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2032 年遠端患者監護人工智慧市場預測：按組件、技術、應用、最終用戶和地區進行的全球分析

Artificial Intelligence in Remote Patient Monitoring Market Forecasts to 2032 - Global Analysis By Component (AI-Enabled Devices, Software, and Services), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球遠端患者監護人工智慧市場預計在 2025 年達到 25.8 億美元，到 2032 年將達到 161.3 億美元，預測期內的複合年成長率為 29.9%。

遠端患者監護中的人工智慧是指在遠距醫療平台中使用人工智慧工具來監控醫院外患者的健康狀況。它處理即時醫療數據，識別潛在風險，並提案個人化治療方案。透過機器學習、預測模型和自動化，人工智慧可以改善患者預後，減少住院次數，加快護理回應速度，並增強慢性病管理。這種方法能夠賦能患者和醫療專業人員，同時確保持續、數據驅動且有效率的醫療服務。

據英國政府稱，2024 年 7 月至 2025 年 1 月期間，私人公司在人工智慧領域投資了約 2.5 億美元。

慢性病增多

糖尿病、心血管疾病和呼吸系統疾病等慢性疾病負擔日益加重，推動了持續健康監測解決方案的需求。人工智慧驅動的遠端患者監護(RPM) 工具正被用於更主動地管理長期疾病並減少再入院率。隨著全球人口老化和診斷能力的提高，醫療保健提供者正轉向預測分析和個人化介入。穿戴式裝置和智慧感測器能夠即時追蹤生命徵象，使臨床醫生能夠更早進行干預。這一趨勢正在加速已開發經濟體和新興經濟體醫療保健生態系統中 RPM 的採用。

資料安全和隱私問題

HIPAA 和 GDPR 等法規結構要求嚴格合規，這可能會減緩採用速度並增加營運成本。雲端基礎和物聯網設備的使用會引入漏洞，需要強大的加密和存取控制機制。規模較小的醫療保健提供者通常缺乏有效保護敏感醫療資訊的技術基礎設施。基於患者資料進行訓練的人工智慧演算法必須遵守道德標準和透明度，才能維護信任。這些與隱私相關的限制限制了可擴展性，並減緩了更廣泛的市場滲透。

個人化護理計劃和建議

人工智慧主導的遠距醫療 (RPM) 系統正在為根據患者獨特需求量身定做的個人化護理路徑開闢新的可能性。機器學習模型可以分析行為模式、用藥依從性和生物特徵數據，從而建議及時的干涉措施。這種個人化服務正在改善治療效果，並提高患者在慢性病和急性病護理中的參與度。新平台正在整合語音助理和自然語言處理功能，以提供情境感知的健康指導。預測分析可以實現風險分層和併發症的早期發現，從而減少急診就診次數。隨著基於價值的照護模式日益普及，個人化遠距醫療 (RPM) 正成為醫療保健轉型的核心部分。

抵制改變與缺乏數位素養

缺乏數位素養阻礙了智慧醫療設備的有效使用，尤其是在老年人群體中。醫療保健專業人員可能會因為不熟悉人工智慧工具或感知到其複雜性而抵制工作流程的改變。培訓計劃和方便用戶使用的介面對於彌合這一採用差距至關重要。文化和製度上的惰性可能會減緩遠端監控與傳統護理模式的融合。如果沒有針對性的教育和支持，遠距醫療平台可能會被低估，並降低其影響力。

COVID-19的影響

新冠疫情顯著加速了全球遠端患者監護技術的普及。醫院關閉且不堪重負，促使人們轉向虛擬護理和人工智慧輔助診斷。遠距患者監護工具在管理隔離患者和遠端追蹤症狀方面發揮了關鍵作用。各國政府和監管機構加快了數位醫療解決方案的核准，促進了創新和部署。疫情後的策略如今強調分散式照護、遠端醫療整合以及人工智慧驅動的分流系統。這場危機引發了醫療服務向遠距、以數據為中心的轉變。

預計人工智慧設備市場在預測期內將佔據最大佔有率

預計人工智慧設備領域將在預測期內佔據最大的市場佔有率，這得益於其在即時健康追蹤和決策支援方面的先進功能。這些設備，包括智慧穿戴裝置和連網監視器，正擴大配備機器學習演算法，以提供預測性洞察。醫院和家庭護理提供者正在利用人工智慧檢測異常並自動發出警報，以便及時介入。與雲端平台和電子健康檔案 (EHR) 的整合正在增強互通性和護理協調性。感測器技術和邊緣運算的不斷創新正在提升設備的功能性和可靠性。隨著人工智慧逐漸融入硬體，預計該領域將在應用和產生收入佔據主導。

