AI in Remote Patient Monitoring Market Size, Share & Trends Analysis Report By Product (Special Monitors, Vital Monitors), By Solution (Hardware, Services, Software), By Technology (Machine Learning, Natural Language Processing, Querying Method, Speech Recognition), By Application, By Region, And By Segment Forecasts, 2023-2031.

Report Id: 2071 Pages: 180 Last Updated: 05 February 2025 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Remote Patient Monitoring Market-

AI in Remote Patient Monitoring Market By Product-

  • Special Monitors
  • Anaesthesia Monitors
  • Blood Glucose Monitor
  • Cardiac Rhythm Monitor
  • Fetal Heart Rate Monitor
  • Multi-Parameter monitors
  • Prothrombin Monitors
  • Respiratory Monitor
  • Vital Monitors
  • Blood Pressure Monitor
  • Brain Monitor
  • Heart Rate Monitor
  • Pulse Oximeter
  • Respiratory Monitor
  • Temperature Monitor

AI in Remote Patient Monitoring Market Seg

AI in Remote Patient Monitoring Market By Solution-

  • Hardware
  • Services
  • Software

AI in Remote Patient Monitoring Market By Technology-

  • Machine Learning
  • Natural Language Processing
  • Querying Method
  • Speech Recognition

AI in Remote Patient Monitoring Market By Application-

  • Cancer
  • Cardiovascular Diseases
  • Dehydration
  • Diabetes
  • Infections
  • Respiratory Issues
  • Sleep Disorders
  • Viral Infection
  • Weight Management & Fitness Monitoring

AI in Remote Patient Monitoring Market By Region-

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of the Middle East and Africa

Chapter 1. Methodology and Scope

1.1. Research Methodology

1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Remote Patient Monitoring Market Snapshot

Chapter 4. Global AI in Remote Patient Monitoring Market Variables, Trends & Scope

4.1. Market Segmentation & Scope

4.2. Drivers

4.3. Challenges

4.4. Trends

4.5. Investment and Funding Analysis

4.6. Industry Analysis – Porter’s Five Forces Analysis

4.7. Competitive Landscape & Market Share Analysis

4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: By Product Estimates & Trend Analysis

5.1. By Product, & Market Share, 2020 & 2031

5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Product:

5.2.1. Special Monitors

5.2.1.1. Anaesthesia Monitors

5.2.1.2. Blood Glucose Monitor

5.2.1.3. Cardiac Rhythm Monitor

5.2.1.4. Fetal Heart Rate Monitor

5.2.1.5. Multi-Parameter monitors

5.2.1.6. Prothrombin Monitors

5.2.1.7. Respiratory Monitor

5.2.2. Vital Monitors

5.2.2.1. Blood Pressure Monitor

5.2.2.2. Brain Monitor

5.2.2.3. Heart Rate Monitor

5.2.2.4. Pulse Oximeter

5.2.2.5. Respiratory Monitor

5.2.2.6. Temperature Monitor

Chapter 6. Market Segmentation 2: By Solution Estimates & Trend Analysis

6.1. By Solution & Market Share, 2020 & 2031

6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Solution:

6.2.1. Hardware

6.2.2. Services

6.2.3. Software

Chapter 7. Market Segmentation 3: By Technology Estimates & Trend Analysis

7.1. By Technology & Market Share, 2020 & 2031

7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Technology:

7.2.1. Machine Learning

7.2.2. Natural Language Processing

7.2.3. Querying Method

7.2.4. Speech Recognition

Chapter 8. Market Segmentation 4: By Application Estimates & Trend Analysis

8.1. By Application & Market Share, 2020 & 2031

8.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Application:

8.2.1. Cancer

8.2.2. Cardiovascular Diseases

8.2.3. Dehydration

8.2.4. Diabetes

8.2.5. Infections

8.2.6. Respiratory Issues

8.2.7. Sleep Disorder

8.2.8. Viral Infection

8.2.9. Weight Management & Fitness Monitoring

Chapter 9. AI in Remote Patient Monitoring Market Segmentation 5: Regional Estimates & Trend Analysis

