The content on this page was current at the time of publication — please check the date/time stamp on this page.

• Diabetes is a long-term (chronic) condition where the level of sugar (glucose) in your blood is too high. nhsinform.scot
• There are several types, but the two most common are:
  • Type 1 diabetes — where the body does not produce insulin.
  • Type 2 diabetes — where the body does not use insulin properly, or doesn’t make enough. nhsinform.scot
• There is also pre-diabetes (sometimes called non-diabetic hyperglycaemia) — this is when blood sugar is higher than normal but not high enough to be diagnosed as diabetes. nhsinform.scot

• According to Diabetes UK, there are 4.6 million people in the UK with a diagnosis of diabetes, and millions more with pre-diabetes. Diabetes UK
  
• About 90% of diagnosed diabetes in the UK is Type 2; around 8% is Type 1, and the remaining ~2% are other, less common types. Diabetes UK
  
• In England, there’s a significant number of people who are undiagnosed — an ONS analysis suggests that around 30% of people with Type 2 diabetes are not aware they have it. Diabetes UK
  
• For pre-diabetes, NHS data shows hundreds of thousands more people are at risk: in 2023, over half a million more people in England were identified as pre-diabetic. NHS England

Diagnosis

• People are usually diagnosed via blood tests, such as HbA1c (a measure of average blood sugar over several months), or fasting glucose tests.
• For pre-diabetes, blood tests and risk assessments by GPs help identify people at risk.

Lifestyle Changes

• Managing or preventing Type 2 diabetes often starts with lifestyle: healthy eating, weight loss, physical activity, and reducing sugar intake.
• The NHS offers structured programmes (like the NHS Diabetes Prevention Programme) to support people at high risk. NHS England

Medication

• Depending on type and severity, people with diabetes may be prescribed insulin (Type 1) or medications such as metformin, sulfonylureas, or newer agents (Type 2) to control blood sugar.

Monitoring & Care

• People with diabetes typically have regular reviews with their GP or diabetes specialist.
• They receive a set of “care processes” each year: blood sugar check, blood pressure, cholesterol, kidney function, foot checks, eye screening etc. According to the National Diabetes Audit, uptake of these care checks has improved over time. GOV.UK
• Annual diabetic eye screening is offered via the NHS Diabetic Eye Screening Programme (DESP) to detect diabetic retinopathy.

AI is being used in several research and pilot projects across the UK to improve how diabetes is detected, monitored, and managed. Here are some examples:

1. Predicting Risk from ECGs
   • A new AI tool called AIRE-DM (developed by Imperial College) analyses ECG (heart) readings from routine scans to detect subtle changes that might indicate a risk of developing Type 2 diabetes, up to 10-13 years in advance. Imperial College Healthcare NHS Trust+1
   • Early detection like this could allow people to make lifestyle changes before the disease develops or progresses.
     
     
2. Diabetic Eye Disease (Retinopathy) Screening
   • AI systems such as EyeArt (by Eyenuk) can automatically analyse retinal (eye) images for signs of diabetic retinopathy — the algorithm is evaluated to be safe and cost-effective per the UK National Screening Committee. eyenuk.com
   • A newer model from King’s College London uses NHS retinal imaging data to predict who is at high risk of sight-threatening diabetic retinopathy up to 3 years ahead, potentially allowing for more personalised eye screening schedules. King's College London
   • NICE (UK) has also reviewed AI technologies (EyeArt, RetinaLyze, Retmarker) for diabetic retinopathy screening, noting their potential to reduce manual workload. NICE
      
3. Predicting Kidney Disease via Eye Images
   • Research from the University of Dundee (UK) shows AI can analyse routine diabetic eye screening photos (retinal images) to predict future risk of kidney disease in people with Type 2 diabetes, even before traditional kidney tests flag problems. Diabetes UK
     ​ 
4. Risk Prediction Models Using Machine Learning
   • Using data from the UK Biobank, researchers have built a prediction model for 10-year risk of Type 2 diabetes that only needs information that can be collected via a smartphone. arXiv
   • Another automated machine-learning framework (AutoPrognosis 2.0) used UK Biobank data to build a risk score for diabetes that may outperform some traditional clinical risk scores. arXiv

Here are some of the promising ways AI could change diabetes care in the UK in the coming years:

1. Very Early Risk Identification
   • AI tools that predict diabetes years before onset (e.g., from ECGs) could help people take preventive action long before they develop high blood sugar.
   • Combined with risk models, AI could personalise lifestyle advice based on an individual’s unique risk profile.
      
     
2. Individualised Eye Screening
   • Rather than everyone having the same annual eye scan, AI risk models could tailor screening intervals: people at low risk might need less frequent checks, while high-risk individuals could be seen sooner.
   • This would optimise NHS resources and reduce patient burden.
      
