医療における生成AIの世界市場（2024年～2031年）

世界の医療における生成AIの市場規模は、2023年に17億5,000万米ドルに達し、2031年には200億1,000万米ドルに達すると予測され、予測期間2024年～2031年のCAGRは35.8%で成長する見込みです。

医療における生成AIは、既存の医療情報に基づいて新しいデータ、洞察、コンテンツを作成できる高度な人工知能技術の活用を指します。この革新的なアプローチは、機械学習やディープラーニング技術を含む洗練されたアルゴリズムを採用し、カルテ、画像データ、臨床記録などの膨大な量の非構造化データを分析します。主な目的は、診断、治療計画、患者エンゲージメント、業務効率など、医療提供のさまざまな側面を強化することです。

医療における生成AIは、現実の医療データを忠実に模倣した合成データを作成することができます。この機能は、患者のプライバシーを損なうことなく機械学習モデルをトレーニングするために特に有用であり、研究開発目的には非常に貴重です。複雑な医療画像（MRIやCTスキャンなど）を分析することで、生成AIは、人間の専門家では検出が困難なパターンを特定することができます。この強化により、診断精度が向上し、病気の早期発見をサポートします。

AIを搭載したバーチャルアシスタントは、健康に関する問い合わせに答えたり、服薬リマインダーを送信したり、パーソナライズされた健康アドバイスを提供したりすることで、患者を双方向的にサポートします。この機能により、患者のエンゲージメントが向上し、より患者中心の医療体験が促進されます。このような要因が、世界の医療における生成AI市場拡大の原動力となっています。

市場力学：

促進要因と抑制要因

個別化医療ソリューションに対する需要の増加

個別化された医療ソリューションへの需要の高まりは、世界の医療における生成AI市場の成長を大きく後押ししており、市場予測期間中もその傾向が続くと予測されています。

医療業界では、遺伝子プロファイル、病歴、ライフスタイル要因に基づいて、患者の特定のニーズに合わせて治療計画を調整する個別化医療の導入が進んでいます。医療における生成AIは、大規模なデータセットを分析し、個別化治療戦略に役立つパターンや相関関係を特定することで、この移行において重要な役割を果たしています。例えば、AIアルゴリズムは、異なる患者が特定の治療にどのように反応するかを予測し、医療提供者が治療アプローチを最適化することで、治療結果の改善を可能にします。

医療における生成AIは、電子カルテ（EHR）、ゲノムデータ、臨床ノートなど、膨大な量の非構造化データの処理に優れています。この機能により、医療提供者は患者の包括的な健康状態プロファイルを作成し、より効果的な介入を行うことができます。多様なデータを合成することで、生成AIは個々の患者に特有の危険因子や健康動向を特定し、プロアクティブケアや早期介入を促進します。

さらに、業界の主要企業は、この世界の医療における生成AI市場の成長を促進する主要な取り組みや製品の発売を行っています。例えば、2023年6月のMicrosoft Azureのニュースによると、生成AIは、医療提供者が効率を高め、ケアをパーソナライズし、意思決定プロセスを強化できるようにすることで、医療研究、診断、治療、患者ケアに革命を起こす可能性を秘めています。医療における生成AIは、研究者が膨大な量の医療データを迅速かつ効率的に分析できるようにします。医療における生成AIは、データ抽出と文書レビューを自動化し、管理業務に費やす時間を大幅に削減します。

同様に、2024年4月、世界保健機関（WHO）は、Smart AI Resource Assistant for Healthの略称であるS.A.R.A.H.の立ち上げを発表しました。この革新的なデジタル・ヘルス・プロモーターのプロトタイプは、生成型人工知能（AI）を搭載し、「私の健康、私の権利」をテーマとする世界保健デーを前に、公衆衛生への取り組みを強化するよう設計されています。

また、2024年10月、Amazon One Medicalは、高度なAI技術を医療サービスに統合し、Amazon BedrockやAWS HealthScribeを含むAWSの生成AIサービスを活用して、医師が時間を節約し、患者ケアを強化できるよう支援します。これらすべての要因が、世界の医療における生成AI市場に需要をもたらしています。

