An AI model doesn't just learn on its own; it follows a protocol. The settings of that protocol, like the "learning rate", are called hyperparameters. In this episode, we explain what these crucial settings are, why they are the 'art' of AI development, and how they help you judge the quality of a research paper.

#Hyperparameters #AIinHealthcare #MachineLearning #ClinicalAI #HealthTech #DataScience #DeepLearning #MedicalEducation #MedEd #ai in medicine Music generated by Mubert https://mubert.com/render

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Imagine trying to find the lowest point in a valley while blindfolded. How would you do it? The same way an AI finds the best answer: one step at a time, always moving downhill. This process is called "gradient descent," and it's one of the engines that powers machine learning. In this episode, we explain how it works, what the "learning rate" is, and why it matters for understanding AI research.

#GradientDescent #AIinHealthcare #MachineLearning #ClinicalAI #HealthTech #AIexplained #DeepLearning #MedicalEducation #MedEd #ai in medicine

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

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The UK has just launched a star-studded National Commission to rewrite the rulebook for AI in the NHS. The goal: faster, safer innovation for patients. It could be a powerful accelerator and will hopefully avoid the pull of becoming another talking shop lost in bureaucracy.

#HealthAI #AIinHealthcare #DigitalHealth #NHS #HealthTech #Regulation #MHRA #Innovation #MedTech #PatientSafety #FutureofHealthcare #UKInnovation

#ai in medicine Music generated by Mubert https://mubert.com/render

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How does an AI model quantify a mistake? It uses a "loss function" – a scorecard that penalises different types of errors. In this episode, we explain what a loss function is, why it's not a one-size-fits-all tool, and how it reveals the true clinical priorities of any AI model. A crucial concept for critically appraising new research.

#LossFunction #AIinHealthcare #MachineLearning #ClinicalAI #HealthTech #DigitalHealth #MedicalEducation #MedEd #CriticalAppraisal #ai in medicine Music generated by Mubert https://mubert.com/render

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How does an AI model actually learn to spot disease on a scan? It all comes down to one fundamental goal: minimising error. In this episode, we kick off a new set of episodes on the mechanics of machine learning by explaining this core principle with a simple clinical analogy that will change how you look at AI. Understanding this is the first step to critically appraising any research paper that lands on your desk.

#ArtificialIntelligence #AIinHealthcare #MachineLearning #ClinicalAI #AIinMedicine #HealthTech #DigitalHealth #MedicalEducation #MedEd #ErrorMinimisation #ai in medicine Music generated by Mubert https://mubert.com/render

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Shmatko, A., Jung, A.W., Gaurav, K. et al. Learning the natural history of human disease with generative transformers. Nature (2025).

Link to paper: https://www.nature.com/articles/s41586-025-09529-3

What if an AI could forecast your health like the weather? A groundbreaking new model called Delphi-2M, published in Nature, claims to do just that — predicting your risk for over 1,000 diseases using technology similar to ChatGPT.

But is this the dawn of a new era in preventive medicine, or a high-tech crystal ball that reflects the biases of our current system?

In this week's episode of The Health AI Brief, we put Delphi-2M through a real-world stress test. We break down the impressive science, interrogate its biggest weakness (generalisability), and define the critical next step needed before a tool like this could ever see the light of day in the NHS.

Tune in to find out if this is a future game-changer or a promising idea that's not quite ready for primetime.

#HealthAI #DigitalHealth #PredictiveMedicine #AIinHealthcare #GenerativeAI #NHS #Delphi2M #FutureofMedicine #UKBiobank #ai in medicine Music generated by Mubert https://mubert.com/render

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We break down a landmark UCL study on the NHS's £21m programme to deploy AI in chest diagnostics.

They uncover the real reasons for significant delays, moving beyond the technology to the critical, real-world barriers: staff capacity, fragmented IT infrastructure, and complex governance. Find out why dedicated project management is the secret to success and what this means for the future of AI in healthcare.

Essential listening for any clinician, manager, or policymaker involved in digital transformation.

Paper:

Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation

Ramsay et al, eClinicalMedicine

https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(25)00414-6/fulltext

Health AI, NHS, Digital Health, AI Implementation, Radiology AI, Chest Diagnostics, Healthcare Technology, Clinical AI, Change Management, NHS Innovation, Digital Transformation, UCL Study, The Lancet, AI in medicine. Music generated by Mubert https://mubert.com/render

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Ever been handed a patient's CT scan on a CD-ROM and wondered why medical systems struggle to communicate? The problem is that they need to speak the same language. This episode decodes the three essential standards of medical data exchange.

We break down DICOM (the "courier package" for images), HL7 (the "digital fax machine" for classic hospital data), and FHIR (the modern "smartphone app" enabling a connected future). Understand these acronyms to better appraise AI research and see the path forward for a truly interoperable healthcare system.

#DICOM #HL7 #FHIR #Interoperability #HealthInformatics #DataStandards #EHR #HealthcareAI #MedicalAI #DigitalHealth #HealthTech #ai in medicine Music generated by Mubert https://mubert.com/render

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How do we know if a medical AI has truly learned to spot disease, or just memorised the answers to its practice questions? The same way we evaluate a trainee: with a final, unseen exam.

This crucial process involves splitting data into three sets: training data (the textbook), validation data (the mock exam), and test data (the final exam). In this episode of The Health AI Brief, we explain why this split is our best defence against overconfident AI, what 'overfitting' means for clinical practice, and why the 'test set' result is the only number you should trust when appraising a new AI study.

#TrainingData #ValidationData #TestData #Overfitting #ModelValidation #ArtificialIntelligence #MachineLearning #HealthcareAI #MedicalAI #ClinicalAI #CriticalAppraisal #EvidenceBasedMedicine #DigitalHealth #ai in medicine Music generated by Mubert https://mubert.com/render

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How do you teach an AI to read a chest X-ray? The same way a consultant teaches a resident doctor on a ward round: you point, you trace, and you provide the correct answer.

This is data annotation, the meticulous, human-led process of "teaching" an algorithm by labelling thousands of examples. In this episode of The Health AI Brief, we explain why the quality of this digital teaching is everything. Discover why you should always ask who did the labelling when reading a new study, how human disagreement limits AI certainty, and why this is becoming a vital new form of clinical practice.

#ai in medicine #DataAnnotation #DataLabelling #SupervisedLearning #GroundTruth #MedicalAI #HealthcareAI #ExpertAnnotation #AIinRadiology #ClinicalAI #ArtificialIntelligence #MachineLearning #DigitalHealth #HealthTech Music generated by Mubert https://mubert.com/render

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