AI and Wearable Technology in Healthcare in 2025

The rapid development of AI and wearable technology in healthcare is transforming the way both patients and healthcare providers approach monitoring and treatment.

By combining real-time data from wearable devices with powerful AI algorithms, healthcare professionals can identify trends, predict health issues, and deliver more personalized care.

This technology enables continuous monitoring of vital signs and movement, offering insights that previously required frequent clinical visits.

In this article, you’ll explore how the integration of AI and wearable devices is reshaping care, improving outcomes, and enhancing the overall patient experience.

AI and Wearable Technology in Healthcare: TL;DR

• AI and wearable technology are working together to improve healthcare by providing real-time health monitoring and personalized insights.
• These tools enable earlier intervention and support proactive care, especially for chronic disease management.
• Wearable devices collect data such as heart rate, activity levels, and sleep patterns, while AI analyzes this information to assist clinicians in diagnosis and treatment planning.
• The growing adoption of AI in healthcare is facilitating large-scale health studies and contributing to better patient outcomes.

What Is AI Wearable Technology and How Does It Impact Modern Healthcare?

AI wearable technology refers to electronic devices that are worn on the body and use artificial intelligence to collect, analyze, and sometimes act on health data.

Examples include smartwatches, fitness trackers, and medical sensors.

The key features of AI wearables are:

• Continuous health data collection
• Real-time monitoring and analysis
• Personalized health feedback

Healthcare providers benefit from the data AI wearables generate.

These devices enable professionals to track vital signs and detect early warning signs of health changes, such as irregular heart rhythms or fluctuations in glucose levels.

IoT in healthcare (Internet of Things) is the integration of internet-connected devices and sensors throughout the medical field. The integration of AI enhances the capabilities of wearables. 

AI algorithms process the large amounts of data collected, identify patterns, and offer individualized recommendations.

Wearable Type

Data Collected

Potential Use

Smartwatch

Heart rate, activity

Fitness, arrhythmia alerts

Glucose Monitor

Blood sugar

Diabetes management

Fitness Tracker

Steps, sleep quality

Lifestyle tracking

Patients also receive more engaging and tailored health experiences.

Devices can remind them to take medication, encourage physical activity, and provide dietary advice based on behavior patterns.

These technologies support the shift towards preventive, personalized healthcare.

The use of AI in preventive healthcare means the facilitation of earlier interventions and more efficient care coordination between patients and providers.

Types of Data Collected by Wearable Devices and How AI Analyzes It

Wearable healthcare devices measure many different physiological parameters.

Common types of data include:

• Heart rate and heart rhythm (EKG readings)
• Respiration rate and breathing patterns
• Body temperature
• Blood oxygen saturation (SpO2)
• Physical activity levels, such as steps taken or distance traveled
• Sleep duration and sleep stages
• Blood glucose levels (from specific monitors)
• Fall detection and location data

AI algorithms process this raw data to detect trends and anomalies.

For example, changes in heart rhythm picked up by a sensor can trigger alerts for irregular heartbeats.

Machine learning models are trained to recognize normal patterns and deviations based on data from large groups of users.

Physical activity data is analyzed to assess exercise habits or sedentary behavior. AI tools can classify the intensity of movement, estimate calorie burn, and provide personalized recommendations.

Sleep data from wearables is processed by AI to distinguish light, deep, and REM sleep stages. This helps users and clinicians better understand sleep quality.

Continuous glucose monitors collect blood sugar data at frequent intervals. AI algorithms look for patterns, predict potential hypoglycemic events, and may recommend insulin dosage adjustments for diabetes management.

Table: Examples of Wearable Data and AI Analysis

Data Type

How AI Analyzes It

Heart rate & EKG

Detects arrhythmias, predicts risks

Activity monitoring

Tracks steps, classifies activity type

Sleep metrics

Identifies sleep stages, flags disruption

Body temperature

Spots fever patterns, monitors changes

Blood glucose

Predicts trends, suggests interventions

4 Benefits of AI-Powered Wearable Technologies in Healthcare

The benefits of AI-powered wearable technology in healthcare are manifold. Let’s take a look at four of these:

1. Continuous Monitoring of Chronic Conditions

AI-powered wearable devices are transforming how chronic conditions are managed by enabling 24/7 health monitoring. These smart wearables continuously collect vital data such as heart rate, blood glucose levels, oxygen saturation, and blood pressure. The data is analyzed in real time to detect irregularities that may indicate early signs of deterioration or health risks.