さらに、遠隔医療との統合の成長に対する需要の高まりが、世界の医療における生成AI市場の拡大に寄与しています。

データセキュリティとプライバシーへの懸念

データセキュリティとプライバシーに関する懸念が、医療における生成AIの世界市場の成長を妨げます。医療における生成AIの統合は、患者ケアと業務効率を改善する大きな機会を提供します。しかし、特に患者情報の機密性が高いため、データのプライバシーやセキュリティに関する重大な懸念も生じています。

医療における生成AIシステムは、多くの場合、電子カルテ（EHR）、医療画像、個人健康情報（PHI）を含む大量のセンシティブな患者データへのアクセスを必要とします。これらのデータは機密性が高く、患者の信頼を維持し、法的基準を遵守するために保護されなければなりません。

米国では、HIPAAがPHIの取り扱いに関する厳格なガイドラインを定めています。医療組織は、利用するテクノロジーがこれらの規制に準拠していることを保証しなければなりません。これには、PHIの機密性、完全性、可用性を保護するセーフガードの導入が含まれます。例えば、医療環境で使用される生成AIツールは、徹底的なセキュリティレビューを受け、コンプライアンスを確保するために提供者とビジネス・アソシエート契約（BAA）に署名しなければなりません。

2024年3月の国立生物工学情報センター（NCBI）の調査発表によると、医療における生成AIの統合は変革の可能性をもたらしますが、広範なデータ要件と固有の不透明性により、重大なプライバシーとセキュリティのリスクももたらします。生成AIシステムは、電子カルテ（EHR）、医療画像、個人健康情報（PHI）など、膨大な量の機密性の高い患者データへのアクセスを必要とします。したがって、上記の要因は、世界の医療における生成AI市場の潜在的な成長を制限している可能性があります。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

• 影響要因
  • 促進要因
    • 個別化された医療ソリューションの需要増加
  • 抑制要因
    • データセキュリティとプライバシーに関する懸念
  • 機会
  • 影響分析

第5章 産業分析

• ポーターのファイブフォース分析
• サプライチェーン分析
• 価格分析
• 特許分析
• 規制分析
• SWOT分析
• アンメットニーズ

第6章 用途別

• 診断と医療画像
• 医薬品の創薬と開発
• 個別化治療
• 患者モニタリングと予測分析
• その他

第7章 エンドユーザー別

• 病院・クリニック
• 医療機関
• 診断センター
• その他

第8章 地域別

• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • スペイン
  • イタリア
  • その他欧州
• 南米
  • ブラジル
  • アルゼンチン
  • その他南米
• アジア太平洋
  • 中国
  • インド
  • 日本
  • 韓国
  • その他アジア太平洋
• 中東・アフリカ

第9章 競合情勢

• 競合シナリオ
• 市況・シェア分析
• M&A分析

第10章 企業プロファイル

• IBM
  • 会社概要
  • 製品ポートフォリオと概要
  • 財務概要
  • 主な発展
• Google LLC
• Microsoft
• OpenAI
• NVIDIA Corporation
• Oracle
• Johnson & Johnson Services, Inc.
• NioyaTech.
• Saxon.

第11章 付録

The global generative AI in healthcare market reached US$ 1.75 billion in 2023 and is expected to reach US$ 20.01 billion by 2031, growing at a CAGR of 35.8% during the forecast period 2024-2031.

Generative AI in healthcare refers to the utilization of advanced artificial intelligence technologies that can create new data, insights, and content based on existing healthcare information. This innovative approach employs sophisticated algorithms, including machine learning and deep learning techniques, to analyze extensive amounts of unstructured data, such as medical records, imaging data, and clinical notes. The primary objective is to enhance various facets of healthcare delivery, including diagnostics, treatment planning, patient engagement, and operational efficiency.

Generative AI in healthcare can produce synthetic data that closely mimics real-world healthcare data. This capability is particularly useful for training machine learning models without compromising patient privacy, making it invaluable for research and development purposes. By analyzing complex medical images (e.g., MRIs and CT scans), generative AI can identify patterns that may be difficult for human practitioners to detect. This enhancement improves diagnostic accuracy and supports early disease detection.

