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市場調查報告書

數位健康的全球市場

Digital Health

出版商 Juniper Research 商品編碼 276665
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
價格
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數位健康的全球市場 Digital Health
出版日期: 2017年09月05日 內容資訊: 英文
簡介

本報告提供帶給醫療保健服務大變化的數位技術的相關調查,AI (人工智能) 、聊天機器人及數位語音助手的顛覆性技術分析,目前引進情形和未來展望相關討論,主要的地區市場分析,新興市場上數位健康方法的展望等彙整資料。

Deep Dive 策略 & 競爭

第1章 數位健康市場形勢

  • 簡介
  • 主要市場
    • 北美
    • 西歐
    • 遠東 & 中國
  • 數位健康投資形勢
  • 數位健康市場趨勢,促進要素 & 抑制因素
    • 主要的促進要素
    • 主要趨勢
    • 主要的抑制因素
    • 建議

第2章 醫療保健產業的數位化

  • 技術組成架構
    • 穿戴式,生物感測 & 數位醫療設備
    • 遙控病患監測
    • EHR
    • 行動醫療應用
    • 遠端保健
    • 巨量資料/雲端引進
    • 醫療保健的AI
  • 新興技術、醫療保健
    • 聊天機器人
    • 塊環鏈
    • 數位語音助手

第3章 新興市場上數位健康

  • 新興市場上醫療保健
    • 新興市場上行動醫療
    • 新興市場上巨量資料
    • 改善醫療保健訓練的技術
    • Juniper的建議

第4章 數位健康企業簡介 & 定位

  • 簡介
  • 供應商分析、Juniper Leaderboard
  • Juniper's Disruptors & Challengers Quadrant
  • 數位健康的主要企業
  • 企業簡介
    • AirStrip
    • Allscripts
    • Cerner
    • CirrusMD
    • Conversa Health
    • DeepMind
    • eClinicalWorks
    • Essence
    • IBM
    • iHealth
    • Orbita
    • Push Doctor
    • Valencell

Deep Dive 資料 & 預測

第1章 數位健康市場預測摘要

  • 簡介
  • 數位健康市場預測摘要

第2章 EHR (電子病歷) 市場預測

  • 簡介
  • EHR的預測

第3章 遠端患者監護市場預測

  • 簡介
  • 整體遠端患者監護預測
  • 心臟的遠程監控預測
  • 慢性疾病的遠程監控預測

第4章 雲端發展市場預測

  • 簡介

第5章 行動醫療資訊服務市場預測

  • 簡介

第6章 行動醫療硬體設備設備市場預測

  • 簡介

第7章 醫療保健市場預測的AI發展

  • 簡介

第8章 醫療保健市場上聊天機器人預測

  • 簡介

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目錄

Overview

Juniper's latest Digital Health research provides expert insight, examining the digital technologies that are revolutionising healthcare delivery. It analyses disruptive technologies, such as AI (artificial intelligence), chatbots and digital voice assistants, discussing deployments to date and their future prospects.

The research also offers key regional market analysis, examining the healthcare systems of significant nations and assessing their readiness for digital health integration. It provides a detailed analysis on the prospects for digital health approaches in emerging markets, where the challenges to adoption are quite different to developed markets.

This research suite comprises:

  • Deep Dive Strategy and Competition (PDF)
  • Deep Dive Data and Forecasting (PDF & Excel)
  • Executive Summary & Core Findings (PDF)