預測期內，居家照護機構預計將以最高複合年成長率成長

在以患者為中心且注重成本效益的醫療服務模式的推動下，預計居家照護領域將在預測期內實現最高成長率。人工智慧工具正在實現慢性病的遠端監控，從而減少頻繁就醫的需求。智慧家庭健康套件和語音助理的興起，讓遠距醫療更便捷、更直覺。報銷改革和人口老化正在進一步推動居家照護模式的發展。雲端基礎的儀錶板和行動應用程式正在為看護者提供切實可行的洞察和遠端監控功能。隨著醫療保健日益分散化，居家照護正成為人工智慧主導的遠距醫療擴展的關鍵前沿。

佔比最大的地區：

在預測期內，亞太地區預計將佔據最大的市場佔有率，這得益於快速的醫療數位化和基礎設施投資。中國、印度和日本等國家正在擴展遠端醫療平台和智慧醫院計畫。政府計畫正透過補貼、先導計畫和本地製造業激勵措施推動人工智慧的普及。該地區穿戴式醫療設備和基於行動的遠距醫療解決方案的普及率正在強勁成長。全球科技公司與本地供應商之間的合作正在加速創新和市場進入。

複合年成長率最高的地區：

在預測期內，北美預計將憑藉其在人工智慧研究和醫療創新領域的領先地位，實現最高的複合年成長率。美國和加拿大正在大力投資智慧醫療基礎設施，包括人工智慧分析和遠距離診斷。監管機構正在簡化數位醫療核准流程，促進遠距醫療（RPM）技術的快速商業化。醫院正在將人工智慧與物聯網和雲端平台結合，以最佳化病患監測和資源配置。優惠的報銷政策和消費者對虛擬護理日益成長的需求正在推動其應用。隨著精準醫療和預測醫學的發展勢頭強勁，北美將繼續為遠距醫療的發展樹立標竿。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球遠端患者監護人工智慧市場（按組件）

  • 支援人工智慧的設備
    • 穿戴式裝置
    • 生物感測器
    • 可攜式心電圖和血糖監測儀
  • 軟體
    • 數據分析平台
    • 預測監測工具
  • 服務
    • 遠端監控服務
    • 人工智慧整合與支援

6. 全球遠端患者監護人工智慧市場（按技術）

  • 機器學習
  • NLP和語音辨識
  • 其他技術

7. 全球遠端患者監護人工智慧市場（按應用）

  • 心血管監測
  • 神經系統疾病
  • 糖尿病管理
  • 急性期後恢復
  • 腫瘤學
  • 呼吸系統疾病
  • 其他用途

8. 全球遠端患者監護人工智慧市場（按最終用戶）

  • 醫院和衛生系統
  • 居家照護環境
  • 門診手術中心（ASC）
  • 病人
  • 其他最終用戶

9. 全球遠端患者監護人工智慧市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第11章 公司概況

  • Koninklijke Philips
  • Medtronic
  • OMRON Healthcare
  • GE HealthCare
  • Biobeat
  • Boston Scientific
  • Dexcom
  • Nihon Kohden
  • F. Hoffmann-La Roche
  • ResMed
  • AliveCor
  • Biotronik
  • Honeywell
  • Masimo
  • Abbott
Product Code: SMRC31137

According to Stratistics MRC, the Global Artificial Intelligence in Remote Patient Monitoring Market is accounted for $2.58 billion in 2025 and is expected to reach $16.13 billion by 2032 growing at a CAGR of 29.9% during the forecast period. Artificial Intelligence in Remote Patient Monitoring involves using AI tools within remote healthcare platforms to oversee patient health beyond hospitals. It processes real-time medical data, identifies potential risks, and suggests tailored treatments. Through machine learning, predictive modelling, and automation, AI boosts patient outcomes, lowers hospital admissions, enables prompt care responses, and enhances chronic condition management. This approach ensures ongoing, data-supported, and efficient healthcare delivery while empowering both patients and healthcare professionals.

According to Gov.UK, private firms invested around USD 250 million investments in AI from July 2024 to January 2025.

Market Dynamics:

Driver:

Growing prevalence of chronic diseases

The increasing burden of chronic illnesses such as diabetes, cardiovascular conditions, and respiratory disorders is fuelling demand for continuous health monitoring solutions. AI-powered remote patient monitoring (RPM) tools are being adopted to manage long-term conditions more proactively and reduce hospital readmissions. As global populations age and diagnostic capabilities improve, healthcare providers are shifting toward predictive analytics and personalized interventions. Wearable devices and smart sensors are enabling real-time tracking of vital signs, empowering clinicians to intervene early. This trend is accelerating RPM adoption across both developed and emerging healthcare ecosystems.

