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𝐏𝐥𝐞𝐧𝐚𝐫𝐲 𝐒𝐩𝐞𝐚𝐤𝐞𝐫 𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 – 𝐀𝐂𝐑𝐓 𝟐𝟎𝟐𝟔 We are honored to welcome Dr. John Quackenbush, Professor of…
𝐏𝐥𝐞𝐧𝐚𝐫𝐲 𝐒𝐩𝐞𝐚𝐤𝐞𝐫 𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 – 𝐀𝐂𝐑𝐓 𝟐𝟎𝟐𝟔 We are honored to welcome Dr. John Quackenbush, Professor of…
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Jeff Allen
JUST RELEASED: Our 2024 Scientific Report contains original manuscripts and whitepapers based on our research partnerships and recent policy initiatives. Key Topics: · Real-World Evidence from Clinical Practice · Innovative Drug Development & Advancing Early Endpoints · Harmonizing Biomarker Measurement & AI Applications 2024 Scientific Report: https://lnkd.in/eYsP7s9u We hope this resource will provide unique insights and inform future oncology drug development, legislative and regulatory policy, and patient care. Thank you to all of our collaborators that help drive this work forward!
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Henry Lee
Today we, at Cultivarium, are launching PRISM, an AI assistant that records what actually happens during experiments in the lab. Protocols may be written down, but the real knowledge of science - how long to wait for a pellet to settle, the exact angle to pipette, the feel of "just right" - is passed down by watching others. It’s tacit, invisible, and often lost. PRISM is our effort to change that. It’s a system for multimodal protocol capture - a lightweight AI tool that observes scientists as they work and creates a structured, human-readable record of what they actually do, not just what they intend to do. This means: • More reproducible science • Easier onboarding of new researchers • A foundation for truly intelligent lab automation We built PRISM because scaling biology requires more than data, it requires capturing expertise. Learn more https://lnkd.in/e23r-RWg
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Alex Dickinson
It's all LLM training all the time now, nothing else matters 😳 "The Tahoe-100M data set is the world's largest atlas of single-cell transcriptomic data, mapping how drugs impact patient cells at scale. It comprises 100 million cells and 60,000 experiments, mapping 1,200 drug treatments across 50 different tumor models. It is larger than all public single-cell datasets combined, and 50x larger than all publicly available, drug-perturbed single-cell data put together." "The NVIDIA team will contribute machine learning and data engineering expertise to TRAIN MODELS on this data and package for use."
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Lorenzo Righetto
Using data from six population-scale cohorts involving more than ten million individuals, a large language model-based aging signature is shown to outperform traditional aging biomarker indices and signatures in predicting multiple health outcomes, at general and organ-specific levels https://lnkd.in/dhVk4d6E
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Ahmed (Hass) Hassoon
🚨 Can your AI save a life when the doctor’s makes diagnostic errors? Our team at Johns Hopkins Bloomberg School of Public Health built a first-of-its-kind benchmark to test whether LLMs can course-correct physicians' diagnostic errors during the initial patient presentation — the moment when small mistakes can have the biggest consequences. We tested models from: OpenAI – GPT-4o, GPT-4.5, GPT -o1 Anthropic – Claude 3.5, Claude 3.7 Google DeepMind – Gemini 2.5 Pro, Gemini 2 Flash xAI – Grok 2, Grok 3 DeepSeek AI – DeepSeek V3, R1 Amazon Nova Pro 📊 Finding: All models share a similar knowledge pattern — but differ sharply in disease-specific performance, with each excelling in some conditions and struggling in others. 💡 Opportunity: Let’s stress-test your latest models, refine this benchmark, and explore safe, high-impact clinical integration. 📩 Building the future of medical AI? My inbox is open. 📖 Stay tuned — the full manuscript with complete methods, results, and insights is coming soon. #AI #HealthcareAI #Diagnostics #PatientSafety #LLM #Benchmark #Collaboration 📢 Huge thanks to our incredible team: Xiaoyi P., Ruxandra Irimia, Anthony Li, MD, MPH, António Bandeira, MD, MPH
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