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ドイツのヘルスケアにおける人工知能（AI）市場予測 2024-2032

ドイツのヘルスケアにおける人工知能（AI）市場予測 2024-2032


GERMANY ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE MARKET FORECAST 2024-2032

主な調査結果 ドイツのヘルスケアにおける人工知能（AI）市場は、予測期間2024-2032年にCAGR 36.00％で成長すると予測される。AI導入を支える大量の医療データ、AI・機械学習スタートアップ企業の増加、臨床意思... もっと見る

　

　

出版社 出版年月 電子版価格 納期 ページ数 言語
Inkwood Research
インクウッドリサーチ 		2024年3月13日 US$1,100
シングルユーザライセンス（印刷不可）
ライセンス・価格情報
注文方法はこちら 		2-3営業日以内 154 英語

　

サマリー

主な調査結果
ドイツのヘルスケアにおける人工知能（AI）市場は、予測期間2024-2032年にCAGR 36.00％で成長すると予測される。AI導入を支える大量の医療データ、AI・機械学習スタートアップ企業の増加、臨床意思決定のための検査における個別化医療の出現、AIによるリアルタイムモニタリングシステムの構築など、いくつかの要因が市場成長をエスカレートさせている。
市場インサイト
ドイツのヘルスケア分野における人工知能（AI）市場は、精密医療と個別化医薬品の開発に著しい重点が置かれている。この戦略的焦点は、医療を個人の特性に最適化し、患者の転帰をカスタマイズし、副作用を最小限に抑えるという市場のコミットメントを反映している。AIアルゴリズムを活用する精密医療は、遺伝子情報、患者の病歴、臨床データを含む広範なデータセットの評価に取り組む。この包括的な方法論により、医療専門家は的確な介入策を策定できるようになり、より個別化された有効な治療アプローチが提供される。
さらに、遺伝学におけるAIの活用が進むことで、ドイツの医療分野は再構築されつつある。AI技術は遺伝学研究において不可欠であり、様々な疾患に関連する潜在的な遺伝子マーカーの同定を助ける。遺伝学におけるAIの統合は、複雑なゲノムデータの調査を迅速化し、疾患リスク予測の精度を高める。その結果、医療従事者はこの先端技術を活用することで、病気の予防、早期診断、個別化された治療計画に関して、より多くの情報に基づいた意思決定を行うことができる。
さらに、AIはリアルタイムのモニタリングシステムを構築することで、医療に変革をもたらしつつある。この根本的な変化により、患者の健康指標を継続的かつ瞬時に追跡できるようになり、積極的な介入と個別ケアが可能になる。AIを活用したリアルタイム・モニタリング・システムは、ウェアラブル端末や電子カルテなど、さまざまなソースからのデータを分析・解釈することができる。この機能により、健康異常の早期発見、タイムリーな介入、治療レジメンの最適化が容易になる。リアルタイム・モニタリング・システムへのAIの統合は、ドイツにおける、より効率的で患者中心の医療への進歩的な一歩を示すものである。
競争に関する洞察
ドイツのヘルスケアにおける人工知能（AI）市場の主要企業には、GE HealthCare、Intel Corporation、Google、IBM Corporationなどがあります。
弊社のレポート提供内容は以下のとおりです：
- 市場全体の主要な調査結果を探る
- 市場ダイナミクス（促進要因、阻害要因、機会、課題）の戦略的内訳
- 全セグメント、サブセグメント、地域の3年間の過去データとともに、最低9年間の市場予測
- 市場セグメンテーション：主要セグメントの徹底的な評価と市場予測
- 地域別分析：言及された地域と国レベルのセグメントを市場シェアとともに評価
- 主要分析：ポーターのファイブフォース分析、ベンダーランドスケープ、オポチュニティマトリックス、主要購買基準など。
- 競争環境は、要因や市場シェアなどに基づく主要企業の理論的説明である。
- 企業プロファイリング：詳細な会社概要、提供する製品・サービス、SCOT分析、最近の戦略的展開など