Restraint:

Data security and privacy concerns

Regulatory frameworks such as HIPAA and GDPR require stringent compliance, which can slow deployment and increase operational costs. The use of cloud-based platforms and IoT devices introduces vulnerabilities that demand robust encryption and access controls. Smaller healthcare providers often lack the technical infrastructure to safeguard sensitive health information effectively. AI algorithms trained on patient data must adhere to ethical standards and transparency to maintain trust. These privacy-related constraints are limiting scalability and delaying broader market penetration.

Opportunity:

Personalized care plans and recommendations

AI-driven RPM systems are unlocking new possibilities for individualized care pathways tailored to patient-specific needs. Machine learning models can analyze behavioral patterns, medication adherence, and biometric data to recommend timely interventions. This personalization is improving treatment outcomes and enhancing patient engagement across chronic and post-acute care settings. Emerging platforms are integrating voice assistants and natural language processing to deliver context-aware health coaching. Predictive analytics is enabling risk stratification and early detection of complications, reducing emergency visits. As value-based care models gain traction, personalized RPM is becoming central to healthcare transformation.

Threat:

Resistance to change and lack of digital literacy

Limited digital literacy, especially among elderly populations, hampers effective utilization of smart health devices. Healthcare professionals may resist workflow changes due to unfamiliarity with AI tools and perceived complexity. Training programs and user-friendly interfaces are essential to bridge this adoption gap. Cultural and institutional inertia can delay integration of remote monitoring into traditional care models. Without targeted education and support, RPM platforms risk underutilization and reduced impact.

Covid-19 Impact

The COVID-19 pandemic significantly accelerated the adoption of remote patient monitoring technologies worldwide. Lockdowns and overwhelmed hospitals prompted a shift toward virtual care and AI-assisted diagnostics. RPM tools played a critical role in managing quarantined patients and tracking symptoms remotely. Governments and regulatory bodies fast-tracked approvals for digital health solutions, boosting innovation and deployment. Post-pandemic strategies now emphasize decentralized care, telehealth integration, and AI-driven triage systems. The crisis catalysed a permanent shift toward remote, data-centric healthcare delivery.

The AI-enabled devices segment is expected to be the largest during the forecast period

The AI-enabled devices segment is expected to account for the largest market share during the forecast period, due to its advanced capabilities in real-time health tracking and decision support. These devices, including smart wearables and connected monitors, are increasingly embedded with machine learning algorithms for predictive insights. Hospitals and homecare providers are leveraging AI to detect anomalies and automate alerts for timely intervention. Integration with cloud platforms and EHRs is enhancing interoperability and care coordination. Continuous innovation in sensor technology and edge computing is expanding device functionality and reliability. As AI becomes more embedded in hardware, this segment is set to lead in both adoption and revenue generation.

The homecare settings segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the homecare settings segment is predicted to witness the highest growth rate, driven by the shift toward patient-centric and cost-effective care. AI-powered tools are enabling remote monitoring of chronic conditions, reducing the need for frequent hospital visits. The rise of smart home health kits and voice-enabled assistants is making RPM more accessible and intuitive. Reimbursement reforms and aging demographics are further supporting home-based care models. Cloud-based dashboards and mobile apps are empowering caregivers with actionable insights and remote supervision. As healthcare decentralizes, homecare is emerging as a key frontier for AI-driven RPM expansion.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share supported by rapid healthcare digitization and infrastructure investments. Countries like China, India, and Japan are scaling up telehealth platforms and smart hospital initiatives. Government programs are promoting AI adoption through subsidies, pilot projects, and local manufacturing incentives. The region is witnessing strong uptake of wearable health devices and mobile-based RPM solutions. Collaborations between global tech firms and regional providers are accelerating innovation and market access.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, driven by its leadership in AI research and healthcare innovation. The U.S. and Canada are investing heavily in smart health infrastructure, including AI-powered analytics and remote diagnostics. Regulatory bodies are streamlining digital health approvals, fostering rapid commercialization of RPM technologies. Hospitals are integrating AI with IoT and cloud platforms to optimize patient monitoring and resource allocation. Favorable reimbursement policies and growing consumer demand for virtual care are boosting adoption. As precision medicine and predictive care gain momentum, North America continues to set the benchmark for RPM evolution.

Key players in the market

Some of the key players profiled in the Artificial Intelligence in Remote Patient Monitoring Market include Koninklijke Philips, Medtronic, OMRON Healthcare, GE HealthCare, Biobeat, Boston Scientific, Dexcom, Nihon Kohden, F. Hoffmann-La Roche, ResMed, AliveCor, Biotronik, Honeywell, Masimo, and Abbott.

Key Developments:

In September 2025, Royal Philips and Masimo announced that the two companies have renewed their multi-year strategic collaboration, marking a fresh chapter in their long-standing partnership. With a shared commitment to innovation and expanding access to high-quality, connected care, the two companies are taking a bold new approach in accelerating the development and delivery of next-generation patient monitoring solutions.

