Imagine your watch alerting you to a serious cardiovascular condition you didn’t even know you had. Samsung’s latest smartwatch can do just that. In October the Suwon-headquartered company announced that its wearable technology will soon enable early detection and monitoring of left ventricular systolic dysfunction (LVSD), a condition responsible for about half of all heart failure cases.

‘Heart failure stands out as one of the most fatal cardiovascular diseases, with a five-year survival rate of only 50% following diagnosis. As a result, early detection of LVSD is critical, as timely diagnosis along with medication and lifestyle adjustments can significantly reduce hospitalisation and lower the risk of death,’  said Samsung.

The company developed algorithms in collaboration with Medical AI, a Korean medical device company specialising in AI-based electrocardiogram (ECG) technology. Based  on Medical AI’s in-house algorithm for the 12-lead ECG analysis, which is already deployed  in more than 100 major hospitals in Korea  and used for about 120 000 patients a month, the algorithms bring ‘clinically proven, real-world reliability that led to securing regulatory approval from South Korea’s Ministry of Food and Drug Safety as the first smartwatch to gain LVSD detection capabilities’, according  to Samsung. ‘This technology not only enables early screening for asymptomatic individuals but also aims to reduce the surging healthcare costs associated with heart failure by promoting early diagnosis and timely treatment.’

In the modern work environment, employee well-being has become a top priority. Rising stress, sedentary lifestyles and blurred boundaries between work and home life have pushed organisations to rethink wellness strategies. While traditional programmes such as gym memberships and mindfulness workshops remain important, technology – especially  AI – has begun to revolutionise how employees monitor and improve their health.

Wearable devices, from fitness trackers  to smartwatches and now even rings, have  long helped users track steps, sleep and heart rates. But the integration of AI transforms  these devices from passive data collectors into active wellness partners. Machine learning algorithms analyse patterns, predict potential health risks and deliver personalised guidance. A smartwatch, for example, can detect insufficient sleep, high stress or prolonged inactivity,  then suggest actionable steps such as   breathing exercises, stretching breaks or adjustments to daily routines. In time, these AI-driven insights can become tailored to the individual.

‘The internet of medical things (IoMT), powered by AI, connects medical devices for real-time monitoring and analysis – anything from a smartwatch to hospital monitors. For example, a wearable heart monitor can detect irregular rhythms and send alerts instantly,’ said Amir Gandomi, data science professor at the University of Technology Sydney, during the release this year of a study conducted with Sunway University in Malaysia and other researchers.  It explored IoMT’s effect on healthcare and ‘highlights breakthrough results, such as AI-powered IoMT achieving 99.84% accuracy in heart disease diagnosis’ from medical imaging and real-time seizure detection.

‘For patients, it means better health management and fewer hospital visits. For families, it offers peace of mind, especially for seniors or those with chronic conditions. IoMT is making healthcare smarter, safer and more responsive,’ said Gandomi, who has a particular focus on  the use of AI and data analytics to improve healthcare, including pandemic response and detection of diseases such as Parkinson’s, diabetes and cancer as well as heart disease.

‘Our research highlights IoMT’s potential  to improve patient outcomes, reduce hospital strain and reduce costs, making it essential for future healthcare systems,’ he said.

In March, a research paper released on the Nature website detailed how the latest AI-driven tech was influencing medical diagnosis across the board. ‘Advanced continuous glucose monitoring systems now incorporate AI algorithms that not only track blood sugar levels  but predict dangerous fluctuations hours in advance, enabling pre-emptive interventions for chronic conditions. Similarly, AI-enhanced cardiac monitors utilise machine learning to detect subtle arrhythmias and predict potential cardiac events with high accuracy, significantly improving early detection rates.

‘Moreover, in resource-limited settings, novel AI-enabled photoplethysmography wearables are now being developed to address critical healthcare gaps, such as in predicting deterioration of dengue fever hours in advance, marking wearable AI’s pivotal and emerging capability to support clinical decision-making in settings where monitoring resources are scarce or with constrained healthcare infrastructure.’