AI-powered virtual assistants provide interactive support to patients by answering health-related queries, sending medication reminders, and offering personalized health advice. This functionality enhances patient engagement and fosters a more patient-centric healthcare experience. These factors have driven the global generative AI in healthcare market expansion.

Market Dynamics: Drivers & Restraints

Increasing Demand for Personalized Healthcare Solutions

The increasing demand for personalized healthcare solutions is significantly driving the growth of the global generative AI in healthcare market and is expected to drive throughout the market forecast period.

The healthcare industry is increasingly embracing personalized medicine, which tailors treatment plans to the specific needs of patients based on their genetic profiles, medical histories, and lifestyle factors. Generative AI in healthcare plays a vital role in this transition by analyzing large datasets to identify patterns and correlations that inform personalized treatment strategies. For instance, AI algorithms can predict how different patients might respond to specific treatments, enabling healthcare providers to optimize therapeutic approaches for improved outcomes.

Generative AI in healthcare excels at processing vast amounts of unstructured data, including electronic health records (EHRs), genomic data, and clinical notes. This capability allows healthcare providers to create comprehensive health profiles for patients, which can be used to tailor interventions more effectively. By synthesizing diverse data types, generative AI helps identify risk factors and health trends specific to individual patients, facilitating proactive care and early intervention.

Furthermore, major players in the industry have key initiatives and product launches that would drive this global generative AI in healthcare market growth. For instance, as per Microsoft Azure news in June 2023, generative AI has the potential to revolutionize medical research, diagnosis, treatment, and patient care by enabling healthcare providers to increase efficiency, personalize care, and enhance decision-making processes. Generative AI in healthcare empowers researchers to analyze vast amounts of medical data rapidly and efficiently. It automates data extraction and document reviews, significantly reducing the time spent on administrative tasks.

Similarly, in April 2024, the World Health Organization (WHO) announced the launch of S.A.R.A.H., which stands for Smart AI Resource Assistant for Health. This innovative digital health promoter prototype is powered by generative artificial intelligence (AI) and is designed to enhance public health engagement ahead of World Health Day, which focuses on the theme "My Health, My Right.

Also, in October 2024, Amazon One Medical integrated advanced AI technology into its healthcare services, leveraging AWS generative AI services, including Amazon Bedrock and AWS HealthScribe, to help doctors save time and enhance patient care. All these factors demand global generative AI in healthcare market.

Moreover, the rising demand for the growth of integration with telemedicine contributes to the global generative AI in healthcare market expansion.

Data Security and Privacy Concerns

Data security and privacy concerns will hinder the growth of the global generative AI in healthcare market. The integration of generative AI in healthcare offers substantial opportunities for improving patient care and operational efficiency. However, it also raises critical concerns regarding data privacy and security, particularly because of the sensitive nature of patient information involved.

Generative AI in healthcare systems often requires access to large volumes of sensitive patient data, including electronic health records (EHRs), medical imaging, and personal health information (PHI). This data is highly confidential and must be protected to maintain patient trust and comply with legal standards.

In the U.S., HIPAA establishes strict guidelines for handling PHI. Healthcare organizations must ensure that any technology they utilize complies with these regulations. This includes implementing safeguards to protect the confidentiality, integrity, and availability of PHI. For instance, any generative AI tool used in a healthcare setting must undergo a thorough security review and have a signed Business Associate Agreement (BAA) with the provider to ensure compliance.

According to the National Center for Biotechnology Information (NCBI) research publication in March 2024, the integration of generative AI in healthcare offers transformative potential, but it also introduces significant privacy and security risks due to its extensive data requirements and inherent opacity. Generative AI systems necessitate access to vast amounts of sensitive patient data, including electronic health records (EHRs), medical imaging, and personal health information (PHI). Thus, the above factors could be limiting the global generative AI in healthcare market's potential growth.

Segment Analysis

The global generative AI in healthcare market is segmented based on application, end-user, and region.

Application:

The diagnostics & medical imaging segment is expected to dominate the global generative AI in healthcare market share

The diagnostics & medical imaging segment holds a major portion of the global generative AI in healthcare market share and is expected to continue to hold a significant portion of the global generative AI in healthcare market share during the forecast period.

