NIH Develops AI Strategy to Transform Biomedical Research and Healthcare
Over the past month, the National Institutes of Health has been collecting public comments on its first-ever AI strategic plan, with the comment period closing on July 15. The RFI seeks input on AI use in biomedical discovery, public health, and clinical decision support, while exploring how NIH can collaborate with government agencies, advocacy organizations, and industry on AI development. Principal Deputy Director Matthew Memoli emphasized this will "guide AI research funding initiatives in healthcare for years to come across the entire agency" and establish a unified central AI structure to accelerate research translation to patient benefit.
Why It Matters: This will influence medical AI development standards and priorities for the next decade. If you work in or work with medical research or healthcare tech, watch for changes and opportunities which will accelerate healthcare AI adoption. Beyond healthcare, this signals how federal agencies across sectors could approach AI regulation, creating ripple effects that could influence everything from financial services to transportation technology.
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Virginia's AI Energy Dilemma: Growth vs. Affordability
Virginia, already home to the nation's "Data Center Alley," connected 15 new data centers in 2024 and expects another 15 by year-end, with over 23,000 MW of capacity in the pipeline. However, Dominion Energy warns that unconstrained AI data center demand could double the state's power consumption within ten years, potentially increasing average ratepayer bills by 50% over the next fifteen years. The state faces a critical policy moment as it balances economic benefits from data center investment against affordability concerns for residents and its commitment to achieve 100% renewable energy by 2050.
Critical Insight: Virginia's experience provides a potential roadmap for other states facing AI infrastructure demands. Policymakers are starting to consider requiring data centers to bring their own energy sources, prioritize builds in water-abundant regions, and establish community funds to offset infrastructure costs rather than passing the full burden to ratepayers. Business leaders implementing AI should also take note that local resource constraints and potential regulatory changes may become increasingly important as communities push back.
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