Key benefits:

• Reduces the need for frequent clinic visits
  
  
• Enhances early detection of complications
  
  
• Empowers patients to stay proactive about their health

Examples:

• Diabetic patients using continuous glucose monitors (CGMs) that send alerts for hypo- or hyperglycemia
  
  
• Heart failure patients wearing devices that detect abnormal heart rhythms or fluid retention

This continuous stream of data ensures healthcare providers are always informed, allowing them to intervene early and adjust care plans as needed.

2. Personalized Treatment Plans

One of the major advantages of integrating AI with wearable technology is the ability to deliver personalized healthcare. By analyzing individual health patterns over time, AI can tailor treatment plans to a patient’s specific needs, making care more effective and responsive.

How it works:

• AI analyzes metrics such as sleep, activity levels, and medication responses
  
  
• Health trends are compared with medical history and population data
  
  
• Dynamic treatment adjustments can be made in real time

Examples:

• Adjusting hypertension medications based on daily blood pressure fluctuations
  
  
• Modifying a fitness or diet plan based on caloric burn, sleep quality, and heart rate variability

This personalized approach leads to better outcomes, improved medication adherence, and higher patient satisfaction.

3. Enhanced Early Detection

AI algorithms are particularly adept at identifying subtle patterns and anomalies in massive datasets, such as those collected by wearables. These capabilities enable the early detection of health issues, often before symptoms become obvious or severe.

Benefits include:

• Earlier diagnosis of potentially serious conditions
  
  
• Reduced emergency interventions
  
  
• More successful long-term disease management

Examples:

• Detecting arrhythmias like atrial fibrillation through smartwatch ECG sensors
  
  
• Noticing sleep apnea or poor-quality sleep based on respiration and movement data
  
  
• Flagging early signs of respiratory issues through changes in oxygen saturation

Early detection allows for timely intervention, which can drastically reduce complications and healthcare costs.

4. Improved Patient Engagement

Wearable technology also plays a vital role in improving patient engagement and self-management. Real-time access to personal health data empowers individuals to take control of their well-being. With instant feedback on their habits and health status, patients become more motivated to stick to treatment plans and adopt healthier lifestyles.

Key impacts:

• Encourages users to monitor and manage sleep, exercise, and stress
  
  
• Promotes accountability in chronic disease management
  
  
• Builds awareness and understanding of personal health trends

Examples:

• A wearable sending a reminder to stand or walk after prolonged inactivity
  
  
• Apps visualizing sleep cycles, encouraging users to improve sleep hygiene
  
  
• Stress-level indicators prompting users to try

6 Examples of AI-Powered Healthcare Wearables

There are several types of AI-powered healthcare wearables that can be used to improve patient care. Here are six examples:

1. Apple Watch

The Apple Watch is one of the most advanced AI-integrated wearables available today. It uses machine learning algorithms to monitor a range of vital health metrics in real time. Notably, it can detect irregular heart rhythms, alerting users to signs of atrial fibrillation. 

It also provides on-demand ECG readings, monitors blood oxygen levels, and tracks daily physical activity such as steps, movement, and calorie burn.

Key features:

• ECG and heart rate monitoring
  
  
• Blood oxygen level tracking
  
  
• Fall detection and emergency SOS
  
  
• Health alerts delivered via the Health app

This device helps both users and clinicians gain early insights into cardiovascular and fitness health, making it valuable for both preventive care and active condition monitoring.

2. Oura Ring

The Oura Ring is a compact, lightweight smart ring designed primarily for sleep and wellness tracking. It collects data on body temperature, heart rate, and activity levels, and uses AI algorithms to assess and score your sleep quality, recovery, and overall readiness for the day.

Key features:

• In-depth sleep analysis including REM, deep, and light sleep
  
  
• Continuous heart rate and body temperature monitoring
  
  
• Personalized health insights through the Oura app
  
  
• Readiness score to optimize rest and productivity
  
  

Oura's holistic health insights are particularly beneficial for users focused on improving sleep hygiene and managing daily energy levels.

3. WHOOP Strap

The WHOOP Strap is a performance-focused wearable popular among athletes and high performers. It offers continuous monitoring of health metrics, including heart rate variability (HRV), respiratory rate, and skin temperature. Its AI platform interprets this data to give detailed feedback on recovery, strain, and sleep.

Key features:

• Real-time HRV and recovery insights
  
  
• Sleep coaching with optimal bedtime suggestions
  
  
• Activity strain monitoring
  
  
• Health trend tracking over time

WHOOP’s AI emphasizes long-term health optimization and physical performance improvement by tailoring recommendations to each user’s physiological data.

