AI in healthcare feels is reaching a turning point. AI companies are either continuing to grow or starting to fold. The only companies that will succeed are the ones producing real results. We are seeing real prospective trials, funding for new care models, and serious conversations about governance, not just hype. Sure, a lot of it’s probably a bubble, but isn’t it exciting? AI is becoming a measurable clinical intervention and managerial force.

Inside this issue:

• News that real trials and funding are stepping up
• Research pushing beyond simulation into randomized evidence
• Ethics & regulation debates heating up globally
• Practical tools you can start trying this week

Let’s dive in.

🧑‍⚕️ Epic expands ambient documentation with generative AI partners: Epic announced deeper rollout of ambient clinical documentation using large language models integrated directly into workflows, with early health system data showing reduced after-hours charting and faster note completion. What caught my eye was how tightly this is being embedded into existing Epic workflows rather than as a bolt-on tool.
Why this Matters: This is one of the clearest examples yet of AI reducing cognitive load without asking clinicians to change how they practice.

🩺 AI-Enabled Stethoscope Doubles Detection of Valvular Disease: A new study in the European Heart Journal - Digital Health showed that an AI-assisted digital stethoscope more than doubled sensitivity for moderate to severe valvular heart disease compared with a traditional stethoscope in routine clinical exams, suggesting a powerful new screening layer for primary care.

🧪 Google and Included Health Launch Nationwide Randomized AI Study: For the first time, a large randomized trial will test conversational AI’s performance in real-world virtual care workflows across diverse patient settings, aiming to provide rigorous evidence beyond feasibility and simulated environments.

🤖 Novel AI foundation model for brain MRI diagnostics: A Nature Neuroscience study details BrainIAC, a self-supervised, generalist AI model pretrained on tens of thousands of brain MRI scans that can perform multiple clinical tasks with high accuracy.
Key Takeaway: AI models that generalize across tasks can unlock more utility from existing imaging data with limited labeled training.

🧠 Agentic AI governance blueprint for healthcare: With agent-based AI spreading across documentation, monitoring, and assistant workflows, researchers propose unified lifecycle management to ensure auditability and control.
Key Takeaway: Governance frameworks will be essential to safely scale AI beyond pilots.

📜Telemedicine association updates balanced AI policy principles: The ATA released a policy framework emphasizing validation, monitoring, explainability, and bias safeguards. The guidelines create a clinician-friendly perspective on ethical deployment.

🧑‍⚖️ Ontario issues privacy-focused AI healthcare guidance: This workshop-driven guidance emphasizes governance, bias mitigation, and accountability for AI scribes and other clinical tools under Ontario privacy law as a practical blueprint for procurement and deployment.

• What it does: Helps triage and refine differential diagnoses with context-aware questioning.
• How I use it: I use it to structure my thinking on complex cases before consult, so I enter sessions with a clearer plan.

Prompt to try:

Ok, it’s easy, but this is a basic example. Be sure to limit PHI.

I talk a lot about administration. Probably because I have to deal with them every day and i lose hours off my life every time I do. But AI has the potential to make our lives so much easier. I’m struck by how evidence generation is now front and center in healthcare AI. Its not just demos or pilot metrics but true randomized evaluations. That is exciting but also a sober reminder: AI must prove its worth in real care before we take it for granted. Think about where you can build evidence in your own workflows.

Tip of the week: Use these tools every day. Just 30 minutes of daily use will make you a pro in no time.