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 医療診断における人工知能(AI)は、医療データの解析や患者の診断支援に利用される技術であり、医療の質を向上させる重要な役割を果たしています。AIは、大量のデータを迅速かつ正確に処理する能力を持っており、これにより専門的な知識を必要とする診断作業を支援することが可能です。 AIの種類には主に機械学習、深層学習、自然言語処理が含まれます。機械学習は、データからパターンを学習し、その情報を使って新しいデータを予測する技術です。これにより、患者の病歴や検査結果に基づいて疾患のリスクを評価することができます。深層学習は機械学習の一種で、大規模なデータセットを使用してより複雑なパターンを捉えることができるため、画像診断や音声認識に特に有効です。また自然言語処理は、医療文書や患者のメモから有用な情報を抽出し、診断支援を行うために用いられます。 AIの用途は多岐にわたります。代表的なものには画像診断、患者モニタリング、症状の自動解析、予測診断などがあります。画像診断では、AIを活用してX線、CTスキャン、MRIなどの医用画像を解析し、がんや他の疾患を検出することができます。これにより、医師が見逃しがちな微細な病変を捉えることができ、早期発見につながります。 患者モニタリングでは、AI技術を通じて患者のリアルタイムデータを解析し、異常が警告されることがあります。これにより、患者の状態を常に把握し、必要な介入を迅速に行うことが可能となります。症状の自動解析では、AIを用いて患者が訴える症状を整理し、考えられる疾患のリストを作成します。これにより医師の診断プロセスを支援し、より迅速かつ正確な診断につながります。 AIは予測診断においても効果を発揮します。ビッグデータを用いて疾患のリスクを評価し、予防策を提案することで、特定の病気に罹患する可能性を低くすることが目的です。 関連技術としては、ビッグデータ解析、クラウドコンピューティング、ロボティクスなどが挙げられます。ビッグデータ解析は、大量の医療情報を効率的に処理し、そこから価値あるインサイトを導出する手法です。これにより、過去の疾患パターンや治療効果を分析し、より適切な診療を行うための基盤を提供します。クラウドコンピューティングは、データの安全な保存とアクセスを可能にし、AIが必要とするデータを容易に活用できる環境を整えます。ロボティクスもAIの応用範囲に含まれ、手術支援ロボットやリハビリ支援ロボットが実際の医療現場で使用されています。 医療診断のAIは、正確性と効率性を向上させる一方で、倫理的な問題やプライバシーの懸念も伴います。医療データの管理と個人情報保護は非常に重要であり、AIが適切に運用されるためには、医療従事者や開発者が責任を持って取り組む必要があります。 今後もAI技術は進化し続け、医療診断の現場での利用が拡大することが予想されます。これにより、より多くの患者が恩恵を受けることができ、医療の質の向上が期待されます。AIは医療の未来において欠かせない要素となることでしょう。 |