9.1. North America

9.1.1. North America AI in Remote Patient Monitoring Market revenue (US$ Million) estimates and forecasts By Product, 2019-2031

9.1.2. North America AI in Remote Patient Monitoring Market revenue (US$ Million) estimates and forecasts By Solution, 2019-2031

9.1.3. North America AI in Remote Patient Monitoring Market revenue (US$ Million) estimates and forecasts By Technology, 2019-2031

9.1.4. North America AI in Remote Patient Monitoring Market revenue (US$ Million) estimates and forecasts By Application, 2019-2031

9.1.5. North America AI in Remote Patient Monitoring Market revenue (US$ Million) estimates and forecasts by country, 2019-2031

9.2. Europe

9.2.1. Europe AI in Remote Patient Monitoring Market revenue (US$ Million) By Product, 2019-2031

9.2.2. Europe AI in Remote Patient Monitoring Market revenue (US$ Million) By Solution, 2019-2031

9.2.3. Europe AI in Remote Patient Monitoring Market revenue (US$ Million) By Technology, 2019-2031

9.2.4. Europe AI in Remote Patient Monitoring Market revenue (US$ Million) By Application, 2019-2031

9.2.5. Europe AI in Remote Patient Monitoring Market revenue (US$ Million) by country, 2019-2031

9.3. Asia Pacific

9.3.1. Asia Pacific AI in Remote Patient Monitoring Market revenue (US$ Million) By Product, 2019-2031

9.3.2. Asia Pacific AI in Remote Patient Monitoring Market revenue (US$ Million) By Solution, 2019-2031

9.3.3. Asia Pacific AI in Remote Patient Monitoring Market revenue (US$ Million) By Technology, 2019-2031

9.3.4. Asia Pacific AI in Remote Patient Monitoring Market revenue (US$ Million) By Application, 2019-2031

9.3.5. Asia Pacific AI in Remote Patient Monitoring Market revenue (US$ Million) by country, 2019-2031

9.4. Latin America

9.4.1. Latin America AI in Remote Patient Monitoring Market revenue (US$ Million) By Product, (US$ Million) 2019-2031

9.4.2. Latin America AI in Remote Patient Monitoring Market revenue (US$ Million) By Solution, (US$ Million) 2019-2031

9.4.3. Latin America AI in Remote Patient Monitoring Market revenue (US$ Million) By Technology, (US$ Million) 2019-2031

9.4.4. Latin America AI in Remote Patient Monitoring Market revenue (US$ Million) By Application, (US$ Million) 2019-2031

9.4.5. Latin America AI in Remote Patient Monitoring Market revenue (US$ Million) by country, 2019-2031

9.5. Middle East & Africa

9.5.1. Middle East & Africa AI in Remote Patient Monitoring Market revenue (US$ Million) By Product, (US$ Million) 2019-2031

9.5.2. Middle East & Africa AI in Remote Patient Monitoring Market revenue (US$ Million) By Solution, (US$ Million) 2019-2031

9.5.3. Middle East & Africa AI in Remote Patient Monitoring Market revenue (US$ Million) By Technology, (US$ Million) 2019-2031

9.5.4. Middle East & Africa AI in Remote Patient Monitoring Market revenue (US$ Million) By Application, (US$ Million) 2019-2031

9.5.5. Middle East & Africa AI in Remote Patient Monitoring Market revenue (US$ Million) by country, 2019-2031

Chapter 10. Competitive Landscape

10.1. Major Mergers and Acquisitions/Strategic Alliances

10.2. Company Profiles

10.2.1. AiCure LLC

10.2.2. Binah.ai

10.2.3. Biofourmis Inc.

10.2.4. Cardiomo Care Inc.

10.2.5. ChroniSense Medical Ltd

10.2.6. Current Health Limited

10.2.7. Ejenta Inc.

10.2.8. Feebris Ltd

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

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.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

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.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

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.

AI in Remote Patient Monitoring Market is expected to grow at a 21.3% CAGR during the forecast period for 2023-2031.

Somatix Inc., Ejenta, Inc., Feebris Ltd., Gyant.com, Inc., Huma Therapeutics Limited, Neteera Technologies Ltd., iBeat, Inc., iHealth Labs, Inc., Othe

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