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目次

TABLE OF CONTENTS
1. RESEARCH SCOPE & METHODOLOGY
1.1. STUDY OBJECTIVES
1.2. METHODOLOGY
1.3. ASSUMPTIONS & LIMITATIONS
2. EXECUTIVE SUMMARY
2.1. MARKET SIZE & ESTIMATES
2.2. COUNTRY SNAPSHOT
2.3. COUNTRY ANALYSIS
2.4. SCOPE OF STUDY
2.5. CRISIS SCENARIO ANALYSIS
2.6. MAJOR MARKET FINDINGS
2.6.1. SOFTWARE OFFERINGS ARE LEADING THE ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE MARKET, DRIVING INNOVATION AND EFFICIENCY
2.6.2. NATURAL LANGUAGE PROCESSING DOMINATING ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE TECHNOLOGY
2.6.3. HEALTHCARE PROVIDERS ARE THE MAJOR USERS OF ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE
2.6.4. DOSAGE ERROR REDUCTION IS THE FASTEST-GROWING APPLICATION
3. MARKET DYNAMICS
3.1. KEY DRIVERS
3.1.1. LARGE VOLUMES OF HEALTHCARE DATA SUPPORTING THE ADOPTION OF AI
3.1.2. GROWING NUMBER OF AI AND MACHINE LEARNING START-UPS
3.1.3. EMERGENCE OF PERSONALIZED MEDICINE IN TESTS FOR CLINICAL DECISION-MAKING
3.1.4. AI CREATING A REAL-TIME MONITORING SYSTEM
3.2. KEY RESTRAINTS
3.2.1. SLOW ADOPTION OF AI-BASED TECHNOLOGIES
3.2.2. CHALLENGES IN MAINTAINING DATA SECURITY
3.2.3. COST CONSTRAINTS AND LOW RETURN ON INVESTMENT (ROI)
4. KEY ANALYTICS
4.1. KEY MARKET TRENDS
4.1.1. WIDENING APPLICATIONS OF AI IN THE HEALTHCARE INDUSTRY
4.1.2. INCREASING DEMAND FOR AI IN DRUG DISCOVERY
4.1.3. HIGH EMPHASIS ON THE DEVELOPMENT OF PRECISION MEDICINE AND PERSONALIZED DRUGS
4.1.4. INCREASING USE OF AI IN GENETICS
4.1.5. AI CREATING A REAL-TIME MONITORING SYSTEM
4.2. PESTLE ANALYSIS
4.2.1. POLITICAL
4.2.2. ECONOMICAL
4.2.3. SOCIAL
4.2.4. TECHNOLOGICAL
4.2.5. LEGAL
4.2.6. ENVIRONMENTAL
4.3. PORTER’S FIVE FORCES ANALYSIS
4.3.1. BUYERS POWER
4.3.2. SUPPLIERS POWER
4.3.3. SUBSTITUTION
4.3.4. NEW ENTRANTS
4.3.5. INDUSTRY RIVALRY
4.4. VALUE CHAIN ANALYSIS
4.4.1. DATA WAREHOUSE
4.4.2. ARTIFICIAL INTELLIGENCE (AI) ANALYSIS
4.4.3. SOFTWARE DEVELOPMENT
4.5. KEY BUYING CRITERIA
4.5.1. APPLICATION
4.5.2. TECHNOLOGY
4.5.3. INTEGRATION WITH EXISTING INFRASTRUCTURE
5. MARKET BY OFFERINGS
5.1. SOFTWARE
5.1.1. MARKET FORECAST FIGURE
5.1.2. SEGMENT ANALYSIS
5.2. SERVICES
5.2.1. MARKET FORECAST FIGURE
5.2.2. SEGMENT ANALYSIS
5.3. HARDWARE
5.3.1. MARKET FORECAST FIGURE
5.3.2. SEGMENT ANALYSIS
6. MARKET BY TECHNOLOGY
6.1. NATURAL LANGUAGE PROCESSING
6.1.1. MARKET FORECAST FIGURE
6.1.2. SEGMENT ANALYSIS
6.2. QUERYING METHOD
6.2.1. MARKET FORECAST FIGURE
6.2.2. SEGMENT ANALYSIS
