In this episode, we explore a powerful new white paper from the World Economic Forum and Boston Consulting Group that outlines how AI could reshape global healthcare.

But the future isn’t just about technology—it’s about leadership, trust, and collaboration.

We break down:

• Why healthcare leaders still hesitate to adopt AI

• How strategic misalignment and regulatory uncertainty hold back progress

• Six key calls to action that could unlock real-world value—now, not someday

From care delivery to operational transformation, this episode unpacks how public-private collaboration could be the key to building a healthier, more equitable world through AI.

In this special episode, we unpack one of the most comprehensive roadmaps yet for the future of AI in medicine.

Drawn from the newly published 2025 review, “Navigating the Endless Frontier”, we explore:

• Foundation models for medical imaging, EMRs, and diagnostics

• AlphaFold 3 and the limits of protein/RNA prediction

• Surgical robots and the LASR autonomy scale

• Brain–Computer Interfaces (BCI) and OpenAI integration

• AI in reproductive health, dementia prediction, and smart elder care

From smart embryo selection to real-time heart disease detection, this isn’t sci-fi—it’s happening now.

In this episode, we delve into the National Academy of Medicine's draft AI Code of Conduct, exploring its implications for healthcare. We discuss the proposed principles and commitments designed to ensure the ethical, safe, and effective integration of AI in health and biomedical sciences. Join us as we unpack the framework aiming to guide stakeholders toward responsible AI adoption in healthcare settings.

🚨 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

AI is revolutionizing medicine—but are we thinking deeply enough about what happens when it goes wrong?

In this episode, we break down a landmark paper that explores the ethical and legal minefields of using AI in healthcare. From algorithmic bias to economic disruption and the clash between innovation and accountability, we explore what responsible AI should look like. The conversation spans global legal efforts—from the EU to Brazil—and asks one critical question: How do we keep AI human-centered in a system built for scale and speed?

This research paper reviews and examines the increasing use of artificial intelligence (AI) in advanced medical imaging. It specifically concentrates on deep learning techniques for image reconstruction in modalities such as MRI, CT, and PET. The study discusses the workflows, technical developments, clinical applications, and challenges associated with AI-driven medical imaging. It explores various neural network architectures, data preparation methods, and loss functions used in this domain. The paper also highlights the potential for AI to improve imaging speed, reduce radiation exposure, and enhance image quality. Ultimately, the review emphasizes AI's capacity to advance medical imaging, paving the way for better clinical diagnosis and treatment, while acknowledging existing limitations such as interpretability and generalizability.