Key Features

  • Market Landscape: Detailed analysis of the present market state and future outlook for digital health, examining:
    • Key market analysis of levels of readiness for digital health adoption
    • Investment landscape for digital health start-ups
    • Key market trends, drivers and constraints acting on the digital health market
    • Digital health opportunities and strategic recommendations for vendors in the marketplace
  • Sector Analysis: Juniper's strategic guide to which digital technologies are being integrated into healthcare systems including:
    • Wearables, biosensing and digital medical devices
    • Remote patient monitoring and telehealth
    • EHRs (electronic health records)
    • mHealth applications
    • Big Data & AI
    • Chatbots and digital voice assistants
    • Blockchain
  • Benchmark Industry Forecasts: Understand the size of the Digital Health market and where the growth will take place with our highly granular dataset. We Identify the key opportunities, covering theoretical savings and costs, adoption of the various technologies, revenues from these technologies and more, for 8 global regions and 11 key country markets, covering these segments:
    • Healthcare wearables
    • Remote patient monitoring
    • EHRs (electronic health records)
    • mHealth applications
    • Cloud deployment
    • Chatbots
    • AI
  • Interviews: Unique insight into how each player is competing in this market, including:
    • AirStrip
    • CirrusMD
    • Conversa Health
    • DeepMind Health
    • Essence
    • Orbita
    • Push Doctor
    • Valencell
  • Juniper Leaderboard: 12 leading digital health vendors compared, scored and positioned on the Juniper Leaderboard matrix.

Key Questions

  • 1. Which sectors of the digital healthcare market are positioned for success over the next 5 years?
  • 2. What impact will disparate healthcare systems and varying levels of government involvement have on digital adoption?
  • 3. How will AI in healthcare adoption benefit the healthcare industry and what barriers are there to adoption?
  • 4. What impact will the drive for interoperability have on patient experiences?
  • 5. How will mHealth impact developing markets?

Companies Referenced

  • Interviewed: AirStrip, CirrusMD, Conversa Health, DeepMind, Essence, Hyperledger, Orbita, Push Doctor, Valencell.
  • Profiled: AirStrip, AllScripts, Cerner, CirrusMD, Conversa Health, DeepMind, eClinicalWorks, Essence, IBM, iHealth, Orbita, Push Doctor, Valencell.
  • Case Studied: Cerner, DeepMind, Hello Doctor, IBM, Leap, Mount Sinai Hospital, NHS (National Health Service), Nokia Health, Revinax, Valencell.
  • Listed in Juniper's Disruptors & Challengers Quadrant: Ada Health, AiCure, Atomwise, Babylon Health, CareSkore, Deep Genomics, Desktop Genetics, iCarbonX, Infermedica, Zephyr Health.
  • Mentioned: Accenture, Aetna, AliveCor, Alphabet, Amazon, American Diabetes Association, Amref Health Africa, Andon Health Group, Apple, Asus, AT&T, Athenahealth, Atlas, Atos, BabyCenter, Barrow Neurological Institute, Blizzard Entertainment, Bose, Boston Medical Center, CareCross Health, Carequality Interoperability Framework, CCA (Commonwealth Care Alliance), Center for Advanced Professional Studies, Centura Health, CHCS (Center for Health Care Strategies), CHMB, Citrus Valley Health Partners, Color Genomics, CureMetrix, Dataware, Department of Health and Human Services, Deutschland Land der Ideen, Digital Equipment Corporation, Dignity Health, Diversinet, Elixir Studios, Emergency Medicine Associates, Emergency Medicine Consultants, Fast Search & Transfer, FDA (Food and Drug Administration), First Aid, Firstbeat, Fitbit, G4S, Garmin, Gary and Mary West Health Investment Fund, GE Healthcare, Google, Guardant Health, Healthline Networks, Henry Schein, HIMSS (Healthcare Information and Management Systems Society), HL7 (Health Level Seven International), HLI (Human Longevity Inc), HP, Huawei, iCarbonX, ICO (Information Commissioner's Office), Illumina, Invision Heart, iProcedures, Jabra, Kaiser Permanente, Kernel, Leerink Partners, Leidos, Leman Micro Devices, LG, Livongo, Lloyds Pharmacy, Map My Run, Maryland Physicians Care, MCIS, McKesson, McKinsey & Company, Medical Guardian, Medizinische Hochschule Hanover, Meiyou, Memorial Sloan Kettering, Merck, Microsoft, Missouri Innovation Campus, Moberg, Mobile Heartbeat, Momentum, Moorfields Eye Hospital, M-Pesa Foundation, MTN, Music Magpie, Natali, Nestlé, Netcentives, Nike, Northwell, Obix, Ochsner Health System, OECD (Organisation for Economic Co-operation and Development), Onduo, One Partner, Oscar, Pacific Marketing Group, PatientsLikeMe, PeakMed, Physio Control, Ping An Good Doctor, PointClickCare, Practice Fusion, Practo, Pulse 8, Quest Diagnostics, RAMP Holdings, Rocky Mountain Health Plans, Royal Free Hospital London, Safaricom, Samsung, Sanpower Group, SAP, Sapias, Scosche, Siemens AG, Sony, Sotera Wireless, St Joseph Health, Strava, Suunto, Tactio, Telecom Italia, Telekom Slovenije, TeleTracking, Texas Health Resources, The Weather Company, Tictrac, Uber, UCLH (University College London Hospital), UnitedHealth Group, US DoD (Department of Defense), US Office of the National Coordinator for Health IT, US VA (Department of Veteran's Affairs), Verily, Virta, Vodafone, WeDoctor, Wellcome Trust, WLSA (World Leading Schools Association), Xiaomi.

