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  "text": "RT @BoWang87: Two major AI releases this week:\n\n• Qwen3.5 — new open-source small models\n• GPT-5.4 — newest frontier closed model\n\nMost ben…",
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    "text": "Two major AI releases this week:\n\n• Qwen3.5 — new open-source small models\n• GPT-5.4 — newest frontier closed model\n\nMost benchmarks compare math and coding.\n\nBut the real test for frontier AI should be biology and healthcare.\n\nThat’s where mistakes actually matter.\n\nSo our team at @UHN ran them on EURORAD — 207 expert-validated radiology differential diagnosis cases.\n\nResults:\n\nGPT-5.4: 92.2%\nQwen3.5-27B: 85%\nGemini 3.1 Pro: ~79%\n\nA 27B open model that runs on a laptop is only 7 points behind the most powerful AI model on earth — and already beating Gemini on this benchmark.\n\nThat gap is much smaller than people expected.\n\nAnd it matters.\n\nFor years hospitals faced an impossible tradeoff:\n\nFrontier models → patient data leaves the hospital\nLocal models → not good enough\n\nThat tradeoff may finally be ending.\n\nQwen3.5-27B runs fully local.\nNo API. No cloud. No patient data leaving the building.\n\nHIPAA / PHIPA compliance becomes architecture, not paperwork.\n\nInteresting detail: 27B and 122B score almost identically here.\nScaling bigger didn’t help much.\n\nOne caveat: with web-scale training, it’s hard to completely rule out that frontier models like GPT-5.4 may have seen parts of evaluation datasets.\n\nStill, the signal is clear:\n\nSmall models are getting good enough for real clinical AI.\n\nAnd if we want to measure real AI progress,\nbiology and healthcare should be the benchmark. \n\nHuge credit to the team\n@alifmunim @AlhusainAbdalla @JunMa_AI4Health @Omar_Ibr12 @oliviaamwei",
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