6.3. CONTEXT AWARE PROCESSING
6.3.1. MARKET FORECAST FIGURE
6.3.2. SEGMENT ANALYSIS
6.4. DEEP LEARNING
6.4.1. MARKET FORECAST FIGURE
6.4.2. SEGMENT ANALYSIS
7. MARKET BY END-USER
7.1. HEALTHCARE PROVIDERS
7.1.1. MARKET FORECAST FIGURE
7.1.2. SEGMENT ANALYSIS
7.2. PHARMACEUTICAL AND BIOTECHNOLOGY COMPANIES
7.2.1. MARKET FORECAST FIGURE
7.2.2. SEGMENT ANALYSIS
7.3. PAYERS
7.3.1. MARKET FORECAST FIGURE
7.3.2. SEGMENT ANALYSIS
7.4. ACOS AND MCOS
7.4.1. MARKET FORECAST FIGURE
7.4.2. SEGMENT ANALYSIS
7.5. PATIENTS
7.5.1. MARKET FORECAST FIGURE
7.5.2. SEGMENT ANALYSIS
8. MARKET BY APPLICATION
8.1. ROBOT-ASSISTED SURGERY
8.1.1. MARKET FORECAST FIGURE
8.1.2. SEGMENT ANALYSIS
8.2. VIRTUAL NURSING ASSISTANT
8.2.1. MARKET FORECAST FIGURE
8.2.2. SEGMENT ANALYSIS
8.3. ADMINISTRATIVE WORKFLOW ASSISTANCE
8.3.1. MARKET FORECAST FIGURE
8.3.2. SEGMENT ANALYSIS
8.4. FRAUD DETECTION
8.4.1. MARKET FORECAST FIGURE
8.4.2. SEGMENT ANALYSIS
8.5. DOSAGE ERROR REDUCTION
8.5.1. MARKET FORECAST FIGURE
8.5.2. SEGMENT ANALYSIS
8.6. CLINICAL TRIAL PARTICIPANT IDENTIFIER
8.6.1. MARKET FORECAST FIGURE
8.6.2. SEGMENT ANALYSIS
8.7. PRELIMINARY DIAGNOSIS
8.7.1. MARKET FORECAST FIGURE
8.7.2. SEGMENT ANALYSIS
8.8. OTHER APPLICATIONS
8.8.1. MARKET FORECAST FIGURE
8.8.2. SEGMENT ANALYSIS
9. COMPETITIVE LANDSCAPE
9.1. KEY STRATEGIC DEVELOPMENTS
9.1.1. MERGERS & ACQUISITIONS
9.1.2. PRODUCT LAUNCHES & DEVELOPMENTS
9.1.3. PARTNERSHIPS & AGREEMENTS
9.2. COMPANY PROFILES
9.2.1. GE HEALTHCARE
9.2.1.1. COMPANY OVERVIEW
9.2.1.2. PRODUCT LIST
9.2.1.3. STRENGTHS & CHALLENGES
9.2.2. GOOGLE
9.2.2.1. COMPANY OVERVIEW
9.2.2.2. PRODUCT LIST
9.2.2.3. STRENGTHS & CHALLENGES
9.2.3. IBM CORPORATION
9.2.3.1. COMPANY OVERVIEW
9.2.3.2. PRODUCT LIST
9.2.3.3. STRENGTHS & CHALLENGES
9.2.4. INTEL CORPORATION
9.2.4.1. COMPANY OVERVIEW
9.2.4.2. PRODUCT LIST
9.2.4.3. STRENGTHS & CHALLENGES
9.2.5. KONINKLIJKE PHILIPS NV
9.2.5.1. COMPANY OVERVIEW
9.2.5.2. PRODUCTS
9.2.5.3. STRENGTHS & CHALLENGES
9.2.6. MEDTRONIC PLC
9.2.6.1. COMPANY OVERVIEW
9.2.6.2. PRODUCT LIST
9.2.6.3. STRENGTHS & CHALLENGES
9.2.7. MICROSOFT CORPORATION
9.2.7.1. COMPANY OVERVIEW
9.2.7.2. PRODUCT LIST
9.2.7.3. STRENGTHS & CHALLENGES
9.2.8. NVIDIA CORPORATION
9.2.8.1. COMPANY OVERVIEW
9.2.8.2. PRODUCT LIST
9.2.8.3. STRENGTHS & CHALLENGES
9.2.9. STRYKER CORPORATION
9.2.9.1. COMPANY OVERVIEW
9.2.9.2. PRODUCT LIST
9.2.9.3. STRENGTHS & CHALLENGES
9.2.10. SIEMENS HEALTHINEERS
9.2.10.1. COMPANY OVERVIEW
9.2.10.2. PRODUCT LIST
9.2.10.3. STRENGTHS & CHALLENGES