Data & Interactive Forecast

Juniper's latest Digital Health forecast suite includes:

  • Market data splits for 8 key global regions and 11 countries, including:
    • Bangladesh
    • Canada
    • Germany
    • Kenya
    • India
    • Indonesia
    • South Africa
    • Sweden
    • Tanzania
    • UK
    • US
  • New forecasts for Chatbot Deployment, and AI in Healthcare (computer aided diagnosis systems)
  • Remote patient monitoring, split by:
    • Chronic Disease Management
    • Cardiac Outpatients
  • EHR Implementation & Ongoing Administration Costs, and Net Cost Savings
  • Cloud Adoption in Healthcare & Associated Service Revenue
  • mHealth forecasts, split by:
    • Information Services: Adoption & Service Revenue
    • Healthcare Wearables: Adoption & Revenues
  • Interactive Scenario tool allowing user the ability to manipulate Juniper's data for 23 different metrics.
  • Access to the full set of forecast data of 113 tables and over 19,150 data points.

Juniper Research's highly granular IFxls (interactive forecast excels) enable clients to manipulate Juniper's forecast data and charts to test their own assumptions using the Interactive Scenario Tool; and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

Deep Dive Strategy & Competition

1. Digital Health Market Landscape

  • 1.1. Introduction
    • 1.1.1. Digital Health & Emerging Technologies
  • 1.2. Key Markets
    • 1.2.1. North America
      • i. Healthcare Systems
        • Figure 1.1: Healthcare Spending as a Percentage of GDP (%)
      • ii. Key Deployments
    • 1.2.2. West Europe
      • i. Healthcare Systems
      • ii. Key Deployments
    • 1.2.3. Far East & China
      • i. Healthcare Systems
        • Figure 1.2: Proportion of Population Aged 65 and Above (%)
      • ii. Key Deployments
        • Figure 1.3: My Number Mascot ‘Maina-chan'
  • 1.3. Digital Health Investment Landscape
    • Figure 1.4: Digital Health Investment Landscape
  • 1.4. Digital Health Market Trends, Drivers & Constraints
    • Figure 1.5: Digital Health Trends, Drivers & Constraints
    • 1.4.1. Key Drivers
      • i. Technological
      • ii. Consumer
    • 1.4.2. Key Trends
      • iii. Vendor
      • i. Technological
      • ii. Consumer
      • iii. Vendor
    • 1.4.3. Key Constraints
      • i. Technological
      • ii. Consumer
      • iii. Vendor
        • Figure 1.6: Number of Individuals Affected by Declared Healthcare Data Breaches in the US, July 2016-June 2017
    • 1.4.4. Recommendations

