AI Care Evolution – Part 2: Product Categories Driving the Future of AI in Care
AI is becoming the operational brain behind modern care systems. Its strength lies in managing indirect tasks such as data sorting, alerts, and first-level triage, freeing human caregivers to focus on empathy, clinical decisions, and personal interaction. (Source: Fotor AI)
AIoT Tools Dominate: From Smartwatches to Predictive Platforms
According to Gartner, the global healthcare IoT market is expected to reach $65.5 billion by 2031, growing at a CAGR of 7%. AIoT (AI + IoT) has taken center stage in elder care—enabling seamless data flow from wellness tracking to complex care.
Wearables like smartwatches, mmWave radars, and AI-enabled smart mattresses are no longer futuristic—they are already collecting real-time health data, detecting anomalies, and predicting risks. Post-pandemic demand for telecare and remote monitoring has surged, spurred by aging demographics and caregiver shortages.
At CES 2025, innovations targeting diabetes and sleep monitoring showed the growing emphasis on continuous tracking and predictive insights. In Taiwan, AI wearables for stress and sleep are already used in community care, alongside smart knee braces paired with rehab apps to improve mobility and compliance.
Simplified Sensors Scale Quickly: Fall Detection and Bed-Exit Alerts in Action
Basic sensors for fall and bed-exit detection are seeing rapid adoption in care facilities due to their simplicity and cost-effectiveness. When paired with EMRs and AI analytics, these devices can automatically generate alerts and predict future risks, such as frailty or repeat falls, supporting a shift toward preventive care.
Experts predict a move from isolated sensors to integrated multimodal systems that can one day simulate “digital twins” of each patient—a real-time digital model of a person’s health. But achieving this will require more affordable, reliable sensors and large, high-quality datasets from real care settings.
AI Supports Care Teams by Handling Routine but Critical Tasks
AI is becoming the operational brain behind modern care systems. Its strength lies in managing indirect tasks such as data sorting, alerts, and first-level triage, freeing human caregivers to focus on empathy, clinical decisions, and personal interaction, which contributes to 3 key ways:
Labour reduction: Automating admin-heavy processes
Error minimization: Enhancing accuracy in routine tasks
Value enhancement: Allowing caregivers to focus on high-touch services
Long-term care systems are integrating AI to optimize scheduling, staffing, and reimbursement processes. Generative models even assist in drafting care plans, helping new staff understand residents’ needs faster. Synced with sensors, AI enables real-time interventions, like repositioning based on smart mattress data or flagging abnormal SpO2 levels from wearables.
AI-Powered Robots and Voice Assistants Take on Social and Supportive Roles
AI also reshapes front-line care through voice assistants and care robots. These fall into 2 categories:
Software-based voice agents (often powered by generative AI)
Hardware robots with navigation, voice, and sensor capabilities
In the U.S., companion robots are used in public health programs to reduce loneliness, offering voice interaction, emotional analysis, and wellness reminders, and some are even covered by insurance. In Taiwan, AI-powered voice systems call older adults for medication reminders and analyze voice tone to assess potential risks.
Robots with basic interaction and navigation functions are deployed in care facilities for cognitive games, health alerts, and social engagement. When linked with personal health data, these robots evolve from companions to proactive aides. Some hospitals are even adapting restaurant delivery robots to transport supplies in dialysis units, relieving staff workloads.
Shared robot services may offer a cost-effective path to broader adoption soon.
Scalability Hinges on Reimbursement Models and Interoperability Standards
From wellness to long-term care, AI solutions now cover the full care continuum. Yet the path to wide-scale commercialization is still blocked—not by technology, but by policy and infrastructure gaps.
Most AIoT deployments still rely on subsidies or pilot programs. For the market to mature, experts point to three key needs:
Clear reimbursement pathways for digital and AI-enabled care
Performance standards to validate reliability and safety
Interoperability protocols that connect diverse systems and datasets
As digital twins become more sophisticated, caregivers will gain 360-degree visibility into elder health. Meanwhile, AI-powered mobility tools, such as autonomous wheelchairs and smart elder vehicles, promise to boost independence and social participation. These converging technologies are steering AI care toward a tipping point, poised to redefine care delivery and reshape the global care economy.
| Category | Examples / Types | Key Functions |
|---|---|---|
| 1. Management Platforms | Smart care management platforms, operation management systems | Integrate scheduling, staffing, records, billing, and resource allocation. Use AI and digital workflows to improve efficiency, reduce errors, and speed decision-making. |
| 2. Fall / Behavior Monitoring | AI vision systems, mmWave radar sensors, wearable activity sensors | Continuously track elderly movement and behavior. Detect falls, wandering, prolonged inactivity, and alert caregivers in real time to enhance safety. |
| 3. Physiological / Risk Monitoring | Smart mattresses, mmWave radar sensors, wearable physiological devices, remote care platforms | Collect vital signs (heart rate, blood pressure, respiration, sleep) continuously. Use AI to analyze risks (frailty, heart disease, chronic conditions), provide warnings, and support remote care teams. |
| 4. Environmental Safety Management | Smart sensors, anomaly alarm systems, safety management platforms | Monitor safety conditions in facilities or homes (smoke, gas, door/window status, intrusion). Immediately notify caregivers or family upon abnormalities. |
| 5. Smart Robots | Companion robots, care robots, service robots | Provide companionship, rounds, delivery, disinfection, and support tasks to reduce caregiver workload using robotics and AI technologies. |
| 6. Personal Health Management Platforms | Chronic disease management platforms, wearable health cloud platforms | Track chronic conditions, diet, exercise, and sleep. Combine wearable data with apps to analyze health trends and assist elderly and families in health management. |
| 7. Exercise Rehab / Smart Aids | AI motion tracking systems, remote rehab devices, smart mobility aids | Use motion capture and sensors to assist mobility, monitor rehab exercises for accuracy and frequency, and create personalized rehab plans to improve adherence and outcomes. |
| 8. AI Voice Interaction | Smart customer service, voice companions | Use voice recognition and generative AI for interactive communication, information queries, answering patient questions, providing care advice, or companionship. |
| 9. Dementia Care | AI emotion and behavior recognition, cognitive rehab AI | Analyze behavior and emotional changes in dementia patients. Provide risk alerts and enable non-pharmacological care interventions. |
Source: AnkeCare
Building on Part 1 of Anke Media Corporation’s AI Care Evolution series, this second installment spotlights real-world use cases, product innovations, and emerging market trends. Stay tuned for Part 3, which will focus on frontline applications of AI in care settings. (Part 1: AI Care Evolution: Part 1 — 3 Game-Changing Trends Rewiring the Care Industry)
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