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人工知能 (AI) 市場予測 2022年:医療ITの成長における主要なアプリケーション領域

Artificial Intelligence Market-Key Application Areas for Growth in Healthcare IT, Forecast to 2022

発行 Frost & Sullivan 商品コード 689273
出版日 ページ情報 英文 126 Pages
納期: 即日から翌営業日
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人工知能 (AI) 市場予測 2022年:医療ITの成長における主要なアプリケーション領域 Artificial Intelligence Market-Key Application Areas for Growth in Healthcare IT, Forecast to 2022
出版日: 2018年08月24日 ページ情報: 英文 126 Pages
概要

当レポートでは、世界の医療ITにおける人工知能 (AI) 市場について調査し、市場の概要、地域市場の動向と世界における産業ダイナミクス、技術概要、現在の市況と成長の展望、主な市場セグメント、市場への影響要因、主な市場参入企業と新興企業、および主な成長機会などについて分析しています。

第1章 エグゼクティブサマリー

第2章 人工知能 (AI) のイントロダクション

  • 人工知能とは何か?
  • AIとコグニティブコンピューティングの違いとは?
  • 人工知能の進化
  • AIを構成する技術とは?

第3章 市場概要

  • 医療における技術導入は新しい産業パラダイムを促進する変曲点に
  • 医療における人工知能:技術の枠組み
  • 医療におけるAIシステムの分類方法
  • 価値を生み出す方法とは?
  • 医療におけるAI:促進因子
  • 医療におけるAI:課題・影響・展望
  • AIのビジネスエコシステム:世界の展望
  • AIのビジネスエコシステム:米国市場はイノベーションの先駆けとなるべくうまく構成
  • AIのビジネスエコシステム:議論

第4章 地域的な視点

  • 世界の医療関連AIに対するKOLの見解
  • 調査手法
  • 世界の医療AI部門の比較評価

第5章 技術概要:主要アプリケーション領域

  • 市場区分
  • AI搭載の臨床管理:アプリケーション・アプリケーション領域
  • 臨床アプリケーション向けAI
  • オペレーションアプリケーション向けAI
  • AI搭載の財務マネジメント:アプリケーション・アプリケーション領域
  • 財務アプリケーション向けAI

第6章 導入の展望

  • 医療介護におけるAI導入の現状とは?
  • 医療IT市場へ提供されるAI製品のタイプ
  • AI製品の内訳:疾病分類・アプリケーション領域別
  • エンドユーザーのタイプ、医療介護を改善するためのAIの活用
  • AIから最も恩恵を受ける医療ITサービスのトップ10
  • 普及分析:病院
  • 普及分析:開業医
  • 普及分析:保険者

第7章 予測・動向:市場全体

  • 市場エンジニアリング測定
  • 予測の前提条件・定義
  • 市場収益予測:状況分析
  • 収益予測
  • 収益比予測:エンドユーザーセグメント
  • 収益予測:エンドユーザーセグメント
  • 収益予測:エンドユーザーセグメントの議論
  • 収益比:サービス産業
  • 収益比:サービス産業の議論

第8章 予測・動向:病院市場セグメント

  • 市場エンジニアリング測定
  • 病院市場:収益予測

第9章 予測・動向:開業医市場セグメント

第10章 予測・動向:保険者市場セグメント

第11章 競合情勢

  • HCITにおけるAIの競合情勢評価:アナリストのコメント
  • 顧客および収益/資金の内訳:医療ITにおけるAI
  • 調達プロセス:エンドユーザーへのAI販売方法
  • 主要ベンダーの環境エコシステム実例
  • 競合環境
  • 注目企業
  • 有望な企業:AI市場における主な新興企業

第12章 成長機会

  • 詳細の戦略に重要な5つの成長機会
  • 成長促進因子
  • 成長機会1:病気の予測
  • 成長機会2:個別化診断
  • 成長機会3:クリニカルドキュメンテーションの自動化
  • 成長機会4:事前許可申請
  • 成長機会5:コンプライアンスの最適化
  • 免責事項

第13章 付録

目次
Product Code: K26D-48

Optimizing Quality and Efficiency of Healthcare Delivery with AI

The study describes how AI technologies are paired with legacy healthcare IT systems to deal with the complexity & growth of medical data. Frost & Sullivan acknowledges that AI intuitively finds ways to convert these ever-expanding resources into actionable evidence for end users. In this study, Frost & Sullivan has forecast global revenue for primary healthcare IT segments* which leverage AI to augment product functionalities. Additionally, the study also features and assesses various progressive AI vendors, offering visionary capabilities that strive to normalize disparate patient data, apply scientific algorithm to generate healthcare evidences and drive cognitive reasoning to predict population health outcomes.

The global market revenue has been classified by these end users' corporate receptivity, behavioral agility, and financial ability to fund and sustain AI-enabled HC interventions that promise better outcomes for all.

Democratization of AI is on the horizon, which may disrupt the way healthcare has been perceived and delivered.

