You hear about new AI models having "billions of parameters." It sounds impossibly complex, but the core idea is surprisingly simple and it's the single most important concept for understanding how an AI actually works. These parameters determine an AI's capabilities, its limitations, and its potential for bias.

In this episode of The Health AI Brief we'll cover:

- A simple, intuitive analogy for what a 'parameter' actually is.

- How the process of 'training' an AI is really just about adjusting these billions of settings.

- Why understanding this concept helps you cut through the hype and critically appraise the AI tools being marketed to clinicians.

This is a foundational concept. Grasp this, and you'll have a much clearer view of the technology poised to change our practice.

AI in Healthcare, AI Parameters, Machine Learning, Neural Networks, Large Language Models (LLM), Healthtech, Digital Health, Clinical AI, AI Training, Model Architecture, Artificial Intelligence, Medical AI Explained.

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How can an AI analyse a CT scan or pathology slide with expert-level accuracy? The answer is Deep Learning—the engine behind the revolution in medical imaging.

In this episode, we explain how these 'deep' neural networks teach themselves to see complex patterns, much like our own visual cortex processes information from simple edges to complex objects.

Knowing this matters. It helps you understand why these models are often "black boxes," why appraising their training data is so critical, and why they are hyper-specialised for a single task.

🎧 Listen now to understand the technology powering the next generation of medical imaging tools.

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#DeepLearning #AIinMedicine #MedicalImaging #Radiology #Pathology #HealthTech #NeuralNetworks #ClinicalAI #MedEd

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Today we're discussing AI-powered physiotherapy app from Flok Health which has seen widespread media coverage and has gained both CQC and MHRA approval, promising to slash waiting lists for back pain.

The goal is compelling: automate care for straightforward cases to free up human clinicians for complex ones. But what does the evidence really say?

In this 5-minute analysis, I break down:

  - The core challenge of validating safety, especially around "red flag" screening.

  - Why pilot studies on NHS staff, while promising, aren't enough.

  - The specific, robust evidence needed to justify widespread adoption.

Is this the future of musculoskeletal care or a solution built on speculative claims? My verdict: Keep a Close Eye on This.

Listen to the full episode to understand the critical next steps for turning this promising idea into a trusted clinical tool.

#HealthAI #DigitalHealth #NHSInnovation #Physiotherapy #HealthTech #ClinicalEvidence #MedTech #BackPain #AIinHealthcare #DigitalTransformation #Flok

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When we say a machine "learns" from data, what's actually happening? What is the engine doing?

In this episode, we break down the fundamental building block of modern AI: the Neural Network. We explain it as a logical chain for synthesising information—from raw data like ECG signals in the 'input layer', to abstract concepts like 'high-risk' in the 'hidden layers', to a final diagnosis.

Understanding this basic architecture is key to knowing an AI's limits. It reveals why these systems are powerful pattern-finders but don't "understand" causality; a crucial distinction for clinical safety.

🎧 Ready to look under the bonnet of AI? Listen now to grasp the core concept behind almost every AI tool you'll encounter.

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#NeuralNetworks #AIinHealthcare #MachineLearning #MedEd #HealthTech #DigitalHealth #DataScience #ClinicalAI

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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.

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#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.

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#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.

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

AI is "better than a radiologist." AI is writing your clinic notes. The term 'AI' is everywhere in medicine, but what does it really mean? It's easy to feel like it's just a confusing buzzword.

In Part 1 of our new series, we cut through the hype to give you a clear, simple definition.

Think of AI as the broad toolbox aiming to mimic human intelligence, from seeing to understanding language. But to really understand it, you need to know about the engine that powers most of these tools.

🎧 Listen to "What is AI, Anyway?" now to get the foundation right.

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#AIinHealthcare #DigitalHealth #MedEd #HealthTech #ClinicalAI #FutureofMedicine #Podcast #ArtificialIntelligence #Doctors #Nurses

Welcome to The Health AI Brief.

In the first episode, we set the stage for our journey: to demystify artificial intelligence for busy clinicians

Roughly five minutes and one concept at a time.

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