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Bunkerhill raises $55M to scale agentic AI across health systems

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Bunkerhill Health has raised $55 million to scale its agentic AI platform, Carebricks. The closing of the company’s Series B round, announced today, folds in continued participation from Sequoia Capital, Felicis, Optum Ventures, and Y Combinator. However, a funding total doesn’t answer the key question any hospital executive wants to know about healthcare AI: does […]

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Neko Health raises $700 million to expand AI body scans in the US

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Neko Health has raised $700 million to expand its AI body scans in the United States, starting with a clinic in New York. The company’s preventive screening service combines medical imaging, blood tests, proprietary sensors, and clinician review. The Series C round was led by Lightspeed Venture Partners and co-led by O.G. Venture Partners. Existing

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AWS and Bluesight build AI for hospital 340B compliance

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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%

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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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De-Identifying Clinical Data for AI: A Technical and Regulatory Guide

ai, AI (Artificial Intelligence), Artificial Intelligence, Healthcare

What is Medical Data De-Identification? Medical data de-identification involves removing or masking personal details of patients from their health records, such as names, dates, location details, ID numbers, and faces in photos or biometrics. This breaks the link between the medical data and the individual, protecting privacy while allowing AI developers and researchers to use

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De-Identifying Clinical Data for AI: A Technical and Regulatory Guide

ai, AI (Artificial Intelligence), Artificial Intelligence, Healthcare

What is Medical Data De-Identification? Medical data de-identification involves removing or masking personal details of patients from their health records, such as names, dates, location details, ID numbers, and faces in photos or biometrics. This breaks the link between the medical data and the individual, protecting privacy while allowing AI developers and researchers to use

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Takeda signs US$600M AI drug discovery deal with Insilico

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Takeda has entered a strategic collaboration with Hong Kong-based Insilico Medicine to use AI in early-stage drug discovery across the Japanese pharmaceutical company’s therapeutic areas. The companies did not disclose which therapeutic areas or disease targets will be covered under the collaboration. The agreement gives Takeda access to Insilico’s Pharma.AI platform, which supports biological target

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Understanding DICOM Annotation for AI: From Data Structure to Clinical Impact

ai, AI (Artificial Intelligence), Annotation, Artificial Intelligence, Healthcare

Medical imaging AI is transforming healthcare. Behind every diagnostic model that detects lung nodules and brain hemorrhages, flags a fracture, or segments an organ lies thousands of carefully annotated medical images. And the vast majority of those images are in one format: DICOM. DICOM image annotation is not a task that generic image labeling workflows

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Understanding DICOM Annotation for AI: From Data Structure to Clinical Impact

ai, AI (Artificial Intelligence), Annotation, Artificial Intelligence, Healthcare

Medical imaging AI is transforming healthcare. Behind every diagnostic model that detects lung nodules and brain hemorrhages, flags a fracture, or segments an organ lies thousands of carefully annotated medical images. And the vast majority of those images are in one format: DICOM. DICOM image annotation is not a task that generic image labeling workflows

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Expert Medical Image Annotation for Ophthalmology and Cardiovascular AI

ai, AI (Artificial Intelligence), Artificial Intelligence, Healthcare

Fields like ophthalmology and cardiovascular care, where imaging is central to diagnosis, have emerged as critical frontiers for AI, making high-quality medical image annotation services indispensable. It’s about training medical AI systems with data that shows how doctors actually think and make decisions. This means the data needs to be labeled by experts, such as

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