Today, AI-powered digital health solutions primarily allow healthcare enterprises to predict, automate, and prescribe evidence-based business decisions. However, AI-powered digital health solutions from technology maturity and real-word applicability standpoint are still in their infancy. Hence the technology penetration across all end users is low to moderate. It has been estimated that at present only around 20% of these end users would be actively using AI to drive real change in the way healthcare has been conceived and delivered. However, as leading IT companies such as IBM Watson Health, Google, Amazon, Microsoft, Philips, GE Healthcare and Salesforce have started to offer impeccable cloud services and tools to independent AI software developers, more progressive applications, capable of ensuring tangible ROI to end users would flourish and drive the overall market penetration for these technologies.

‘Big-Tech' is taking the lead to catalyse innovation in the healthcare AI market.

Google is primarily using AI to source and normalize EHR data to identify disease patterns that were historically untraceable. IBM continues to grow its presence in healthcare, driven by a focus on AI-powered clinical decision support, imaging analytics and population health management through its Watson Health business. Additionally, Amazon is making inroads into various aspects of healthcare AI (and could make a big move voice applications, cloud infrastructure solutions and visionary research works)-or several big moves-- in this year. Major EHR companies are committing to reduce physicians' IT burden by incorporating AI-enabled and voice based medical assistants into their incumbent EHR workflows. Epic recently announced that it has integrated Nuance's AI-powered virtual assistant platform into its EHR. Functionalities include the ability for clinicians to ask for lab results, medication lists, visit summaries and other information in the Epic Haiku mobile app. Athenahealth is also partnering with progressive start-ups that help physicians automate the process of patient scheduling, clinical documentation and coding.

Putting Patient Data into Work is a Critical Perquisite of Success for AI-powered health IT solutions.

“Big-tech” must acknowledge that patient generated data which next-generation IT platforms interpret has multiple utilities for diverse healthcare stakeholders. Fully informed consent from patients coupled with 100% compliance with stringent data usage regulation has to be ensured to remain relevant in the market.

Frost and Sullivan's new research report on Artificial Intelligence Market - Key Application Areas for Growth in Healthcare IT provides a comprehensive analysis on the global healthcare AI market which is primarily segmented into three service verticals that embrace a range of AI technologies to modernize care, personalize treatment and improve outcomes for key end users.

Key Regional Markets, Covered in the Research Study:

  • The United States of America
  • The United Kingdom
  • France
  • Germany
  • China
  • Switzerland
  • Japan
  • India

Types of AI Products Discussed in the Report:

  • SaaS Based
  • App Based
  • Integrated Hardware
  • Research-based
  • Doctor-facing
  • Patient-facing
  • Telehealth

Key Service Segments (Vendors are Selected by Sub-Segments), covered in the Research Study:*

  • Clinical Applications
  • EMR
  • Clinical Decision Support Systems
  • Clinical Analytics
  • Imaging IT
  • Precision Medicine/Genomics IT
  • Wearable
  • Femtech
  • Mental Health
  • Patient Engagement
  • Population Health Management
  • Telehealth
  • Operational Applications
  • Administrative Management
  • Supply Chain Management
  • Enterprise Content Management
  • Cybersecurity
  • Financial Applications
  • Business Intelligence
  • Revenue Cycle Management
  • Coding and Claims Management

Key Disease Categories, covered in the Research Study:

  • Cancer
  • Cardiology
  • Diabetes
  • Endometriosis
  • Infectious Diseases
  • Rare Diseases

*Adoption of AI for the pharmaceutical market, as it relates to digital health, is also discussed but is not included in the market revenue numbers.

Key Issues Addressed:

  • What are the regional market trends and industry dynamics in global AI for healthcare IT market? What is the future potential for AI-powered healthcare IT solutions?
  • What is the current market scenario? How much growth is expected? Which are the major market segments? What will be the impact of external trends on each business segment?
  • Who are the major participants in the global AI for healthcare IT market? What and how are they innovating in this space?
  • What are the important business model considerations (penetration, pricing and profitability information) for healthcare stakeholders and providers? Who should pay for healthcare AI products? Is there an untapped opportunity in this market?

Table of Contents

1. EXECUTIVE SUMMARY

  • Executive Summary
  • Key Findings-Opportunity Analysis: AI for Clinical Applications
  • Key Findings-Opportunity Analysis: AI for Operational Applications
  • Key Findings-Opportunity Analysis: AI for Financial Applications
  • Regional Market Key Findings-AI Footprint across the World
  • Market Engineering Measurements
  • Market Engineering Measurements (continued)
  • CEO's Perspective
  • 3 Big Predictions

2. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

  • What Really is Artificial Intelligence?
  • What is the Difference Between AI and Cognitive Computing?
  • The Evolution of Artificial Intelligence
  • What Technologies Constitute AI?

