Chapter 1. Research Scope
1.1. Research Objectives
1.2. Market Definition
1.3. Analysis Period
1.4. Market Size Breakdown by Segments
1.4.1. Market size breakdown, by component
1.4.2. Market size breakdown, by application
1.4.3. Market size breakdown, by end user
1.4.4. Market size breakdown, by region
1.4.5. Market size breakdown, by country
1.5. Market Data Reporting Unit
1.5.1. Value
1.6. Key Stakeholders
Chapter 2. Research Methodology
2.1. Secondary Research
2.1.1. Paid
2.1.2. Unpaid
2.1.3. P&S Intelligence database
2.2. Primary Research
2.3. Market Size Estimation
2.4. Data Triangulation
2.5. Currency Conversion Rates
2.6. Assumptions for the Study
2.7. Notes and Caveats
Chapter 3. Executive Summary
Chapter 4. Market Indicators
Chapter 5. Industry Outlook
5.1. Market Dynamics
5.1.1. Trends
5.1.2. Drivers
5.1.3. Restraints/challenges
5.1.4. Impact analysis of drivers/restraints
5.2. Impact of COVID-19
5.3. Porter’s Five Forces Analysis
5.3.1. Bargaining power of buyers
5.3.2. Bargaining power of suppliers
5.3.3. Threat of new entrants
5.3.4. Intensity of rivalry
5.3.5. Threat of substitutes
Chapter 6. Global Market
6.1. Overview
6.2. Market Revenue, by Component (2017–2030)
6.3. Market Revenue, by Application (2017–2030)
6.4. Market Revenue, by End User (2017–2030)
6.5. Market Revenue, by Region (2017–2030)
Chapter 7. North America Market
7.1. Overview
7.2. Market Revenue, by Component (2017–2030)
7.3. Market Revenue, by Application (2017–2030)
7.4. Market Revenue, by End User (2017–2030)
7.5. Market Revenue, by Country (2017–2030)
Chapter 8. Europe Market
8.1. Overview
8.2. Market Revenue, by Component (2017–2030)
8.3. Market Revenue, by Application (2017–2030)
8.4. Market Revenue, by End User (2017–2030)
8.5. Market Revenue, by Country (2017–2030)
Chapter 9. APAC Market
9.1. Overview
9.2. Market Revenue, by Component (2017–2030)
9.3. Market Revenue, by Application (2017–2030)
9.4. Market Revenue, by End User (2017–2030)
9.5. Market Revenue, by Country (2017–2030)
Chapter 10. LATAM Market
10.1. Overview
10.2. Market Revenue, by Component (2017–2030)
10.3. Market Revenue, by Application (2017–2030)
10.4. Market Revenue, by End User (2017–2030)
10.5. Market Revenue, by Country (2017–2030)
Chapter 11. MEA Market
11.1. Overview
11.2. Market Revenue, by Component (2017–2030)
11.3. Market Revenue, by Application (2017–2030)
11.4. Market Revenue, by End User (2017–2030)
11.5. Market Revenue, by Country (2017–2030)
Chapter 12. U.S. Market
12.1. Overview
12.2. Market Revenue, by Component (2017–2030)
12.3. Market Revenue, by Application (2017–2030)
12.4. Market Revenue, by End User (2017–2030)
Chapter 13. Canada Market
13.1. Overview
13.2. Market Revenue, by Component (2017–2030)
13.3. Market Revenue, by Application (2017–2030)
13.4. Market Revenue, by End User (2017–2030)
Chapter 14. Germany Market
14.1. Overview
14.2. Market Revenue, by Component (2017–2030)
14.3. Market Revenue, by Application (2017–2030)