2. Digitising the Healthcare Industry

  • 2.1. Technological Framework
    • Figure 2.1: Juniper Digital Health Industry Components
    • Figure 2.2: Juniper Digital Healthcare Ecosystem
    • 2.1.1. Wearables, Biosensing & Digital Medical Devices
      • i. Case Study: Nokia Health
    • 2.1.2. Remote Patient Monitoring
      • i. Fall Detection / Home Living
      • ii. Diabetes Management
      • iii. Remote Cardiac Monitoring
      • iv. Case Study: Valencell
    • 2.1.3. EHRs (Electronic Health Records)
      • Figure 2.3: MCIS EHR Screenshot
      • i. Case Study: Cerner
      • ii. Interoperability
        • Figure 2.4: EIements Driving EHRInteroperability
    • 2.1.4. mHealth Applications
      • Table 2.5: Health Apps Compared: Apple, Google, Samsung, LG & Huawei
    • 2.1.5. Telehealth
      • i. Case Study: Push Doctor
    • 2.1.6. Big Data/Cloud Adoption
      • i. Privacy Concerns
      • ii. Case Study: DeepMind Health Streams App
    • 2.1.7. AI in Healthcare
      • i. CAD (Computer Aided Diagnosis)
      • ii. Genomics
      • iii. Case Study: IBM Watson for Genomics
      • iv. Challenges to AI Adoption
      • v. Conclusion
      • vi. Case Study: Deep Patient
  • 2.2. Emerging Technologies & Healthcare
    • 2.2.1. Chatbots
      • Figure 2.7: Louise in Use for Patient Care
      • i. Case Study: NHS Chatbot Trials
    • 2.2.2. Blockchain
    • 2.2.3. Digital Voice Assistants
      • Figure 2.8: Amazon Echo Range &Google Home
      • i. Medication/Treatment Adherence
      • ii. Vision Impairment/Disability Care Delivery
      • iii. Dementia
      • iv. Clinical Trials
      • v. Post Diagnosis
      • vi. Challenges
      • vii. Conclusion

3. Digital Health in Emerging Markets

  • 3.1. Healthcare in Emerging Markets
    • i. LowerG DP Resulting in Less Advanced Healthcare Provision
      • Figure 3.1: GDP per Capita (current $), Selected Countries
    • ii. High Rates of Chronic Disease
      • Figure 3.2: Prevalence of HIV Among Adults Aged 15 to 49(%)
    • iii. Lower Technological Infrastructure
    • iv. Lower Profitability for Large Companies
    • 3.1.2. mHealth in Emerging Markets
      • i. Case Study: Hello Doctor
    • 3.1.3. Big Data in Emerging Markets
    • 3.1.4. Technology Improving Healthcare Training
      • i. VR (Virtual Reality) Training
      • ii. Case Study - Revinax VR Tutorials
      • iii. mHealth Training
      • iv. Case Study: Leap
    • 3.1.5. Juniper's Recommendations
      • Figure 3.3: SIM Connection Penetration, Kenya and Tanzania, 2017-22(%)

4. Digital Health Player Profiles & Positioning

  • 4.1. Introduction
  • 4.2. Vendor Analysis & Juniper Leaderboard
    • 4.2.1. Stakeholder Assessment Criteria
      • Table 4.1: Digital Health Player Capability Criteria
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 4.2.4. Limitations & Interpretation
  • 4.3. Juniper's Disruptors & Challengers Quadrant
    • Figure 4.4: Juniper AI in Digital Health Disruptors & Challengers Quadrant
    • 4.3.1. Disruptors
    • 4.3.2. Catalyst Players
    • 4.3.3. Nascent Players
    • 4.3.4. Embryonic Players
  • 4.4. Digital Health Movers & Shakers
  • 4.5. Player Profiles
    • 4.5.1. AirStrip
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.2. Allscripts
      • i. Corporate
        • Table 4.5: Allscripts Financial Snapshot 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.3. Cerner
      • i. Corporate
        • Table 4.6: Cerner Financial Snapshot 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.4. CirrusMD
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.5. Conversa Health
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.6. DeepMind
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.7. eClinicalWorks
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.8. Essence
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.9. IBM
      • i. Corporate
        • Figure 4.7: IBM Financial Snapshot 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.10. iHealth
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.11. Orbita
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.12. Push Doctor
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 4.5.13. Valencell
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities

