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      <image:title>Blog - AI in Healthcare: What’s Actually Working? - Make it stand out</image:title>
      <image:caption>Finding: The time savings were real, but they scaled with how much a physician actually used the tool — the technology did not create value on its own; adoption intensity did.</image:caption>
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      <image:title>Blog - AI in Healthcare: What’s Actually Working? - Make it stand out</image:title>
      <image:caption>Finding: The algorithm's accuracy wasn't what saved lives — the mortality benefit only appeared when a clinician acted on the alert within three hours. This is the cleanest published proof that AI value is gated by workflow and trust, not model performance alone.</image:caption>
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      <image:title>Blog - AI in Healthcare: What’s Actually Working? - Make it stand out</image:title>
      <image:caption>Finding: AI-supported reading caught more cancer, including more aggressive and advanced disease, while nearly halving radiologist reading workload — a rare case where quality and capacity improved together rather than trading off.</image:caption>
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      <image:title>Blog - AI in Healthcare: What’s Actually Working? - Make it stand out</image:title>
      <image:caption>Finding: The mechanism is credible and consistent with other dental-AI evidence — color-annotated radiographs improving patient understanding and case acceptance in the chair. The specific 13x figure is a single-site, vendor-reported number and should be re-derived from your own case-acceptance and production data before it goes in a pro forma.</image:caption>
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      <image:title>Blog - AI in Healthcare: What’s Actually Working? - Make it stand out</image:title>
      <image:caption>Finding: The office-level, controlled results (case acceptance, diagnostic consistency) are the credible part of this study. The enterprise-wide dollar figures are projections built on that per-clinic data, not a reported financial result — worth citing the mechanism, not the headline number.</image:caption>
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