14.4. Market Revenue, by End User (2017–2030)
Chapter 15. France Market
15.1. Overview
15.2. Market Revenue, by Component (2017–2030)
15.3. Market Revenue, by Application (2017–2030)
15.4. Market Revenue, by End User (2017–2030)
Chapter 16. U.K. Market
16.1. Overview
16.2. Market Revenue, by Component (2017–2030)
16.3. Market Revenue, by Application (2017–2030)
16.4. Market Revenue, by End User (2017–2030)
Chapter 17. Italy Market
17.1. Overview
17.2. Market Revenue, by Component (2017–2030)
17.3. Market Revenue, by Application (2017–2030)
17.4. Market Revenue, by End User (2017–2030)
Chapter 18. Spain Market
18.1. Overview
18.2. Market Revenue, by Component (2017–2030)
18.3. Market Revenue, by Application (2017–2030)
18.4. Market Revenue, by End User (2017–2030)
Chapter 19. Japan Market
19.1. Overview
19.2. Market Revenue, by Component (2017–2030)
19.3. Market Revenue, by Application (2017–2030)
19.4. Market Revenue, by End User (2017–2030)
Chapter 20. China Market
20.1. Overview
20.2. Market Revenue, by Component (2017–2030)
20.3. Market Revenue, by Application (2017–2030)
20.4. Market Revenue, by End User (2017–2030)
Chapter 21. India Market
21.1. Overview
21.2. Market Revenue, by Component (2017–2030)
21.3. Market Revenue, by Application (2017–2030)
21.4. Market Revenue, by End User (2017–2030)
Chapter 22. Australia Market
22.1. Overview
22.2. Market Revenue, by Component (2017–2030)
22.3. Market Revenue, by Application (2017–2030)
22.4. Market Revenue, by End User (2017–2030)
Chapter 23. South Korea Market
23.1. Overview
23.2. Market Revenue, by Component (2017–2030)
23.3. Market Revenue, by Application (2017–2030)
23.4. Market Revenue, by End User (2017–2030)
Chapter 24. Brazil Market
24.1. Overview
24.2. Market Revenue, by Component (2017–2030)
24.3. Market Revenue, by Application (2017–2030)
24.4. Market Revenue, by End User (2017–2030)
Chapter 25. Mexico Market
25.1. Overview
25.2. Market Revenue, by Component (2017–2030)
25.3. Market Revenue, by Application (2017–2030)
25.4. Market Revenue, by End User (2017–2030)
Chapter 26. Saudi Arabia Market
26.1. Overview
26.2. Market Revenue, by Component (2017–2030)
26.3. Market Revenue, by Application (2017–2030)
26.4. Market Revenue, by End User (2017–2030)
Chapter 27. South Africa Market
27.1. Overview
27.2. Market Revenue, by Component (2017–2030)
27.3. Market Revenue, by Application (2017–2030)
27.4. Market Revenue, by End User (2017–2030)
Chapter 28. U.A.E. Market
28.1. Overview
28.2. Market Revenue, by Component (2017–2030)
28.3. Market Revenue, by Application (2017–2030)
28.4. Market Revenue, by End User (2017–2030)
Chapter 29. Competitive Landscape
29.1. List of Market Players and their Offerings
29.2. Competitive Benchmarking of Key Players
29.3. Product Benchmarking of Key Players
29.4. Recent Strategic Developments
Chapter 30. Company Profiles
30.1. Microsoft Corporation
30.1.1. Business overview
30.1.2. Product and service offerings
