IN 1947, Willem Einthoven’s electrocardiograph — a machine requiring the patient to immerse their hands and feet in buckets of saline solution to complete the electrical circuit — weighed 270 kilograms and required a team of five technicians to operate. A physician who needed to assess a patient’s heart rhythm had to bring the patient to the machine. Today, a wristwatch smaller than a matchbox performs continuous cardiac monitoring, detects atrial fibrillation with clinical-grade accuracy, and transmits data to a physician thousands of kilometres away in real time. This compression of capability — from a room-sized apparatus operated by specialists to a consumer device worn by tens of millions — captures, in miniature, the transformation now reshaping the management of chronic disease globally.
Remote Patient Monitoring (RPM) — the systematic use of connected devices to collect clinical data outside traditional healthcare settings and transmit it to care providers for assessment and management — has existed as a concept since the early 2000s. What has changed in the past three years is not the idea but the infrastructure, the intelligence, and the scale of adoption. The global IoT medical devices market was valued at $64.77 billion in 2024 and is projected to grow to $80.03 billion in 2025 and $364.83 billion by 2032, at a compound annual growth rate of 24.2 percent. The global market for AI-powered wearables alone is forecast to surpass $39 billion in 2026. These figures reflect a genuine structural shift in healthcare delivery — from episodic, clinic-based care toward continuous, data-rich, home-centred care pathways for the management of chronic conditions that collectively account for approximately 90 percent of the $4.1 trillion annual US healthcare expenditure.
The Technology Stack: From Skin to Cloud
Modern RPM systems operate across three integrated layers. The device layer — the interface between the patient’s body and the digital system — has expanded dramatically in clinical capability while shrinking in size and cost. Consumer wearables including Apple Watch Series 10 and Samsung Galaxy Watch Ultra now offer clinically validated ECG monitoring, blood oxygen saturation (SpO2) measurement, atrial fibrillation detection, and sleep staging. Medical-grade patches and implantable sensors extend continuous monitoring to blood glucose, blood pressure, and cardiac biomarker tracking without the need for cuffs, finger pricks, or frequent clinical visits. The VitaSensors AI-integrated vital signs monitor, commercially launched in 2025, exemplifies the convergence: it interprets multi-parameter patient data in real time and relays structured alerts to clinicians for immediate decision-making.
The connectivity layer transmits device data through cellular, WiFi, or Bluetooth networks to cloud platforms where the analytics layer applies machine learning to continuous physiological streams. This is where AI transforms RPM from a data collection exercise into a predictive clinical tool. AI-enabled remote monitoring has demonstrated the ability to detect early signs of heart failure 7 to 10 days before symptoms become clinically apparent — a window during which intervention can prevent hospitalisation entirely. AI-enabled triage systems in hospital radiology and RPM programmes have reduced average report turnaround times from 11.2 days to as low as 2.7 days. AI-driven predictive alerts have cut hospital readmissions by as much as 38 percent for high-risk chronic patients by identifying clinical deterioration before it becomes acute.
Chronic Disease: Where RPM Delivers Greatest Value
The conditions for which RPM delivers the most demonstrable clinical and economic value are also the most prevalent in the global disease burden: heart failure, hypertension, diabetes, and chronic obstructive pulmonary disease (COPD). Chronic diseases affect over one billion people globally. In each of these conditions, the clinical challenge is the same: the disease is continuously active between clinic visits, progresses silently, and produces acute events — hospitalisations, myocardial infarctions, diabetic crises — that are enormously expensive to treat and often preventable with earlier intervention. RPM closes the gap between clinic visits with continuous data, and AI transforms that data into actionable clinical intelligence.
In heart failure management, continuous weight monitoring and impedance sensing can detect fluid accumulation — a precursor to acute decompensation — days before the patient experiences symptomatic breathlessness. In hypertension, 24-hour ambulatory blood pressure monitoring via wearable devices provides far more clinically meaningful data than the isolated readings taken in a clinic, where white-coat hypertension creates systematic bias. In diabetes, continuous glucose monitors (CGMs) from companies including Dexterity, Libre, and Eversense have moved from speciality tools for Type 1 diabetics to mainstream management platforms for the far larger Type 2 population, enabling both patients and clinicians to see glycaemic patterns that no fingerstick testing schedule could reveal.
The Governance and Equity Landscape
The RPM expansion has prompted significant regulatory activity. The FDA’s January 2025 comprehensive draft guidance on AI-enabled device software functions introduced lifecycle management requirements that apply directly to AI-driven RPM platforms, requiring documented data lineage, bias analysis, performance monitoring, and change control plans. By late 2025, 1,451 AI-enabled medical devices had received cumulative FDA authorisation, with 295 new clearances in 2025 alone. The EU AI Act’s high-risk provisions, effective August 2026, will impose additional compliance requirements on RPM systems marketed in Europe.
The equity dimension is equally pressing. AI-driven RPM has the potential to reduce healthcare disparities by extending clinical-quality monitoring to patients in rural and underserved areas who cannot access specialist care in person. A RAND study estimated that broad use of RPM and digital health in chronic care could save the US healthcare system $25 billion annually from rural telehealth alone. However, the populations most in need of RPM — elderly patients managing multiple comorbidities, economically disadvantaged patients with limited digital literacy — are frequently those least able to navigate the technology, and devices validated predominantly on younger, healthier, lighter-skinned populations carry embedded performance biases that reduce accuracy in precisely the populations who need them most. For India, with its 77 million diabetics (the world’s largest diabetic population), 220 million hypertensives, and a severe specialist shortage in tier-2 and tier-3 cities, RPM represents an extraordinary public health opportunity that AIIMS, Apollo, and the broader health technology ecosystem must collectively mobilise to realise.
– Indraneel P