     
3. Early Detection of Complications
   • Using retinal images to predict kidney disease is a powerful example. In future, similar AI tools might predict other diabetic complications, like cardiovascular disease, nerve damage, or foot problems, enabling earlier intervention.
      
     
4. Continuous Monitoring Through Wearables
   • AI could be integrated with wearable devices (e.g., smartwatches or glucose monitors) to provide real-time risk alerts or trend warnings (such as rising glucose) — this could support both people with Type 1 and Type 2 diabetes.
      
     
5. Decision Support for Clinicians
   • AI could help clinicians decide which medications or treatment strategies might be most effective based on a patient’s risk profile, history, and predicted trajectories.
     ​ 
     
6. Precision Medicine
   • By combining genetic data, imaging (such as retinal scans), and clinical data, AI could help stratify people into subtypes of diabetes. This might guide more precise treatment (which drug, what dose, when to intensify).

• Accuracy and Bias: AI models must be trained and validated on diverse UK populations; otherwise they could underperform for some demographic groups.
   
  
• Regulation and Safety: Not all AI tools are fully approved or deployed; they need rigorous clinical trials and regulatory oversight.
   
  
• Data Privacy: Using sensitive health data (ECG, retinal scans, genomic data) requires strong data protection, secure storage, and patient consent.
   
  
• Integration into NHS: Large-scale implementation can be difficult — tools need to integrate with NHS systems, workflows, and budgets.
  ​ 
  
• Over-reliance: Clinicians must be careful not to rely solely on AI predictions — AI should support, not replace, clinical judgement.

• Diabetes is common in the UK, especially Type 2, and many people remain undiagnosed.
• Traditional care involves blood tests, regular monitoring, lifestyle change, and treatment.
• AI is already being used in research and pilot projects — for example, to predict diabetes risk from ECG, to screen for eye disease, and even to forecast kidney disease from retinal images.
• In the future, AI could enable earlier detection, more personalised screening, better monitoring, and smarter treatment decisions.
• But for these benefits to become widespread, challenges around accuracy, regulation, data protection, and NHS integration must be addressed.

• NHS Inform – About diabetes nhsinform.scot
  
• Office for National Statistics / Diabetes UK – undiagnosed Type 2 diabetes Diabetes UK
  
• NHS England – people at risk of Type 2 diabetes (pre-diabetes figures) NHS England
  
• Imperial College Healthcare NHS Trust – AI prediction of Type 2 diabetes from ECGs Imperial College Healthcare NHS Trust
  
• NICE Medtech Innovation Briefing – AI in diabetic retinopathy screening NICE
  
• King’s College London – AI model for predicting diabetic retinopathy risk King's College London
  
• Diabetes UK / University of Dundee – AI predicting kidney disease from eye images Diabetes UK
  
• UK Biobank / Machine Learning risk model for Type 2 diabetes arXiv
  
• AutoPrognosis 2.0 – prognostic modelling for diabetes arXiv
  

The content on this page was current at the time of publication — please check the date/time stamp on this page.

Hypertension, or high blood pressure, is when the pressure of the blood flowing through your arteries is consistently too high. Blood pressure is written as two numbers:

• Systolic pressure (the top number) - pressure when the heart beats.
• Diastolic pressure (the bottom number) - pressure when the heart relaxes.

In the UK, high blood pressure is usually diagnosed when readings are 140/90 mmHg or above in a clinic, or 135/85 mmHg or above when measured at home.
Reference: NHS — High blood pressure (hypertension)

Hypertension often has no symptoms, but it increases the risk of heart attack, stroke, kidney disease and other serious conditions.
Reference: British Heart Foundation — High blood pressure

Around one in three adults in the UK has high blood pressure.
Reference: Blood Pressure UK — Blood pressure facts and figures
​
In England, roughly 29% of adults with high blood pressure are undiagnosed, meaning millions of people are unaware they have it.
Reference: Office for National Statistics — Risk factors for undiagnosed high blood pressure (2015–2019)

1. Measuring and Diagnosing
Blood pressure is checked in GP surgeries, pharmacies or during NHS Health Checks. When needed, doctors may use ambulatory blood pressure monitoring (ABPM) — a device you wear for 24 hours.
Reference: NHS — How high blood pressure is diagnosed

2. Lifestyle ChangesPeople are usually advised to:

• Reduce salt intake
• Stay active
• Achieve a healthy weight
• Limit alcohol
• Stop smoking

These steps can significantly lower blood pressure.
Reference: NHS — Prevention of high blood pressure

3. Medication
If lifestyle changes don’t fully control blood pressure, medicines such as ACE inhibitors, calcium-channel blockers or diuretics may be prescribed.
Reference: NICE Guidelines — Hypertension in adults: diagnosis and management

4. Ongoing Monitoring
​Regular reviews with a GP or nurse help ensure blood pressure remains within a safe target range tailored to the individual.
Reference: NHS — Treatment for high blood pressure

AI is not yet routine NHS practice for hypertension, but UK-based research is showing promising uses.