　

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Summary

KEY FINDINGS
The Germany artificial intelligence (AI) in healthcare market is projected to grow at a CAGR of 36.00% during the forecast period 2024-2032. Several factors escalate the market growth, such as large volumes of healthcare data supporting the adoption of AI, a growing number of AI and machine learning start-ups, the emergence of personalized medicine in tests for clinical decision-making, and AI creating a real-time monitoring system.
MARKET INSIGHTS
The Germany artificial intelligence (AI) in healthcare market is witnessing a remarkable emphasis on the development of precision medicine and personalized drugs. This strategic focus reflects the market’s commitment to optimizing medical treatments to individual characteristics, customizing patient outcomes, and minimizing adverse effects. Utilizing AI algorithms, precision medicine engages in the evaluation of extensive datasets that include genetic information, patient histories, and clinical data. This comprehensive methodology empowers healthcare professionals to formulate precise interventions, providing a therapeutic approach that is both more individualized and efficacious.
Moreover, the increasing utilization of AI in genetics is restructuring the field of healthcare in Germany. AI technologies are essential in genetic research, aiding in the identification of potential genetic markers associated with various diseases. The integration of AI in genetics expedites the examination of complex genomic data and enhances the accuracy of disease risk predictions. As a result, healthcare practitioners can leverage this advanced technology to make more informed decisions regarding disease prevention, early diagnosis, and personalized treatment plans.
Additionally, AI is transforming healthcare by creating a real-time monitoring system. This fundamental change allows for continuous and instantaneous tracking of patient health metrics, enabling proactive intervention and personalized care. Real-time monitoring systems powered by AI can analyze and interpret data from various sources, including wearable devices and electronic health records. This capability facilitates early detection of health anomalies, timely intervention, and the optimization of treatment regimens. The integration of AI into real-time monitoring systems marks a progressive step towards more efficient, patient-centric healthcare in Germany.
COMPETITIVE INSIGHTS
Some of the major companies in the Germany artificial intelligence (AI) in healthcare market include GE HealthCare, Intel Corporation, Google, IBM Corporation, etc.
Our report offerings include:
• Explore key findings of the overall market
• Strategic breakdown of market dynamics (Drivers, Restraints, Opportunities, Challenges)
• Market forecasts for a minimum of 9 years, along with 3 years of historical data for all segments, sub-segments, and regions
• Market Segmentation caters to a thorough assessment of key segments with their market estimations
• Geographical Analysis: Assessments of the mentioned regions and country-level segments with their market share
• Key analytics: Porter’s Five Forces Analysis, Vendor Landscape, Opportunity Matrix, Key Buying Criteria, etc.
• The competitive landscape is the theoretical explanation of the key companies based on factors, market share, etc.
• Company profiling: A detailed company overview, product/services offered, SCOT analysis, and recent strategic developments