4. Galaxy Smart Ring

Samsung’s Galaxy Smart Ring is a new entrant into the wearable space, equipped with powerful sensors to track heart rate, sleep patterns, and physical activity. Using AI, it provides personalized health insights through the Samsung Health app, helping users understand trends and make informed wellness decisions.

Key features:

• Sleep stage detection
  
  
• Daily activity tracking
  
  
• Integration with Galaxy devices and Samsung Health
  
  
• AI-based wellness suggestions

The ring is designed for users seeking a discreet, stylish, and data-driven approach to health tracking.

5. Ultrahuman Ring

The Ultrahuman Ring focuses on metabolic health and performance, combining insights from wearable tracking with optional glucose monitoring. It captures daily movement, sleep patterns, and metabolic markers to help users improve fitness and manage blood sugar more effectively.

Key features:

• Continuous metabolic tracking
  
  
• AI-driven glucose trend analysis
  
  
• Daily readiness and recovery scoring
  
  
• Personalized activity and nutrition tips

Ideal for fitness enthusiasts and individuals managing insulin sensitivity or blood sugar levels, Ultrahuman leverages AI to translate raw data into actionable insights.

6. GOQii Fitness Band

The GOQii Fitness Band blends traditional health tracking with real-time coaching and AI-powered recommendations. It monitors steps, heart rate, calories, and sleep, and uniquely connects users to certified health coaches for guidance on wellness and lifestyle.

Key features:

• Daily fitness tracking with AI-driven analysis
  
  
• Personalized coaching through the GOQii platform
  
  
• Goal setting and progress monitoring
  
  
• Health habit suggestions tailored to user data

The GOQii ecosystem is ideal for users seeking more than passive data, offering both tracking and professional support in one package.

5 Main Challenges in Integrating AI and Wearable Technology into Healthcare

As with the introduction of any new technology into any industry or sector, there are some challenges that come with AI and wearable technology in healthcare. 

Here are the five main challenges that come with this integration.

1. Data Quality and Accuracy

One of the most significant challenges in using AI-powered wearable devices in healthcare is ensuring the quality and reliability of the data collected. While wearables generate massive amounts of real-time data, factors such as sensor calibration, battery degradation, environmental interference, and even user behavior (like improper device placement or inconsistent usage) can lead to inaccurate or inconsistent readings.

Key concerns:

• Inaccurate measurements can result in false alarms or missed diagnoses
  
  
• Devices may interpret physiological changes differently across users
  
  
• Clinical decision-making can be compromised without reliable data

To mitigate this, developers must continuously test and refine sensors, while healthcare providers should treat wearable data as supplementary, not standalone.

2. Patient Privacy and Confidentiality

Wearables and AI systems collect and transmit sensitive personal health data, including heart rate, sleep patterns, blood glucose levels, and more. This makes them prime targets for cybersecurity threats. Ensuring data encryption, secure cloud storage, and controlled access is vital to protecting patient confidentiality.

Challenges include:

• Compliance with regulations such as HIPAA (U.S.) or GDPR (EU)
  
  
• Risk of unauthorized access or data breaches
  
  
• Balancing data utility with privacy protections

Manufacturers and healthcare providers must implement robust data protection frameworks and ensure transparent data practices to build patient trust.

3. Health Equity and Fairness

Access to AI-powered wearables is often limited by economic, geographic, and technological disparities. Individuals in low-income or rural areas may lack access to the internet, smartphones, or the devices themselves. Furthermore, AI algorithms can unintentionally reproduce biases present in their training datasets, leading to less accurate predictions for underrepresented populations.

Implications include:

• Widening of the digital divide in healthcare
  
  
• Inaccurate health assessments for certain ethnic, gender, or age groups
  
  
• Potential mistrust of AI-driven healthcare solutions

To address this, stakeholders must prioritize inclusive data sets, design affordable solutions, and engage diverse communities during development and deployment.

4. Usability and Patient Engagement

For wearable technology to be effective, it must be user-friendly, comfortable, and accessible. Complicated interfaces, uncomfortable hardware, or a lack of clear instructions can result in poor user engagement, especially among older adults or individuals with disabilities.

Barriers to adoption:

• Complex setup or maintenance
  
  
• Difficulty interpreting health metrics
  
  
• Low digital literacy among certain populations

User-centered design, intuitive mobile apps, and ongoing support and education are essential to improving adherence and maximizing health outcomes.