In April 2025, Medtronic plc announced it has submitted 510(k) applications to the U.S. Food and Drug Administration (FDA) seeking clearance for an interoperable pump. FDA clearance of this pump would pave the way for system integration with a continuous glucose monitoring (CGM) sensor based on Abbott's most advanced CGM platform.

Components Covered:

  • AI-Enabled Devices
  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • NLP & Speech Recognition
  • Other Technologies

Applications Covered:

  • Cardiovascular Monitoring
  • Neurological Disorders
  • Diabetes Management
  • Post-Acute Recovery
  • Oncology
  • Respiratory Disorders
  • Other Applications

End Users Covered:

  • Hospitals & Health Systems
  • Homecare Settings
  • Ambulatory Surgical Centers (ASCs)
  • Patients
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Artificial Intelligence in Remote Patient Monitoring Market, By Component

  • 5.1 Introduction
  • 5.2 AI-Enabled Devices
    • 5.2.1 Wearables
    • 5.2.2 Biosensors
    • 5.2.3 Portable ECG & Glucose Monitors
  • 5.3 Software
    • 5.3.1 Data Analytics Platforms
    • 5.3.2 Predictive Monitoring Tools
  • 5.4 Services
    • 5.4.1 Remote Monitoring Services
    • 5.4.2 AI Integration & Support

6 Global Artificial Intelligence in Remote Patient Monitoring Market, By Technology

  • 6.1 Introduction
  • 6.2 Machine Learning
  • 6.3 NLP & Speech Recognition
  • 6.4 Other Technologies

7 Global Artificial Intelligence in Remote Patient Monitoring Market, By Application

  • 7.1 Introduction
  • 7.2 Cardiovascular Monitoring
  • 7.3 Neurological Disorders
  • 7.4 Diabetes Management
  • 7.5 Post-Acute Recovery
  • 7.6 Oncology
  • 7.7 Respiratory Disorders
  • 7.8 Other Applications

8 Global Artificial Intelligence in Remote Patient Monitoring Market, By End User

  • 8.1 Introduction
  • 8.2 Hospitals & Health Systems
  • 8.3 Homecare Settings
  • 8.4 Ambulatory Surgical Centers (ASCs)
  • 8.5 Patients
  • 8.6 Other End Users

9 Global Artificial Intelligence in Remote Patient Monitoring Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Koninklijke Philips
  • 11.2 Medtronic
  • 11.3 OMRON Healthcare
  • 11.4 GE HealthCare
  • 11.5 Biobeat
  • 11.6 Boston Scientific
  • 11.7 Dexcom
  • 11.8 Nihon Kohden
  • 11.9 F. Hoffmann-La Roche
  • 11.10 ResMed
  • 11.11 AliveCor
  • 11.12 Biotronik
  • 11.13 Honeywell
  • 11.14 Masimo
  • 11.15 Abbott

List of Tables

  • Table 1 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By AI-Enabled Devices (2024-2032) ($MN)
  • Table 4 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Wearables (2024-2032) ($MN)
  • Table 5 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Biosensors (2024-2032) ($MN)
  • Table 6 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Portable ECG & Glucose Monitors (2024-2032) ($MN)
  • Table 7 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Software (2024-2032) ($MN)
  • Table 8 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Data Analytics Platforms (2024-2032) ($MN)
  • Table 9 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Predictive Monitoring Tools (2024-2032) ($MN)
  • Table 10 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Services (2024-2032) ($MN)
  • Table 11 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Remote Monitoring Services (2024-2032) ($MN)
  • Table 12 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By AI Integration & Support (2024-2032) ($MN)
  • Table 13 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 15 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By NLP & Speech Recognition (2024-2032) ($MN)
  • Table 16 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 17 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Application (2024-2032) ($MN)
  • Table 18 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Cardiovascular Monitoring (2024-2032) ($MN)
  • Table 19 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Neurological Disorders (2024-2032) ($MN)
  • Table 20 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Diabetes Management (2024-2032) ($MN)
  • Table 21 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Post-Acute Recovery (2024-2032) ($MN)
  • Table 22 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 23 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Respiratory Disorders (2024-2032) ($MN)
  • Table 24 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 25 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By End User (2024-2032) ($MN)
  • Table 26 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Hospitals & Health Systems (2024-2032) ($MN)
  • Table 27 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Homecare Settings (2024-2032) ($MN)
  • Table 28 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Ambulatory Surgical Centers (ASCs) (2024-2032) ($MN)
  • Table 29 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Patients (2024-2032) ($MN)
  • Table 30 Global Artificial Intelligence in Remote Patient Monitoring Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.