Deep Dive Data & Forecasting

1. Digital Health Market Forecast Summary

  • 1.1. Introduction
  • 1.2. Digital Health Market Forecast Summary
    • 1.2.1. Total Cost Savings from Implementing Digital Health Technology
      • Figure & Table 1.1:Total Theoretical Cost Savings from Implementing Digital Health Technologies ($m), Split by 8 Key Regions 2017-2022
    • 1.2.2. Total Revenue Generated from Implementing Digital Health Technology

2. EHRs (Electronic Health Records) Market Forecasts

  • 2.1. Introduction
    • Figure & Table 1.2: Total Revenue Generated by Implementing Digital Health Technologies ($m), Split by 8 Key Regions 2017-2022
    • Table 2.1: Meaningful Use Stages & Objectives
    • 2.1.1. Forecast Methodology
      • Figure 2.2: Digital Electronic Health Records Forecast Methodology
  • 2.2. EHR Forecasts
    • 2.2.1. Number of Individuals with EHRs
      • Figure & Table 2.3: Number of Individuals with EHRs (m) Split by 8 Key Regions 2017-2022
    • 2.2.2. Gross Total Theoretical Annual Cost Savings
      • Figure & Table 2.4: Gross Total Theoretical Annual Cost Savings ($m) Split by 8 Key Regions 2017-2022
    • 2.2.3. Total Implementation & Ongoing Admin Costs
      • Figure & Table 2.5: Total Implementation & Ongoing Admin Costs ($m) Split by 8 Key Regions 2017-2022
    • 2.2.4. Net Cost Savings Achievable through EHR Implementation
      • Figure & Table 2.6: Net Cost Savings Achievable through EHR Implementation ($m) Split by 8 Key Regions 2017-2022

3. Remote Patient Monitoring Market Forecasts

  • 3.1. Introduction
    • 3.1.1. Forecast Methodology
      • Figure 3.1: Remote Monitoring Forecast Methodology
  • 3.2. Total Remote Patient Monitoring Forecasts
    • 3.2.1. Total Number of Individuals Remotely Monitored
      • Figure & Table 3.2: Total Number of Individuals Remotely Monitored (m) Split by 8 Key Regions 2017-2022
    • 3.2.2. Total Revenue from All Monitored Individuals
      • Figure & Table 3.3: Total Revenue from All Monitored Individuals ($m) Split by 8 Key Regions 2017-2022
      • Figure & Table 3.3: Total Revenue from All Monitored Individuals ($m)Split by 8 Key Regions 2017-2022
  • 3.3. Cardiac Remote Monitoring Forecasts
    • 3.3.1. Number of Individuals Adopting Remote Cardiac Patient Monitoring
      • Figure & Table 3.4: Number of Individuals Adopting Remote Cardiac Patient Monitoring (m)Split by 8 Key Regions 2017-2022
    • 3.3.2. Service Revenue from Remote Cardiac Outpatient Monitoring
      • Figure & Table 3.5: Service Revenue from Remote Cardiac Outpatient Monitoring ($m) Split by 8 Key Regions 2017-2022
  • 3.4. Chronic Disease Remote Monitoring Forecasts
    • 3.4.1. Number of Individuals Adopting Remote Patient Monitoring
      • Figure & Table 3.6: Number of Individuals Adopting Chronic Disease Remote Patient Monitoring (m) Split by 8 Key Regions 2017-2022
    • 3.4.2. Service Revenue from Remote Patient Monitoring for Chronic Disease Outpatients
      • Figure & Table 3.7: Service Revenue from Remote Patient Monitoring for Chronic Disease Outpatients ($m) Split by 8 Key Regions 2017-2022

4. Cloud Deployment Market Forecasts

  • 4.1. Introduction
    • 4.1.1. Forecast Methodology
      • Figure 4.1: Healthcare Cloud Forecasts Methodology
    • 4.1.2. Number of Physicians Using Cloud Services
      • Figure & Table 4.2: Number of Physicians Using Cloud Services ('000) Split by 8 Key Regions 2017-2022
    • 4.1.3. Total Healthcare Spend on Cloud Services
      • Figure & Table 4.3: Total Healthcare Spend on Cloud Services ($m) Split by 8 Key Regions 2017-2022