The diagnostics & medical imaging segment is a crucial component of the generative AI in healthcare market, significantly enhancing healthcare professionals' capabilities to analyze and interpret medical images. The integration of generative AI in healthcare technologies has transformed traditional imaging practices, leading to improved diagnostic accuracy and operational efficiency.

Generative AI in healthcare technologies, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), equip healthcare providers with advanced tools for analyzing complex medical images, including MRIs, CT scans, and X-rays. These models enhance diagnostic accuracy by identifying subtle abnormalities that may be overlooked by human practitioners, thereby facilitating early disease detection.

In diagnostics, generative AI excels at analyzing complex medical images, such as MRIs and CT scans, with remarkable precision. Utilizing techniques like convolutional neural networks (CNNs), generative AI assists in detecting abnormalities that may be overlooked by human eyes. This enhanced diagnostic capability not only improves accuracy but also supports early disease detection, which is crucial for effective treatment outcomes.

Furthermore, major players in the industry product launches that would drive this global generative AI in healthcare market growth. For instance, in September 2024, Harrison.ai launched a radiology-specific vision language model named Harrison. rad.1, marking a significant advancement in healthcare artificial intelligence. This model is designed to address specific needs in the field of radiology, enhancing the capabilities of AI in medical imaging and diagnostics.

Also, in December 2023, Google launched MedLM, a suite of generative AI models specifically designed for the healthcare industry. This initiative is part of Google's ongoing efforts to leverage artificial intelligence to enhance healthcare delivery and improve patient outcomes. These factors have solidified the segment's position in the global generative AI in healthcare market.

Geographical Analysis

North America is expected to hold a significant position in the global generative AI in healthcare market share

North America holds a substantial position in the global generative AI in healthcare market and is expected to hold most of the market share.

Healthcare institutions across North America, including hospitals, clinics, and diagnostic centers, are increasingly recognizing the potential of generative AI. The integration of AI into clinical workflows is viewed as a means to enhance diagnostic accuracy, optimize treatment planning, and improve patient outcomes. This trend is bolstered by a growing body of evidence supporting the effectiveness of AI technologies in various clinical domains such as radiology, pathology, and cardiology.

Rapid advancements in generative AI technologies, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), enable more effective analysis of complex medical data. These technologies allow healthcare providers to generate synthetic data for training machine learning models, thereby improving diagnostic capabilities and facilitating personalized medicine.

Furthermore, in this region, a major number of key players' presence, well-advanced healthcare infrastructure, government initiatives & regulatory support, investments, and product launches would propel the global generative AI in healthcare market. For instance, in February 2024, in New Jersey, CitiusTech launched an industry-first solution for healthcare organizations to help address the reliability, quality, and trust requirements for generative AI in healthcare solutions. The CitiusTech Gen AI Quality & Trust solution will help organizations design, develop, integrate, and monitor quality and facilitate trust in Generative AI applications, providing the confidence needed to adopt and scale Gen AI applications enterprise-wide.

Also, in June 2024, in New Jersey, Cognizant launched its first set of healthcare large language model (LLM) solutions as part of an expanded generative AI partnership with Google Cloud. This initiative aims to harness the power of generative AI in healthcare to address various challenges in the healthcare sector, enhancing operational efficiency, improving patient care, and streamlining administrative processes. Thus, the above factors are consolidating the region's position as a dominant force in the global generative AI in healthcare market.

Asia Pacific is growing at the fastest pace in the global generative AI in healthcare market share

Asia Pacific holds the fastest pace in the global generative AI in healthcare market and is expected to hold most of the market share.

The Asia-Pacific region is undergoing significant digital transformation, with healthcare systems increasingly adopting advanced technologies. This shift facilitates the integration of generative AI solutions that enhance patient care, streamline processes, and improve operational efficiency.

Countries such as China, India, Japan, and Singapore have vast and diverse patient populations, providing a rich dataset for training generative AI in healthcare models. This diversity enables the development of robust and accurate algorithms that can address unique regional health challenges, improving diagnosis and treatment planning.

