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Google tests AMIE for clinical video consultations

Google’s research medical AI system, AMIE (Video), conducted synchronous video consultations with professional patient actors and received clinical evaluator ratings on par with primary care physicians across several core measures. Fifteen trained actors portrayed conditions across cardiopulmonary, abdominal, HEENT, neurological or psychiatric, and musculoskeletal presentations. Google says studies involving real patients and their own health […]

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Stanford Evo 2 AI model generates phages against E. coli

Stanford researchers have synthesised nearly 300 phages from DNA sequences produced by the Evo 2 generative AI model. Laboratory testing narrowed the group to 16 phages that showed particularly strong E. coli-killing activity. The work centres on bacteriophage ΦX174, pronounced “FYE-ex-1-7-4”. Brian Hie, an assistant professor of chemical engineering and Dieter Schwarz Foundation Stanford Data

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PRISM2 model uses clinical dialogue to interpret pathology slides

Built by Paige and Microsoft, PRISM2 reads whole-slide images through a perceiver-based encoder trained jointly on tissue tiles and clinical dialogue drawn from pathology reports. The model aggregates thousands of tile embeddings per slide into one representation, then generates text that answers diagnostic questions rather than simply classifying pixels.  Training data spans 2.3 million whole-slide

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Why biological data matters more in AI drug discovery

GSK has entered into a research collaboration with British biotechnology company Relation Therapeutics worth up to $110 million, expanding the companies’ existing work in AI-assisted drug discovery. Under the agreement, Relation will generate large-scale datasets measuring how human cells respond to genetic changes and drug interventions. The data will be used to train AI models

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Guardoc Health processes clinical documentation using Amazon Nova models

Guardoc Health says it processes over one million clinical documents daily using Amazon Nova models through Bedrock. Bringing AI into clinical documentation comes down to a specific kind of risk calculation. Get it wrong and the errors compound into denied Medicare claims under the Patient-Driven Payment Model, audit fines, litigation exposure, and in the worst

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Nvidia bets physical AI can solve healthcare robotics’ data problem

Nvidia’s new Medical Physics Simulation framework treats healthcare robots as physical AI systems that need embodied experience to learn, not just code. Physical AI is the term Nvidia and much of the robotics industry now use to describe machines that have to learn how the world behaves through contact, force, and consequence, rather than through

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SenseTime’s Galaxy Project targets domestic AI chip scale-up

SenseTime has launched the Galaxy Project, teaming with nearly 20 partners to scale domestic AI chip infrastructure in China. In a keynote titled ‘Intelligent Transformation and Symbiosis,’ Yang Fan – the company’s co-founder and president of its Large Device Business Group – laid out what SenseTime describes as a closed loop connecting chip-level technology, ecosystem

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Examining Google DeepMind’s AI bioresilience push

Google DeepMind and Isomorphic Labs outlined a bioresilience program to curb AI misuse in biology while aiding outbreak response. The two organisations published an update on a joint initiative that began quietly and has now built out more than 15 partnerships with government bodies, biosecurity organisations, and research groups over the past 12 months. The

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AWS and Bluesight build AI for hospital 340B compliance

AWS (Amazon Web Services) has explained how Bluesight developed Prism, an AI layer that connects hospital pharmacy and compliance data across its product suite. Prism Assistant for ControlCheck has reached general availability and operates across 20 health systems, according to AWS vendor statements. Meanwhile, a multi-product agent for 340B Group Purchasing Organisation (GPO) compliance remains

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AWS GraphRAG deployment cuts drug research cycles by 87%

A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph. Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial

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