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Table of Contents

TABLE OF CONTENTS
1. RESEARCH SCOPE & METHODOLOGY
1.1. STUDY OBJECTIVES
1.2. METHODOLOGY
1.3. ASSUMPTIONS & LIMITATIONS
2. EXECUTIVE SUMMARY
2.1. MARKET SIZE & ESTIMATES
2.2. COUNTRY SNAPSHOT
2.3. COUNTRY ANALYSIS
2.4. SCOPE OF STUDY
2.5. CRISIS SCENARIO ANALYSIS
2.6. MAJOR MARKET FINDINGS
2.6.1. SOFTWARE OFFERINGS ARE LEADING THE ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE MARKET, DRIVING INNOVATION AND EFFICIENCY
2.6.2. NATURAL LANGUAGE PROCESSING DOMINATING ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE TECHNOLOGY
2.6.3. HEALTHCARE PROVIDERS ARE THE MAJOR USERS OF ARTIFICIAL INTELLIGENCE (AI) IN HEALTHCARE
2.6.4. DOSAGE ERROR REDUCTION IS THE FASTEST-GROWING APPLICATION
3. MARKET DYNAMICS
3.1. KEY DRIVERS
3.1.1. LARGE VOLUMES OF HEALTHCARE DATA SUPPORTING THE ADOPTION OF AI
3.1.2. GROWING NUMBER OF AI AND MACHINE LEARNING START-UPS
3.1.3. EMERGENCE OF PERSONALIZED MEDICINE IN TESTS FOR CLINICAL DECISION-MAKING
3.1.4. AI CREATING A REAL-TIME MONITORING SYSTEM
3.2. KEY RESTRAINTS
3.2.1. SLOW ADOPTION OF AI-BASED TECHNOLOGIES
3.2.2. CHALLENGES IN MAINTAINING DATA SECURITY
3.2.3. COST CONSTRAINTS AND LOW RETURN ON INVESTMENT (ROI)
4. KEY ANALYTICS
4.1. KEY MARKET TRENDS
4.1.1. WIDENING APPLICATIONS OF AI IN THE HEALTHCARE INDUSTRY
4.1.2. INCREASING DEMAND FOR AI IN DRUG DISCOVERY
4.1.3. HIGH EMPHASIS ON THE DEVELOPMENT OF PRECISION MEDICINE AND PERSONALIZED DRUGS
4.1.4. INCREASING USE OF AI IN GENETICS
4.1.5. AI CREATING A REAL-TIME MONITORING SYSTEM
4.2. PESTLE ANALYSIS
4.2.1. POLITICAL
4.2.2. ECONOMICAL
4.2.3. SOCIAL
4.2.4. TECHNOLOGICAL
4.2.5. LEGAL
4.2.6. ENVIRONMENTAL
4.3. PORTER’S FIVE FORCES ANALYSIS
4.3.1. BUYERS POWER
4.3.2. SUPPLIERS POWER
4.3.3. SUBSTITUTION
4.3.4. NEW ENTRANTS
4.3.5. INDUSTRY RIVALRY
4.4. VALUE CHAIN ANALYSIS
4.4.1. DATA WAREHOUSE
4.4.2. ARTIFICIAL INTELLIGENCE (AI) ANALYSIS
4.4.3. SOFTWARE DEVELOPMENT
4.5. KEY BUYING CRITERIA
4.5.1. APPLICATION
4.5.2. TECHNOLOGY
4.5.3. INTEGRATION WITH EXISTING INFRASTRUCTURE
5. MARKET BY OFFERINGS
5.1. SOFTWARE
5.1.1. MARKET FORECAST FIGURE
5.1.2. SEGMENT ANALYSIS
5.2. SERVICES
5.2.1. MARKET FORECAST FIGURE
5.2.2. SEGMENT ANALYSIS
5.3. HARDWARE
5.3.1. MARKET FORECAST FIGURE
5.3.2. SEGMENT ANALYSIS
6. MARKET BY TECHNOLOGY
6.1. NATURAL LANGUAGE PROCESSING
6.1.1. MARKET FORECAST FIGURE
6.1.2. SEGMENT ANALYSIS
6.2. QUERYING METHOD
6.2.1. MARKET FORECAST FIGURE
6.2.2. SEGMENT ANALYSIS
6.3. CONTEXT AWARE PROCESSING
