🚨 AI in Clinical Diabetes Decision-Making — What’s Just Hype vs. Real Help?

A new Nature paper just dropped:
📄 Artificial Intelligence in Clinical Decision Support: Applications, Challenges, and Future Directions
👉 Read Full PDF Here

This one’s going to set the tone for how hospitals and health systems adopt AI in 2025 and beyond.

🧠 Key insights:

• Why most AI tools still struggle to get past the pilot stage

• What “explainability” really means to a clinician at the bedside

• The ethical risk of AI recommending treatments without accountability

💬 My question to you:
What’s one thing you think AI should never replace in healthcare?

Let’s talk 👇

#AIinHealthcare #DigitalHealth #HealthTech #ClinicalAI #FutureOfMedicine #NatureDigitalMedicine

AI doesn’t just predict anymore—it double-checks the doctor.

How do we know a diagnosis is accurate, a surgery went right, or a patient received the right care? Enter: AI-powered quality assurance.

In this episode, we explore how AI is transforming patient safety—across diagnostics, pathology, surgery, and more. From advanced lesion detection during endoscopy to precision in pathology, AI is already outperforming human baselines in critical ways. But what stands in the way of full adoption? We also unpack the hard stuff: data standards, explainability, and ethical oversight.

No lectures. No theory. Just code, datasets, and real-world healthcare problems.

This week on AI in Medicine, we explore a trainee-led case study where future doctors learned Python, value-based care analytics, and responsible GenAI—all through hands-on data challenges.

These aren’t hackathons for show. They’re how we build a new kind of physician:
🧠 Clinically sharp
💻 Data-literate
🧭 Ethically grounded

🎙️ AI Datathons in Medical Education: A Trainee-Led Case Study