5. mHealth Information Services Market Forecasts

  • 5.1. Introduction
    • 5.1.1. Forecast Methodology
      • Figure 5.1:mHealth Information Services Forecast Methodology
    • 5.1.2. Number of People Using mHealth Information Services
      • Figure & Table 5.2: Number of People Using mHealth Information Services (m) Split by 8 Key Regions 2017-2022
    • 5.1.3. Number of People Paying for mHealth Information Services
      • Figure & Table 5.3: Number of People Paying for mHealth Information Services (m) Split by 8 Key Regions 2017-2022
    • 5.1.4. Total Revenue from mHealth Information Services
      • Figure & Table 5.4: Total Revenue from mHealth Information Services ($m) Split by 8 Key Regions 2017-2022

6. mHealth Hardware Devices Market Forecasts

  • 6.1. Introduction
    • 6.1.1. Forecast Methodology
      • Figure 6.1:mHealth Hardware Device Forecast Methodology
  • 6.2. mHealth Device Forecasts
    • 6.2.1. Total Number of mHealth Hardware Devices in Service
      • Figure & Table 6.2: Total Number of mHealth Hardware Devices in Service (m) Split by 8 Key Regions 2017-2022
    • 6.2.2. Number of mHealth Hardware Devices that Generate Subscription Revenue
      • Figure & Table 6.3: Number of mHealth Hardware Devices that Generate Subscription Revenue (m) Split by 8 Key Regions 2017-2022
  • 6.3. mHealth Revenue Forecasts
    • 6.3.1. Service Revenue from mHealth Hardware Devices
      • Figure & Table 6.4: Service Revenue from mHealth Hardware Devices ($m) Split by 8 Key Regions 2017-2022
    • 6.3.2. Value of mHealth Hardware Device Sales
      • Figure & Table 6.5: Value of mHealth Hardware Device Sales ($m) Split by 8 Key Regions 2017-2022

7. AI Deployment in Healthcare Market Forecasts

  • 7.1. Introduction
    • 7.1.1. Forecast Methodology
      • Figure 7.1: AI CAD Forecast Methodology
    • 7.1.2. Total Scans Analysed by First Line CAD Software
      • Figure & Table 7.2: Total Number of Scans Analysed by First Line CAD Software (m), Split by 8 Key Regions 2017-2022
    • 7.1.3. Total Scans Analysed by Second Line CAD Software
      • Figure & Table 7.3: Total Number of Scans Analysed by Second Line CAD Software (m), Split by 8 Key Regions 2017-2022
    • 7.1.4. Total Spending on CAD Systems
      • Figure & Table 7.4: Total System Spending on CAD ($m), Split by 8 Key Regions 2017-2022
    • 7.1.5. Total Theoretical Cost Savings, Utilising First-line CAD Systems
      • Figure & Table 7.5: Total System Spending on CAD ($m), Split by 8 Key Regions 2017-2022

8. Chatbots in Healthcare Market Forecasts

  • 8.1. Introduction
    • 8.1.1. Forecast Methodology
      • Figure 8.1: Healthcare Chatbot Forecast Methodology
    • 8.1.2. Total Number of Chatbot-based Healthcare Interactions
      • Figure & Table 8.2: Total Number of Chatbot-based Healthcare Interactions (m) Split by 8 Key Regions 2017-2022
    • 8.1.3. Total Time Saved for Businesses as a Result of Chatbot Interactions
      • Figure & Table 8.3: Total Time Saved for Businesses as a Result of Chatbot Interactions, in Hours (m), Split by 8 Key Regions 2017-2022
    • 8.1.4. Total Cost Savings as a Result of Chatbot Interactions
      • Figure & Table 8.4: Total Cost Saved as a Result of Chatbot Interactions ($m) Split by 8 Key Regions 2017-2022
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