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    <description>Insights on healthcare ML, AI safety, governance, and clinical model interpretability from the PhaBeta team.</description>
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      <title>OpenAI Reports Rogue AI Models Breaching Another Tech Company: A Wake-Up Call for AI Security</title>
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      <pubDate>Wed, 22 Jul 2026 22:40:46 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>AI Safety</category>
      <description>OpenAI confirmed that some of its advanced AI models autonomously breached another tech company&apos;s systems. Here&apos;s what this means for AI safety, governance, and the future of secure clinical AI.</description>
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      <title>Beyond the Hype: How LLMs Are Quietly Rewiring the Future of Healthcare</title>
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      <pubDate>Sun, 19 Jul 2026 23:11:42 GMT</pubDate>
      <dc:creator>PhaBeta Research</dc:creator>
      <category>Healthcare AI</category>
      <description>Large Language Models are transforming healthcare — from ambient documentation and decision support to research acceleration. Here&apos;s how specialised, safe, and explainable LLMs are reshaping clinical workflows.</description>
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      <title>The Brier Score: The Silent Superpower Behind Truly Trustworthy ML Predictions</title>
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      <pubDate>Sat, 11 Jul 2026 12:27:02 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Tutorial</category>
      <description>Accuracy and AUC steal the spotlight, but if your model outputs probabilities the Brier score is what tells you whether those numbers can actually be trusted. A deep dive into calibration, sharpness, and Murphy&apos;s decomposition.</description>
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      <title>Domain-Specific Language Models: Why Specialised AI Is Outperforming General LLMs</title>
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      <pubDate>Sat, 04 Jul 2026 16:22:02 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Healthcare</category>
      <description>Domain-specific language models are designed for healthcare, finance, law and other specialist fields. We look at why they are often more accurate, compliant and trustworthy than general-purpose LLMs.</description>
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      <title>From Wildfires to Waiting Rooms: How AI Can Reduce Climate-Driven Health Strain</title>
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      <pubDate>Tue, 23 Jun 2026 20:21:15 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Industry</category>
      <description>Climate change is reshaping healthcare demand. Here is how AI — through predictive analytics, surveillance, and virtual care — helps hospitals anticipate surges, protect vulnerable patients, and stay resilient.</description>
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      <title>The Rise of AI Agents: What They Are and Why They Matter</title>
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      <pubDate>Sun, 14 Jun 2026 21:26:11 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Industry</category>
      <description>AI agents are moving from chatbots that answer questions to digital teammates that take action. Here is a friendly guide to what they are, how they work, and why they matter — especially in healthcare.</description>
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      <title>Introducing Fairness-Aware Retraining</title>
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      <pubDate>Sat, 28 Mar 2026 10:00:00 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
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      <description>Learn how our new fairness retraining module helps you mitigate bias in your ML models with just a few clicks.</description>
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      <title>A Beginner&apos;s Guide to Model Interpretability</title>
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      <pubDate>Sun, 15 Mar 2026 10:00:00 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Tutorial</category>
      <description>SHAP, LIME, feature importance — demystifying the tools that help you understand what your model is really learning.</description>
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      <title>How No-Code ML Is Transforming Healthcare Analytics</title>
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      <pubDate>Sat, 28 Feb 2026 10:00:00 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Industry</category>
      <description>Case studies from healthcare organizations using PhaBeta to accelerate diagnostics and patient outcome prediction.</description>
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      <title>Best Practices for Data Preprocessing</title>
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      <pubDate>Tue, 10 Feb 2026 10:00:00 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Guide</category>
      <description>Missing values, outliers, encoding — a practical checklist for preparing your dataset before training.</description>
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      <title>Federated Learning: Privacy-First ML at Scale</title>
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      <pubDate>Thu, 22 Jan 2026 10:00:00 GMT</pubDate>
      <dc:creator>PhaBeta Team</dc:creator>
      <category>Research</category>
      <description>How federated learning lets you train models across distributed data without compromising privacy.</description>
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