5. Integration With Existing Healthcare Infrastructure

A major operational hurdle is the integration of wearable and AI-generated data into existing clinical systems, such as Electronic Health Records (EHRs). Many healthcare providers struggle with data silos, lack of interoperability, and non-standardized formats, making it difficult to use wearable data in day-to-day care.

Key integration challenges:

• Limited APIs or incompatible data formats
  
  
• Overload of raw data with little clinical context
  
  
• Delayed adoption due to IT complexity

Efforts should focus on developing standardized protocols, enhancing vendor collaboration, and ensuring that clinicians can easily interpret and act on wearable insights within their normal workflow.

Discover the Potential of AI in Healthcare With Keragon

Keragon is an AI-powered platform focused on healthcare automation. It’s designed to streamline complex tasks while maintaining HIPAA compliance.

By automating routine processes, Keragon helps medical staff save time and reduce manual effort.

Some of the features offered by Keragon include:

• Automated document management
• Secure patient data processing
• Workflow optimization for clinicians and administrators

These features support hospitals and clinics by improving efficiency and minimizing errors.

Keragon’s emphasis on compliance ensures that sensitive health information remains secure.

Keragon recently secured $7.5 million in seed funding, highlighting growing confidence in AI-driven healthcare solutions.

This investment is expected to accelerate the development and adoption of new capabilities.

AI-powered automation allows healthcare organizations to focus more on patient care, reducing paperwork and improving service delivery.

As these tools become more integrated, medical professionals will benefit from more streamlined and consistent operations.

Key Benefit

Description

Data Security

HIPAA-compliant data management

Time Savings

Automates repetitive administrative tasks

Improved Accuracy

Reduces manual errors in records

With platforms like Keragon, healthcare systems can leverage artificial intelligence to address challenges in data management, compliance, and workflow efficiency.

FAQs

How is artificial intelligence used in wearable devices?

AI helps make sense of the vast amount of data that wearable devices collect.

Smartwatches and fitness bands use AI-driven algorithms to identify patterns in heart rate, sleep, and physical activity.

Machine learning models can detect deviations from personal health norms. This enables these devices to provide alerts for irregular heart rhythms or potential sleep disorders.

Some devices support chronic disease management by monitoring glucose levels, physical activity, or medication adherence.

Many wearables use AI for personalized coaching. They analyze daily trends and suggest adjustments for exercise, recovery, or stress management.

Through smartphone apps, AI compiles these insights into easy-to-understand reports for users and, when needed, for healthcare providers.

What is the future of AI-powered wearable devices in healthcare?

AI-powered wearables are expected to offer more accurate, real-time risk predictions and automated medical alerts.

As data sources increase, AI systems will create a more complete view of each patient’s health in their natural environment.

Integration with telehealth platforms is likely to grow. This would let doctors access continuous health data and make better-informed decisions quickly.

There is growing interest in medical-grade wearables approved for use in hospital settings and remote patient monitoring.

Future trends include improved battery life, better privacy and security features, and the use of new sensors for tracking blood pressure, hydration, and respiration.

Regulations and data standards will play a key role in how quickly these technologies are adopted.

How is AI used in sensors?

AI is used to process raw signals from sensors, such as accelerometers, photoplethysmography (PPG), and electrocardiograms (ECGs), and extract key health metrics.

Signal filtering, noise reduction, and pattern recognition are common uses of AI in wearable sensors.

AI models can differentiate between normal and abnormal heart rhythms or detect falls using motion data.

In activity trackers, AI distinguishes between walking, running, or cycling by interpreting sensor readings in real time.

Wearables with advanced AI can monitor subtle physiological changes, enabling the early detection of conditions like atrial fibrillation or respiratory changes.

Edge AI allows these analyses to occur directly on the device, preserving privacy and reducing data transmission needs.

Who can benefit from medical AI and wearable technologies?

Medical AI and wearables can benefit a wide range of people. Patients with chronic conditions, such as diabetes or cardiovascular disease, gain continuous monitoring and early warning notifications.

Athletes and those interested in fitness use AI wearables for performance tracking and injury prevention.

Older adults and people at risk of falls benefit from emergency alerts and daily activity monitoring.

Healthcare providers use these tools for remote patient management.

Researchers gain access to large, real-world datasets for medical studies.

These technologies may also help those seeking to improve lifestyle habits or manage stress more effectively by providing personalized feedback.

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