By Anugyan Sharma
As the global humanity advances towards a more profound appreciation of AI and its applications to heath care services, a deeper understanding of the role of AI integration into medical systems and devices becomes the need of the hour. The author pursuing his Bioengineering at UC San Diego perceives a bigger value of AI for the health care of the future. A wearable healthcare device that identifies an abnormal pattern can go beyond monitoring health parameters to provide an important warning of the onset of a serious illness. The person wearing it still needs to understand what that information means for everyday life. Knowing a step count, a sleep score or a glucose reading is only the beginning. The larger opportunity for artificial intelligence in healthcare is to connect those observations with practical guidance and expert advice that helps people not only to eat well, move safely, sleep better and but develop lasting healthy habits and maintain a stress-free healthy life. This is the way to avoid crowds in the hospitals and create affordable health care at home.
Current key trends in Wearable Medical Devices
The wearable medical device landscape is undergoing a rapid transition from basic fitness tracking to clinical-grade, continuous patient monitoring. Driven by advancements in microelectronics, biomaterials, and artificial intelligence, the market is shifting toward predictive, non-invasive, and continuous healthcare interventions.
The wearable medical device development is moving beyond optical photoplethysmography (HRV/pulse) toward tracking biochemical markers. Key developments include continuous sweat sensors for glucose, lactate, and electrolytes, alongside advancements in non-invasive blood pressure monitoring and continuous glucose monitors (CGMs) for non-diabetic metabolic health.
Edge AI is supporting modern wearables process physiological signals locally at the device level. This allows instantaneous detection of arrhythmias, respiratory distress, and fall events while improving data privacy and battery efficiency. Devices are shifting from rigid wrist-worn hardware to highly discreet form factors. Key advancements include epidermal electronic tattoos (e-tattoos), continuous ultrasound bio-patches for organ and cardiac imaging, smart rings with multi-modal biometrics, and smart contact lenses for ocular pressure and glucose detection.
Development of flexible, stretchable substrates and biodegradable materials that allow devices to temporarily sit on or inside the body, dissolving naturally after their operational window to eliminate removal procedures.
Wearable platforms are directly integrating into hospital Electronic Health Record systems via standardized protocols. This shift facilitates proactive intervention for chronic diseases (hypertension, COPD, cardiovascular care) and enables earlier hospital discharge through “hospital-at-home” care models.
The Meaning and Purpose of Making a big of AI
Making “AI big” in healthcare should mean expanding its contribution to human wellbeing. Alongside supporting diagnosis, intelligent devices should help people understand their mind, their body and their metabolism and make informed choices based on their lifestyles and health trends. Essentially, AI should become a health “life guru”: an accessible guide that teaches, encourages and adapts, while respecting individual choice and knowing precisely when professional care is needed. Its intelligence should be measured by the usefulness of the support it offers and the services it makes, more proficient and accessible. Afterall, AI thrives on hyper connectivity and access to the reservoir of data that drives its algorithms with speed and depth of analytics.
I imagine a future in which health monitoring becomes part of an ongoing conversation. A person should be able to ask, “What can I do differently today?” and receive an answer that considers their health, circumstances and priorities. That answer might involve a meal, an exercise, a change in routine or a conversation with a doctor. The goal should be to make health information easier to act on, without making everyday life feel like a medical examination. It is here lie a great opportunity to make a big of AI.
A comprehensive AI empowered system of wearable medical device could combine a person’s meal records, activity patterns, preferences and relevant medical information to suggest achievable changes. It might recommend a lentil dish, vegetables and whole grains within the person’s budget, or help compare available cafeteria meals against their nutritional needs. For example, glucose monitoring readings could help inform future meal choices.
Looking further ahead, I envision AI connecting a wider range of health indicators: blood pressure from validated monitors, sleep patterns and resting heart rate from wearables, and cholesterol levels from clinical tests. A sustained rise in blood pressure could prompt lower-salt food suggestions and appropriate medical follow-up. Elevated cholesterol could lead to practical swaps, such as replacing foods high in saturated fat with suitable alternatives. The system should help users understand these connections and choose manageable changes.
Other patterns could open different conversations. Repeated short nights could become the starting point for building a more consistent sleep routine. A change in resting heart rate could prompt questions about fatigue, stress or illness, rather than an automatic instruction to exercise harder. A thoughtful system would consider changes over time alongside how the person feels. It would ask questions when context is missing and acknowledge when a measurement has several possible explanations.
Exercise guidance should become equally personal. Someone spending long hours at a desk might be offered a short walk between commitments. Another person could receive a gradual routine involving gentle stretches, chair-supported movements or resistance-band exercises, adapted to their abilities and care needs. The device should explain how to perform an activity safely and ask whether it feels comfortable.
This future depends on AI becoming your guru. It could help us achieve synchronous breathing that creates a calm and composed mind even under duress and distress. Every recommendation should help the users understand something about their own health. Over time, the system could help someone choose one change, try it, reflect on the experience and adjust. If a goal is missed, it should explore what got in the way. The measure of progress should include whether people feel more informed and capable, rather than whether they follow every instruction or spend more time using an app. In turn, AI becomes more matured and a trustworthy medical advisor to maintain a healthy life.
The future should also be accessible. Useful advice must work with affordable foods, different languages, disabilities and demanding schedules. Healthier living cannot depend on owning the most expensive device or having access to a gym. Developers should build for the realities of people’s lives and evaluate whether their tools genuinely improve wellbeing across different communities. Making AI bigger should mean widening its benefits. The big of AI in healthcare shall reduce the burden of diseases, as also the crowds in the hospitals.
Vision for future of Healthcare
As a bioengineering undergraduate at UC San Diego, I see this future as a meaningful connection between engineering and human health. My studies place me at the intersection of biological systems and the technologies designed to understand them. What interests me about bigger AI is the possibility of turning a biological measurement into something a person can understand and use. I want to contribute to devices whose value continues after the measurement, through clear explanations, practical support and respect for the person using them.
This vision also calls for more effective integration of engineering and medical sciences and collaboration of biomedical technologist and medical doctors in the hospitals and healthcare units. Engineers, clinicians, nutrition professionals and the people who will use these devices should shape them together. A recommendation may be technically sound yet impossible to follow during a night shift or in a household with limited food choices. Listening to these experiences must become part of how we define truly good engineering, it should be something that adapts to anyone who wants to use it.
For me, that is the healthcare future worth building: technology that helps people learn from their health information and make better decisions throughout their lives. We should expect healthcare AI to leave people with greater understanding, confidence and control over their wellbeing. Its greatest contribution should be helping people live well.
I should not hesitate to add that AI being machine-driven analytics requires authenticity of data sets and the responsible use of its information and advice under medical supervision. It’s like keeping your family physician in the loop.
*The author Anugyan Sharma is a student member of Biomedical Engineering Society and Bioengineering Undergraduate at California University, San Diego.