3. MARKET OVERVIEW

  • Technology Adoption in Healthcare is at an Inflexion Point that is Driving New Industry Paradigms
  • Artificial Intelligence in Healthcare-A Technical Framework
  • How to Categorize AI Systems in Healthcare
  • How is the Value Being Created?
  • AI in Healthcare-Drivers
  • AI in Healthcare-Challenges, Impact and Outlook
  • The Business Ecosystem of AI-Global Perspective
  • The Business Ecosystem of AI-The US Market is Well Organized to Pioneer Innovation
  • The Business Ecosystem of AI-Discussion
  • The Business Ecosystem of AI-Discussion (continued)

4. REGIONAL VIEWS

  • What KOLs Perceive of AI's Relevancy in Healthcare Across the Globe (In Order of Importance)
  • Methodology For the Following Slides
  • Comparative Assessment of the Global Healthcare AI Sector
  • Comparative Assessment of the Global Healthcare AI Sector (continued)
  • Comparative Assessment of the Global Healthcare AI Sector (continued)
  • Comparative Assessment of the Global Healthcare AI Sector (continued)

5. TECHNOLOGY OVERVIEW-KEY APPLICATION AREAS

  • Market Segmentation
  • AI Powered Clinical Management-Application and Category Areas
  • AI for Clinical Applications
  • AI for Clinical Applications (continued)
  • AI for Clinical Applications (continued)
  • AI for Clinical Applications (continued)
  • AI Powered Operations Management-Application and Category Areas
  • AI for Operational Applications
  • AI for Operational Applications (continued)
  • AI for Operational Applications (continued)
  • AI for Operational Applications (continued)
  • AI Powered Financial Management-Application and Category Areas
  • AI for Financial Applications
  • AI for Financial Applications (continued)
  • AI for Financial Applications (continued)
  • AI for Financial Applications (continued)

6. ADOPTION OUTLOOK

  • What is the State of AI Adoption Across Care Delivery?
  • Types of AI Products Offered to the Healthcare IT Market
  • AI Product Breakdown by Disease Categories and Application Areas
  • Types of End Users, Leveraging AI to Improve Patient Care
  • Top 10 Healthcare IT Services that will Benefit Most from AI
  • Top 10 Healthcare IT Services that will Benefit Most from AI (continued)
  • Penetration Analysis-Hospitals
  • Penetration Analysis-Physician Practices
  • Penetration Analysis-Payers

7. FORECASTS AND TRENDS-TOTAL MARKET

  • Market Engineering Measurements
  • Market Engineering Measurements (continued)
  • Forecast Assumptions and Definitions
  • Forecast Assumptions and Definitions (continued)
  • Market Revenue Forecast-Scenario Analysis
  • Revenue Forecast
  • Percent Revenue Forecast by End-user Segment
  • Revenue Forecast by End-user Segment
  • Revenue Forecast by End-user Segments Discussion
  • Percent Revenue by Service Verticals
  • Percent Revenue by Service Verticals Discussion

8. FORECASTS AND TRENDS-HOSPITALS MARKET SEGMENT

  • Market Engineering Measurements
  • Market Engineering Measurements (continued)
  • Hospital Market-Revenue Forecast

9. FORECASTS AND TRENDS-PHYSICIAN PRACTICES MARKET SEGMENTS

  • Market Engineering Measurements
  • Market Engineering Measurements (continued)
  • Physician Practices Market-Revenue Forecast

10. FORECASTS AND TRENDS-PAYERS MARKET SEGMENTS

  • Market Engineering Measurements
  • Market Engineering Measurements (continued)
  • Total Payer Market-Revenue Forecast

11. COMPETITIVE LANDSCAPE

  • Competitive Landscape Assessment for AI in HCIT-Analyst Commentary
  • Competitive Landscape Assessment for AI in HCIT-Analyst Commentary (continued)
  • Customer and Revenue/Funding Break-up-AI in Healthcare IT
  • Procurement Process-How AI is Sold to End Users
  • Select Vendor Environment Ecosystem Examples
  • Competitive Environment
  • Competitive Environment (continued)
  • Competitive Environment (continued)
  • Competitive Environment (continued)
  • Competitive Environment (continued)
  • Companies to Watch
  • Companies to Watch (continued)
  • Promising Participants-Key New Companies in the AI Space
  • Promising Participants-Key New Companies in the AI Space (continued)

12. GROWTH OPPORTUNITIES

  • 5 Growth Opportunities Critical for Future Strategies
  • Levers for Growth
  • Growth Opportunity 1-Predict Diseases
  • Growth Opportunity 2-Personalize Diagnosis
  • Growth Opportunity 3-Automate Clinical Documentation
  • Growth Opportunity 4-Pre-authorize Claims
  • Growth Opportunity 5-Optimize Compliance
  • Strategic Imperatives for AI in Healthcare IT
  • Legal Disclaimer

13. APPENDIX

  • Selected Sources
  • Selected Sources (continued)
  • List of Exhibits
  • List of Exhibits (continued)
  • List of Exhibits (continued)