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

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

healthaibrief@outlook.com

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.

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

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

healthaibrief@outlook.com

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