{
  "abstract": "Clinical AI is increasingly integrated into electronic health record workflows, but its cost is difficult to define. Health systems may pay through usage-based services, subscription or enterprise licenses, local hosting, or combinations of these approaches. Usage-based pricing introduces a distinct problem because billing units such as tokens or billable sessions are vendor-defined and may vary across data types, languages, and over time. Fixed licensing agreements can make vendor charges more predictable, while locally hosted AI models shift spending toward infrastructure and personnel. Regardless of the payment structure, the full resources required to use and maintain AI, including engineering, validation, oversight, and rights to clinical data, are difficult to capture. As the complexity of multimodal and agentic systems increases, so will that of the payment structures. Health systems will need to proactively develop ways to accurately measure the cost of AI use, evaluate its clinical value, and negotiate agreements with AI vendors that allow for regular reassessment of prices and terms. Doing so will help ensure that AI does not become yet another driver of healthcare spending detached from value delivered.",
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  "affiliations": [
    "Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.",
    "Department of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, United States of America.",
    "Departments of Biomedical Data Science, Computer Science, and Epidemiology, Dartmouth College, Hanover, New Hampshire, United States of America."
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      "name": "Jennifer Hong"
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      "name": "Saeed Hassanpour",
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  "title": "Paying for clinical AI: The bill is not the cost",
  "title_text": "Paying for clinical AI: The bill is not the cost",
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