6.3.1. MARKET FORECAST FIGURE
6.3.2. SEGMENT ANALYSIS
6.4. DEEP LEARNING
6.4.1. MARKET FORECAST FIGURE
6.4.2. SEGMENT ANALYSIS
7. MARKET BY END-USER
7.1. HEALTHCARE PROVIDERS
7.1.1. MARKET FORECAST FIGURE
7.1.2. SEGMENT ANALYSIS
7.2. PHARMACEUTICAL AND BIOTECHNOLOGY COMPANIES
7.2.1. MARKET FORECAST FIGURE
7.2.2. SEGMENT ANALYSIS
7.3. PAYERS
7.3.1. MARKET FORECAST FIGURE
7.3.2. SEGMENT ANALYSIS
7.4. ACOS AND MCOS
7.4.1. MARKET FORECAST FIGURE
7.4.2. SEGMENT ANALYSIS
7.5. PATIENTS
7.5.1. MARKET FORECAST FIGURE
7.5.2. SEGMENT ANALYSIS
8. MARKET BY APPLICATION
8.1. ROBOT-ASSISTED SURGERY
8.1.1. MARKET FORECAST FIGURE
8.1.2. SEGMENT ANALYSIS
8.2. VIRTUAL NURSING ASSISTANT
8.2.1. MARKET FORECAST FIGURE
8.2.2. SEGMENT ANALYSIS
8.3. ADMINISTRATIVE WORKFLOW ASSISTANCE
8.3.1. MARKET FORECAST FIGURE
8.3.2. SEGMENT ANALYSIS
8.4. FRAUD DETECTION
8.4.1. MARKET FORECAST FIGURE
8.4.2. SEGMENT ANALYSIS
8.5. DOSAGE ERROR REDUCTION
8.5.1. MARKET FORECAST FIGURE
8.5.2. SEGMENT ANALYSIS
8.6. CLINICAL TRIAL PARTICIPANT IDENTIFIER
8.6.1. MARKET FORECAST FIGURE
8.6.2. SEGMENT ANALYSIS
8.7. PRELIMINARY DIAGNOSIS
8.7.1. MARKET FORECAST FIGURE
8.7.2. SEGMENT ANALYSIS
8.8. OTHER APPLICATIONS
8.8.1. MARKET FORECAST FIGURE
8.8.2. SEGMENT ANALYSIS
9. COMPETITIVE LANDSCAPE
9.1. KEY STRATEGIC DEVELOPMENTS
9.1.1. MERGERS & ACQUISITIONS
9.1.2. PRODUCT LAUNCHES & DEVELOPMENTS
9.1.3. PARTNERSHIPS & AGREEMENTS
9.2. COMPANY PROFILES
9.2.1. GE HEALTHCARE
9.2.1.1. COMPANY OVERVIEW
9.2.1.2. PRODUCT LIST
9.2.1.3. STRENGTHS & CHALLENGES
9.2.2. GOOGLE
9.2.2.1. COMPANY OVERVIEW
9.2.2.2. PRODUCT LIST
9.2.2.3. STRENGTHS & CHALLENGES
9.2.3. IBM CORPORATION
9.2.3.1. COMPANY OVERVIEW
9.2.3.2. PRODUCT LIST
9.2.3.3. STRENGTHS & CHALLENGES
9.2.4. INTEL CORPORATION
9.2.4.1. COMPANY OVERVIEW
9.2.4.2. PRODUCT LIST
9.2.4.3. STRENGTHS & CHALLENGES
9.2.5. KONINKLIJKE PHILIPS NV
9.2.5.1. COMPANY OVERVIEW
9.2.5.2. PRODUCTS
9.2.5.3. STRENGTHS & CHALLENGES
9.2.6. MEDTRONIC PLC
9.2.6.1. COMPANY OVERVIEW
9.2.6.2. PRODUCT LIST
9.2.6.3. STRENGTHS & CHALLENGES
9.2.7. MICROSOFT CORPORATION
9.2.7.1. COMPANY OVERVIEW
9.2.7.2. PRODUCT LIST
9.2.7.3. STRENGTHS & CHALLENGES
9.2.8. NVIDIA CORPORATION
9.2.8.1. COMPANY OVERVIEW
9.2.8.2. PRODUCT LIST
9.2.8.3. STRENGTHS & CHALLENGES
9.2.9. STRYKER CORPORATION
9.2.9.1. COMPANY OVERVIEW
9.2.9.2. PRODUCT LIST
9.2.9.3. STRENGTHS & CHALLENGES
9.2.10. SIEMENS HEALTHINEERS
9.2.10.1. COMPANY OVERVIEW
9.2.10.2. PRODUCT LIST
9.2.10.3. STRENGTHS & CHALLENGES

　

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