​1. Predicting Hypertension from ECG Results
Researchers at Imperial College Healthcare NHS Trust have developed an AI system that analyses ECG (heart tracing) results to predict the risk of developing high blood pressure or related complications.
Reference: Imperial College Healthcare NHS Trust — AI model predicts high blood pressure

2. Analysing Eye Images to Estimate Blood Pressure
A deep-learning tool trained on UK Biobank retinal images has shown it can estimate systolic blood pressure and predict future cardiovascular risk.
Reference: UK Biobank — AI analysis of retinal images and blood pressure

3. Brain Imaging and Machine Learning
A UK Biobank study found that machine-learning models can use brain imaging data to identify patterns linked with current and future blood pressure levels.
Reference: Cardiovascular Research (Oxford University Press) — Brain signatures of blood pressure

4. Wearable Devices Combined with AI
​UK research teams are developing machine-learning models that estimate blood pressure using signals from smart wristbands and wearable sensors.
Reference: UK Biobank — Blood pressure estimation using smart devices

​1. Earlier Detection
AI tools could screen large groups of people using ECGs, eye images or wearable data to identify those at risk sooner — helping reduce the number of undiagnosed cases.

2. More Personalised Care
AI could combine genetic information, imaging and health history to predict who is most likely to develop complications such as stroke or heart disease, supporting more tailored treatment plans.

3. Continuous Blood Pressure Monitoring
Future wearable devices using AI may track blood pressure more accurately throughout the day, giving patients and doctors better insight into real-time changes.

4. Better Self-Management Tools
AI-enabled apps may offer personalised advice on diet, activity and lifestyle based on an individual’s trends and risk profile.
​
5. Clinical Decision Support
​AI may assist GPs by suggesting which medicines are most likely to work for a particular patient or by highlighting early signs of deterioration.

References

• NHS. High blood pressure (hypertension).
• British Heart Foundation. High blood pressure information.
• Blood Pressure UK. Blood pressure facts and figures.
• Office for National Statistics. Risk factors for undiagnosed high blood pressure in England (2015–2019).
• NICE Guideline NG136. Hypertension in adults: diagnosis and management.
• Imperial College Healthcare NHS Trust. AI model predicts high blood pressure from ECGs.
• UK Biobank. AI analysis of retinal images relates to blood pressure.
• Cardiovascular Research (Oxford). Functional brain connectivity and blood pressure.
• UK Biobank. Blood pressure estimation research using smart wristbands.

At You ID Me, we know that many people are thinking about how companies use artificial intelligence (AI) and what it means for their personal information. This is especially important when it comes to emergency medical data, where accuracy and reliability can make a real difference.
​ 
This short guide explains exactly how we use AI – and, just as importantly, how we don’t use it.

Your emergency medical profile is created by you, in your own words.
 
When a paramedic or first responder scans your You ID Me product, they see exactly the information you provided, exactly as you entered it.
​ 
We do not use any form of AI to interpret, rewrite, summarise or modify your medical details.
This is a deliberate choice.

• AI can sometimes make mistakes or “hallucinate”, which means it may generate information that isn’t correct.
• In an emergency, even small inaccuracies could be dangerous.
• By avoiding AI in this part of our system, we ensure there is zero margin for AI-generated errors.

This means your emergency information remains accurate, reliable and fully under your control.

Although your emergency data is never handled by AI, we use helpful AI tools in two specific, safe areas:
 
1. Speak4Me (Text-to-Speech)
Our Speak4Me feature reads parts of your medical profile aloud for a first responder. To do this, we use Google’s text-to-speech technology.

• It only reads the exact words you have written.
• It does not analyse, change or add to your information.
• It simply converts text to a clear spoken voice.

This can help a responder quickly understand essential details if you’re unable to communicate.

2. Website Chatbot
We also use a learning chatbot on our website.

• It is used only for general customer questions and support.
• It never has access to your emergency profile or personal medical information.
• It cannot change or influence your stored details in any way.

This tool is simply there to help you find answers more quickly when browsing our site.

We use AI only where it is safe, appropriate and useful — and never where it could interfere with your emergency information.

Your safety and privacy come first, and the data a paramedic or first responder sees will always be:

• exactly what you entered,
• unchanged, and
• free from AI interpretation.

If you have any questions about how we handle your information, we’re always happy to help.