One of the most powerful benefits of AI in personal wellness is predictive analytics. By analysing biometric data, lifestyle habits and environmental factors, AI can anticipate health challenges before they escalate. Elevated stress, fatigue or early signs of burnout can be identified, allowing employees to take proactive measures – through mindfulness, exercise or consultation with a healthcare professional.

‘AI systems’ predictive analytics capabilities now enable the identification of subtle patterns and early warning signs that precede serious health events. These capabilities are powered by advancements such as transfer learning (a technique where insights gained from one dataset or task are applied to improve performance on a different but related task) and federated learning (an approach that securely trains AI models using data from multiple sources without actually sharing patients’ raw data, addressing key privacy concerns while enabling collaborative model development),’ according to the Nature research paper.

‘The transition from intermittent to continuous monitoring represents a crucial advancement in patient surveillance – where uninterrupted data streams reveal previously undetectable physiological trends and variability patterns that static measurements miss, offering deeper insights into disease progression and therapeutic response. This capability spans from detecting early sepsis patterns in hospitalised patients to predicting exacerbations in chronic conditions like COPD [chronic obstructive pulmonary disease] at home, representing a fundamental shift from reactive to proactive healthcare delivery.’

For employers, predictive wellness offers tangible advantages – reduced absenteeism,   higher productivity and a more engaged workforce. Aggregated and anonymised data also provides insights into organisational wellness trends, helping HR teams design targeted interventions without compromising privacy.

Workplace wellness isn’t just physical; it’s emotional too. AI-powered apps and chatbots now provide accessible mental health support. From mood tracking and guided meditation to cognitive behavioural exercises, AI can help employees manage stress and recognise early signs of burnout. While AI cannot replace professional therapists, it serves as an immediate resource, bridging the gap between need and care, and empowering organisations to address mental health proactively.

AI also enhances engagement through gamification. Wellness goals become interactive challenges, with personalised progress tracking and rewards. Algorithms can adjust difficulty and suggest new activities based  on performance, keeping users motivated. Team challenges and shared achievements foster social interaction, boosting morale  and reinforcing healthy habits.

But AI wellness tools are not without challenges. In a blog post published by Med-Tech Insights, technology writer Lydia Harper lists some of the hurdles facing widespread adoption of wearable technology for employee health diagnosis.

‘Arguably the most pressing challenge in health wearable technology involves accuracy, or more specifically, difficulties related to calibration. Continuous monitoring of health metrics can provide users with important and actionable insights, but any inaccuracies present in the hardware and software used to collect data will impact the efficacy of each wearable device. Even the most advanced physiological sensors currently available have limitations, including things like motion artefacts and signal interference, with these factors potentially contributing to imprecise  or false readings,’ writes Harper.

Another concern is security. ‘The data collected by wearable health devices is considered as sensitive information to the user, meaning strict cybersecurity policies must be created to prevent confidential information being exposed to third parties. This is particularly important when dealing with cloud-based storage systems linked to wireless health devices.

‘For medical professionals to safely utilise wearable health technology, security integrators and commercial security system installers must be consulted to develop unique cybersecurity solutions and to implement appropriate security policies involving wider systems like access control devices and security cameras. Policies must be updated frequently to prevent new vulnerabilities being exposed over time.’

Equitable access is of course essential – employees should not be disadvantaged by lack of devices or connectivity. What’s more, an over-reliance on AI can create anxiety if recommendations feel intrusive or prescriptive. The most successful programmes must combine AI insights with human oversight  to ensure support is accurate, empathetic  and actionable, so it is crucial that companies integrate AI wellness fully into corporate culture for the tools to be truly effective. Transparent communication and opt-in frameworks will be needed to build this  trust, encouraging participation.

As AI continues to evolve, its role in workplace wellness will deepen. Future  systems may integrate dietary habits, stress levels, sleep patterns and workload to offer holistic health guidance – a personal coach  for every employee.

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