it stays in history, not the current-medication export. Every graph connection links to its source. Local Mac. Fictional notes. Here’s the run 🙂
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Hot take: a closed biology model is a dead end. 30+ open models made 4x faster by people who never trained them. A 10K-token ribosome on one GPU node. Binder design from $10K to $150 per target. None of it works behind an API. Open weights are the only thing that compounds.
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact. In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code. Read more: anthropic.com/research/claud…
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The code that did this is itself open: github.com/anthropics/upl Same principle on the clinical side. Every OpenMed model is open weights on Hugging Face, so a hospital can run it inside its own firewall and check every output: huggingface.co/OpenMed
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We gave MiniCPM5-2B from @OpenBMB an old medication list and a newer note saying naproxen was stopped. With OpenMed 2.5, it stays in history, not the current-medication export. Every graph connection links to its source. Local Mac. Fictional notes. Here’s the run 🙂
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OpenMed retweeted
🏥 Bringing local agentic AI to healthcare with MiniCPM5-2B × OpenMed! @OpenMed_AI paired OpenMed with MiniCPM5-2B to explore a local clinical AI workflow — combining privacy-preserving clinical data processing with a compact model capable of tool use, reasoning, and long-context understanding. ✨ Highlights: 🧠 MiniCPM5-2B powers the agent layer, calling tools, comparing lab results, and generating clinical handoffs with source references 🔒 OpenMed masks sensitive identifiers and extracts clinical context before the model processes the data ⚡ Compact 2B-scale model enables practical local inference on resource-constrained hardware 🛠️ Together, they demonstrate how open models can connect clinical data processing with agentic workflows while keeping inference on local hardware It’s exciting to see MiniCPM5-2B move beyond standalone model benchmarks into real-world healthcare workflows — bringing tool use, reasoning, and local deployment together with OpenMed. 🙌 Built something with MiniCPM5-2B? Share your case with us! 🚀 🔗 GitHub: github.com/maziyarpanahi/ope… 🤗 Model: huggingface.co/openbmb/MiniC…
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298 commits. 13 contributors. 7 first-timers. OpenMed v2.5.0 is out 🙂 Clinical privacy and extraction previews, richer FHIR + OMOP exports, and more local audit tools. 4,331 commits so far. Thank you to everyone who has helped build this, past and present.
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And thank you to our contributors whose X accounts we haven’t found yet. Using your GitHub names here so nobody gets left out: 09Catho, 22f3001334-droid, AadityaAnand, AaronProbha18, aastha-m22, aayushi-sing, AbrarH4, AbrarOfficial310, affanhamid, AlexFucuson9, AlyanPremani05, Ankitha101003, annaladasur, annamalai2912, annguyenNous, ardittirana, axelray-dev, be-student, Bembaby, Binary67, chawki-nasrallah, coolstick784, cycsmail, DrVelvetFog, eholy, eslam-ahmed43, gaoflow, guhyun9454, handlecusion, Hitesh-XS, Ispagiytiy, janithcd, JonthanaHanh, josephkehan-prog, kashvipeehu24, KaustAbhinand, KevinAndrewDong, kkkhs, krudo-taco, libaojiang, LobsterQBA, Mr-Neutr0n, nyxst4ck, PouyanJay, prakashiitp, Rahul-pamula, reddyvaishnavi25, RonitGandhi, rtmalikian, SamBradley2024, sharrmeen, ShiHuiwen-creat, speedyk-005, takagibit18, thangldw, thirdwing, tomatotomata, Udaytaneja, vamshiss, vanthinh6886, viditjain88, VishnuPrasath-S-20, VishnupriyaRNathhh, wessim852. Whether your first PR landed in v2.5 or you helped months ago, you’re part of OpenMed. Really happy we get to build this together 🙂
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We paired MiniCPM5-2B with OpenMed and gave it a fictional clinical note. OpenMed masks identifiers and extracts clinical context. MiniCPM calls tools, compares lab results and writes a handoff with clickable sources. All inference on the Mac. Here's the run 🙂
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In other words: race or pace all you want behind closed doors. In open source, we’ll keep advancing responsibly, transparently, and in public. More than ever, intelligence this powerful cannot be left in the hands of a few. More than ever, open-source AI must win.
Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead. You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement. I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible. But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier. Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want. Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well. So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it. If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.
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that’s gonna be us working on open-source medical AI in the future! lmao
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