AI in Remote Patient Monitoring Market Size is valued at USD 1,221.35 Mn in 2022 and is predicted to reach USD 6,896.41 Mn by the year 2031 at a 21.3% CAGR during the forecast period for 2023-2031.
AI in Remote Patient Monitoring (RPM) utilizes technology to remotely gather and analyze patient health data, offering continuous monitoring, early warning capabilities, personalized care, and cost reduction. This approach integrates with telehealth, tailors care plans, ensures data security, and aids clinical decision-making. It empowers patients, reduces healthcare expenses, and has the potential for population health management. Successful implementation requires robust data infrastructure, secure communication, and adherence to regulations, ultimately improving patient care and healthcare efficiency.
Large data quantities, growing issues connected to healthcare expenditures, and precise patient outcomes contribute to the healthcare sector's rapid evolution.
The need for real-time data is growing as the prevalence of chronic diseases, including diabetes, cardiovascular disease, and chronic respiratory diseases, rises, which in turn is driving demand for AI technology in remote patient monitoring. Approximately 9.3% of the world's population, or 463 Mn people, have diabetes, according to Diabetes Research and Clinical Practices, with the highest prevalence in low- and middle-income nations.
The AI in the Remote Patient Monitoring market has been segmented based on product, solution, technology, and application. The market is broadly divided into special and vital monitors based on the product. The solution segment includes hardware, services, and software. The technology segment includes machine learning, natural language processing, querying methods, and speech recognition. The application segments include cancer, cardiovascular diseases, dehydration, diabetes, infections, respiratory issues, sleep disorders, viral infection, and weight management & fitness monitoring.
The market for AI in remote patient monitoring is dominated by the machine learning segment in terms of revenue. Machine learning, a kind of AI, uses specialized algorithms to assist clinicians in swiftly comprehending complex data. In order to aid in the early detection of health status deterioration, they can assist with patient evaluations and even classify the patient's varied movements and activities. Large datasets can be processed by these AI systems to find and comprehend complicated patterns for decision-making.
The market for artificial intelligence (AI) in remote patient monitoring had the greatest revenue share in the world. Doctors can get asset information via remote monitoring using AI software even when assets are dispersed across several physical sites. It can, therefore, be used to monitor and assess the performance and condition of assets located away from the workplace, such as while patients are travelling.
The market had its greatest revenue share in North America. In 2022, the market is dominated by North America as a result of the presence of significant companies. Additionally, it is projected that the expansion will be aided by North America's more straightforward payback regulations and a rise in the occurrence of uncommon diseases. The market has also been considerably impacted by the general public's increased awareness of diseases, their treatments, and related preventative actions. The adoption of smartphones, network advancements, and internet and social media use drives the industry. The growth of mHealth apps and intensive R&D in health wearables are driving the demand for AI-based remote patient monitoring solutions in the North American region.
| Report Attribute | Specifications |
| Market Size Value In 2022 | USD 1,221.35 Mn |
| Revenue Forecast In 2031 | USD 6,896.41 Mn |
| Growth Rate CAGR | CAGR of 21.3% from 2023 to 2031 |
| Quantitative Units | Representation of revenue in US$ Mn and CAGR from 2023 to 2031 |
| Historic Year | 2019 to 2022 |
| Forecast Year | 2023-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Product, Solution, Technology, Application |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico ;The UK; France; Italy; Spain; China; Japan; India; South Korea; South East Asia; South Korea; South East Asia |
| Competitive Landscape | Aicurev, Binah.Ai, Biofourmis, Care.Ai Inc., Connect America LLC, Cardiomo Care, Inc., ChroniSense Medical, Ltd., CU-BX Automotive Technologies Ltd., Current Health, Healthsaas Inc., Implicity, Maya Md, Somatix Inc., Ejenta, Inc., Feebris Ltd., Gyant.com, Inc., Huma Therapeutics Limited, Neteera Technologies Ltd., iBeat, Inc., iHealth Labs, Inc., Others |
| Customization Scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Remote Patient Monitoring Market By Product-
AI in Remote Patient Monitoring Market By Solution-
AI in Remote Patient Monitoring Market By Technology-
AI in Remote Patient Monitoring Market By Application-
AI in Remote Patient Monitoring Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.