Governments across the Asia-Pacific region are actively promoting the adoption of AI technologies in healthcare. They provide funding, infrastructure support, and regulatory frameworks to encourage research and development in generative AI in healthcare industry. These initiatives foster collaborations between industry, academia, and healthcare institutions, accelerating the development and deployment of generative AI solutions.

Furthermore, key players in the industry's technological advancements help to drive the global generative AI in healthcare market growth. For instance, in November 2024, In Japan, healthcare innovators are developing AI-augmented systems to enhance the capabilities of radiologists and surgeons, providing them with "real-time superpowers" to improve patient care and operational efficiency. A notable instance of this advancement is Fujifilm's collaboration with NVIDIA, which has resulted in the creation of an AI application designed to assist surgeons during procedures.

Also, in October 2024, China made a significant leap in healthcare innovation by announcing the establishment of the world's first AI hospital, known as the Agent Hospital. This pioneering facility, developed by researchers from Tsinghua University, represents an innovative approach to integrating artificial intelligence into medical practice, marking Asia's leadership in healthcare technology.

Thus, the above factors are consolidating the region's position as the fastest-growing force in the global generative AI in healthcare market.

Competitive Landscape

The major global players in the generative AI in healthcare market include IBM, Google LLC, Microsoft, OpenAI, NVIDIA Corporation, Oracle, Johnson & Johnson Services, Inc., NioyaTech., and Saxon. Among others.

Key Developments

• In October 2024, Microsoft announced significant advancements in its Cloud for Healthcare offerings, unveiling several artificial intelligence enhancements aimed at improving healthcare delivery. These enhancements include new healthcare AI models in Azure AI Studio, enhanced data capabilities in Microsoft Fabric, and developer tools within Copilot Studio. Many of these innovations are currently available in preview mode, allowing early adopters to explore their functionalities.
• In March 2024, NVIDIA Healthcare launched a suite of generative AI microservices aimed at advancing drug discovery, medical technology (MedTech), and digital health. This initiative includes a catalog of 25 new cloud-agnostic microservices that enable healthcare developers to leverage the latest advancements in generative AI across various applications, including biology, chemistry, imaging, and healthcare data management

Why Purchase the Report?

• Pipeline & Innovations: Reviews ongoing clinical trials, and product pipelines, and forecasts upcoming advancements in medical devices and pharmaceuticals.
• Product Performance & Market Positioning: Analyzes product performance, market positioning, and growth potential to optimize strategies.
• Real-world Evidence: Integrates patient feedback and data into product development for improved outcomes.
• Physician Preferences & Health System Impact: Examines healthcare provider behaviors and the impact of health system mergers on adoption strategies.
• Market Updates & Industry Changes: Covers recent regulatory changes, new policies, and emerging technologies.
• Competitive Strategies: Analyzes competitor strategies, market share, and emerging players.
• Pricing & Market Access: Reviews pricing models, reimbursement trends, and market access strategies.
• Market Entry & Expansion: Identifies optimal strategies for entering new markets and partnerships.
• Regional Growth & Investment: Highlights high-growth regions and investment opportunities.
• Supply Chain Optimization: Assesses supply chain risks and distribution strategies for efficient product delivery.
• Sustainability & Regulatory Impact: Focuses on eco-friendly practices and evolving regulations in healthcare.
• Post-market Surveillance: Uses post-market data to enhance product safety and access.
• Pharmacoeconomics & Value-Based Pricing: Analyzes the shift to value-based pricing and data-driven decision-making in R&D.

The global generative AI in healthcare market report delivers a detailed analysis with 60+ key tables, more than 50 visually impactful figures, and 176 pages of expert insights, providing a complete view of the market landscape.

Target Audience 2023

• Manufacturers: Pharmaceutical, Medical Device, Biotech Companies, Contract Manufacturers, Distributors, Hospitals.
• Regulatory & Policy: Compliance Officers, Government, Health Economists, Market Access Specialists.
• Technology & Innovation: AI/Robotics Providers, R&D Professionals, Clinical Trial Managers, Pharmacovigilance Experts.
• Investors: Healthcare Investors, Venture Fund Investors, Pharma Marketing & Sales.
• Consulting & Advisory: Healthcare Consultants, Industry Associations, Analysts.
• Supply Chain: Distribution and Supply Chain Managers.
• Consumers & Advocacy: Patients, Advocacy Groups, Insurance Companies.
• Academic & Research: Academic Institutions.

