In our last episode, we described Machine Learning as the engine that powers AI. So, what is the specific output of that engine? What is created when an AI is "trained"?

The answer is the "model." This is the end product of the training process—the distilled knowledge, captured in digital form.

In this episode, we explain what an AI model is and why it's not the whole product. We cover why a model is a 'frozen' snapshot of knowledge, the danger of 'model drift,' and how biased data creates a flawed tool.

Understanding this will equip you to evaluate the whole car, not just the engine, the next time you see a new AI solution.

🎧 Listen to Episode 004: "Models" now and learn what questions to ask before you trust the black box.

Music generated by Mubert https://mubert.com/render

#AIModel #ModelDrift #ResponsibleAI #MedEd #HealthTech #AIinHealthcare #DataBias #ClinicalAI #Podcast

A Deep Dive into the OpenAI & Penda Health AI Study

In this episode, we provides a critical analysis of the highly publicised paper, "AI-based Clinical Decision Support for Primary Care: A Real-World Study."

We go beyond the abstract's impressive claims to dissect the real-world implications:

- The Design: Acknowledging the brilliant workflow integration and "on-the-job" training effect.

- The Data: Why a 60% baseline error rate for "inappropriate treatment" raises serious questions.

- The Business Case: The hard trade-off between a 3.5-minute increase in consultation time and no measurable change in patient outcomes.

- The Verdict: Is this a template for the future or a case of "marketing over medicine"?

Listen now for a pragmatic, no-nonsense verdict on whether this promising tool is truly ready for the real world.

Music generated by Mubert https://mubert.com/render

#AIinHealthcare #DigitalTransformation #ClinicalDecisionSupport #PendaHealth #OpenAI #LLMs #HealthTech #AIConsult #GPT-4o

In the last episode we called AI the "toolbox." Now, we're looking at the most powerful tool inside: Machine Learning (ML).

So, what’s the real difference? Think of it this way: AI is the destination, but Machine Learning is the engine getting us there.

In this episode, we break down the simple but powerful "Train, Detect, Predict" framework. This is the key to understanding how these tools actually work and, more importantly, where they can fail.

Master this, and you'll know exactly what critical questions to ask the next time you encounter a new "AI" solution in your practice.

🎧 Listen to Part 2: "AI vs. Machine Learning" now and feel empowered, not confused.

Music generated by Mubert https://mubert.com/render

#AIvsML #MachineLearning #MLinHealthcare #DataScience #MedEd #HealthTech #AIinHealthcare #DigitalTransformation #ClinicalAI #Podcast