30.1.3. Key financial summary
30.2. NVIDIA Corporation
30.2.1. Business overview
30.2.2. Product and service offerings
30.2.3. Key financial summary
30.3. Siemens Healthineers AG
30.3.1. Business overview
30.3.2. Product and service offerings
30.3.3. Key financial summary
30.4. International Business Machines Corporation
30.4.1. Business overview
30.4.2. Product and service offerings
30.4.3. Key financial summary
30.5. Merative L.P.
30.5.1. Business overview
30.5.2. Product and service offerings
30.6. GE HealthCare Technologies Inc.
30.6.1. Business overview
30.6.2. Product and service offerings
30.6.3. Key financial summary
30.7. Intel Corporation
30.7.1. Business overview
30.7.2. Product and service offerings
30.7.3. Key financial summary
30.8. Aidoc Medical Ltd.
30.8.1. Business overview
30.8.2. Product and service offerings
30.9. Alphabet Inc.
30.9.1. Business overview
30.9.2. Product and service offerings
30.9.3. Key financial summary
30.10. Digital Diagnostics Inc.
30.10.1. Business overview
30.10.2. Product and service offerings
30.11. Prognos Health Inc.
30.11.1. Business overview
30.11.2. Product and service offerings
30.12. Butterfly Network Inc.
30.12.1. Business overview
30.12.2. Product and service offerings
30.12.3. Key financial summary
30.13. EchoNous Inc.
30.13.1. Business overview
30.13.2. Product and service offerings
30.14. NeuraSignal Inc.
30.14.1. Business overview
30.14.2. Product and service offerings
30.15. Riverain Technologies
30.15.1. Business overview
30.15.2. Product and service offerings
Chapter 31. Appendix
31.1. Abbreviations
31.2. Sources and References
31.3. Related Reports
| ※参考情報 医療診断におけるAI(人工知能)は、医療の現場で使用される技術として、患者の病状を診断し、治療方針を決定する過程をサポートするために活用されています。この技術は、データ解析能力が高く、大量の情報を迅速に処理できるため、医療従事者の負担を軽減し、診断精度を高めることが期待されています。 AIによる医療診断の種類には、主に画像診断、病歴の解析、予測モデル、ナチュラルランゲージプロセッシング(NLP)を用いた診断支援などがあります。 まず、画像診断では、AIはCTやMRI、X線などの医療画像を解析することで、異常を検出することができます。機械学習アルゴリズムを使用して、過去の診断情報や医療画像データを学習することにより、新たな画像に対しても高い精度で疾患の有無を判定することが可能になります。 次に、病歴の解析では、患者の電子カルテや過去の診療データを利用して、病歴や症状に基づくリスク評価を行います。AIは、多くの患者データから共通のパターンを学習し、特定の病気に対する感受性を予測することができます。これにより、早期発見や予防的治療が促進されます。 予測モデルに関しては、AIを用いて疾患の進行や再発リスクを予測することができます。これにより、医療従事者は患者の治療計画をより効果的に立てることができるようになります。たとえば、患者の生活習慣や遺伝情報を基に、病気の予測を行うことが可能です。 ナチュラルランゲージプロセッシング(NLP)は、医師が行う診断や病歴の文書化においても重要です。AIは、自然言語を理解し、医師が記載した診断内容や患者の訴えを分析することができます。これにより、重要な情報を抽出し、診断をサポートすることが可能となります。 AI技術の関連技術としては、機械学習、深層学習、ビッグデータ解析、ロボティクスなどが挙げられます。機械学習と深層学習は、AIの基本となる技術であり、特に画像診断においては、神経ネットワークを使用して画像の特徴を自動で学習し、高精度な診断を行うことに寄与しています。ビッグデータ解析は、膨大な医療情報を処理し、そこから価値あるインサイトを得るための手法です。ロボティクスは、手術などの実践的な医療行為にAIを応用する際に使用され、精密かつ効率的な医療を提供するために役立っています。 医療分野でのAI活用は日々進化しており、現在も新しい技術や研究が展開されています。その成果として、AIは診断の精度向上だけでなく、患者の治療効果の向上、医療コストの削減にも貢献することが期待されています。 一方で、AIの導入には倫理的問題やプライバシーの保護、医師の仕事への影響など、さまざまな課題も存在します。このため、技術の導入に際しては適切な規制やガイドラインが必要とされます。今後の医療診断におけるAIの役割とその発展は、医療の質を向上させる大きな可能性を秘めています。これらの技術が進化し続ける中で、患者にとってより良い医療サービスを提供できる未来が期待されます。 |
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