Table of Contents

1. Methodology and Scope

• 1.1. Research Methodology
• 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

• 3.1. Snippet by Application
• 3.2. Snippet by End-User
• 3.3. Snippet by Region

4. Dynamics

• 4.1. Impacting Factors
  • 4.1.1. Drivers
    • 4.1.1.1. Increasing Demand for Personalized Healthcare Solutions
  • 4.1.2. Restraints
    • 4.1.2.1. Data Security and Privacy Concerns
  • 4.1.3. Opportunity
  • 4.1.4. Impact Analysis

5. Industry Analysis

• 5.1. Porter's Five Force Analysis
• 5.2. Supply Chain Analysis
• 5.3. Pricing Analysis
• 5.4. Patent Analysis
• 5.5. Regulatory Analysis
• 5.6. SWOT Analysis
• 5.7. Unmet Needs

6. By Application

• 6.1. Introduction
  • 6.1.1. Analysis and Y-o-Y Growth Analysis (%), By Application
  • 6.1.2. Market Attractiveness Index, By Application
• 6.2. Diagnostics & Medical Imaging *
  • 6.2.1. Introduction
  • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 6.3. Drug Discovery & Development
• 6.4. Personalized Treatment
• 6.5. Patient Monitoring & Predictive Analytics
• 6.6. Others

7. By End-User

• 7.1. Introduction
  • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 7.1.2. Market Attractiveness Index, By End-User
• 7.2. Hospitals & Clinics*
  • 7.2.1. Introduction
  • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 7.3. Healthcare Organizations
• 7.4. Diagnostic Centers
• 7.5. Others

8. By Region

• 8.1. Introduction
  • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
  • 8.1.2. Market Attractiveness Index, By Region
• 8.2. North America
  • 8.2.1. Introduction
  • 8.2.2. Key Region-Specific Dynamics
  • 8.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 8.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 8.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 8.2.5.1. U.S.
    • 8.2.5.2. Canada
    • 8.2.5.3. Mexico
• 8.3. Europe
  • 8.3.1. Introduction
  • 8.3.2. Key Region-Specific Dynamics
  • 8.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 8.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 8.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 8.3.5.1. Germany
    • 8.3.5.2. U.K.
    • 8.3.5.3. France
    • 8.3.5.4. Spain
    • 8.3.5.5. Italy
    • 8.3.5.6. Rest of Europe
• 8.4. South America
  • 8.4.1. Introduction
  • 8.4.2. Key Region-Specific Dynamics
  • 8.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 8.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 8.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 8.4.5.1. Brazil
    • 8.4.5.2. Argentina
    • 8.4.5.3. Rest of South America
• 8.5. Asia-Pacific
  • 8.5.1. Introduction
  • 8.5.2. Key Region-Specific Dynamics
  • 8.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 8.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 8.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 8.5.5.1. China
    • 8.5.5.2. India
    • 8.5.5.3. Japan
    • 8.5.5.4. South Korea
    • 8.5.5.5. Rest of Asia-Pacific
• 8.6. Middle East and Africa
  • 8.6.1. Introduction
  • 8.6.2. Key Region-Specific Dynamics
  • 8.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 8.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

9. Competitive Landscape

• 9.1. Competitive Scenario
• 9.2. Market Positioning/Share Analysis
• 9.3. Mergers and Acquisitions Analysis

10. Company Profiles

• 10.1. IBM*
  • 10.1.1. Company Overview
  • 10.1.2. Product Portfolio and Description
  • 10.1.3. Financial Overview
  • 10.1.4. Key Developments
• 10.2. Google LLC
• 10.3. Microsoft
• 10.4. OpenAI
• 10.5. NVIDIA Corporation
• 10.6. Oracle
• 10.7. Johnson & Johnson Services, Inc.
• 10.8. NioyaTech.
• 10.9. Saxon.

LIST NOT EXHAUSTIVE

11. Appendix

• 11.1. About Us and Services
• 11.2. Contact Us