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市场调查报告书

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1629820

全球医疗保健市场的生成式人工智慧 - 2024-2031

Global Generative AI in Healthcare Market - 2024-2031

出版日期: 2024年12月30日 | 出版商:

DataM Intelligence | 英文 176 Pages | 商品交期: 最快1-2个工作天内

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简介目录

2023年，全球医疗保健市场的生成式人工智慧规模达到17.5亿美元，预计2031年将达到200.1亿美元，2024-2031年预测期间复合年增长率为35.8%。

医疗保健中的生成式人工智慧是指利用先进的人工智慧技术，可以根据现有的医疗保健资讯创建新的资料、见解和内容。这种创新方法采用复杂的演算法（包括机器学习和深度学习技术）来分析大量非结构化资料，例如医疗记录、影像资料和临床记录。主要目标是增强医疗保健服务的各个方面，包括诊断、治疗计划、患者参与和营运效率。

医疗保健领域的生成式人工智慧可以产生与现实世界医疗保健资料非常相似的合成资料。此功能对于在不损害患者隐私的情况下训练机器学习模型特别有用，使其对于研究和开发目的非常有价值。透过分析复杂的医学影像（例如 MRI 和 CT 扫描），生成式 AI 可以识别人类从业者可能难以检测到的模式。这项增强功能提高了诊断准确性并支援早期疾病检测。

由人工智慧驱动的虚拟助理透过回答与健康相关的查询、发送用药提醒和提供个人化的健康建议，为患者提供互动支援。此功能增强了患者参与度并促进了更以患者为中心的医疗保健体验。这些因素推动了全球生成式人工智慧在医疗保健市场的扩张。

市场动态：

驱动程式和限制

对个人化医疗保健解决方案的需求不断增长

对个人化医疗保健解决方案不断增长的需求正在显着推动全球生成人工智慧在医疗保健市场的成长，并预计将在整个市场预测期内推动这一成长。

医疗保健行业越来越多地采用个人化医疗，即根据患者的遗传特征、病史和生活方式因素，根据患者的具体需求量身定制治疗计划。医疗保健领域的生成人工智慧在这一转变中发挥着至关重要的作用，它透过分析大型数据集来识别为个人化治疗策略提供资讯的模式和相关性。例如，人工智慧演算法可以预测不同患者对特定治疗的反应，使医疗保健提供者能够优化治疗方法以改善结果。

医疗保健领域的生成式人工智慧擅长处理大量非结构化资料，包括电子健康记录 (EHR)、基因组资料和临床记录。这项功能使医疗保健提供者能够为患者创建全面的健康檔案，从而更有效地制定干预措施。透过综合不同的资料类型，产生人工智慧有助于识别特定于个别患者的风险因素和健康趋势，促进主动护理和早期介入。

此外，该行业的主要参与者都有关键倡议和产品发布，这将推动全球生成式人工智慧在医疗保健市场的成长。例如，根据 Microsoft Azure 2023 年 6 月的新闻，生成式 AI 有可能使医疗保健提供者提高效率、个人化护理和增强决策流程，从而彻底改变医学研究、诊断、治疗和患者护理。医疗保健领域的生成式人工智慧使研究人员能够快速有效地分析大量医疗资料。它可以自动执行资料撷取和文件审查，从而显着减少管理任务所花费的时间。

同样，2024年4月，世界卫生组织（WHO）宣布推出SARAH，即健康智慧人工智慧资源助理。这项创新的数位健康促进者原型由产生人工智慧 (AI) 提供支持，旨在在世界卫生日之前加强公众健康参与，该日的主题是「我的健康，我的权利」。

此外，2024 年 10 月，Amazon One Medical 将先进的 AI 技术整合到其医疗保健服务中，利用 AWS 生成式 AI 服务（包括 Amazon Bedrock 和 AWS HealthScribe）帮助医生节省时间并增强患者护理。所有这些因素都需要医疗保健市场中的全球生成式人工智慧。

此外，远距医疗整合的需求不断增长，有助于全球生成式人工智慧在医疗保健市场的扩张。

资料安全和隐私问题

资料安全和隐私问题将阻碍全球产生人工智慧在医疗保健市场的成长。生成式人工智慧在医疗保健中的整合为改善患者护理和营运效率提供了大量机会。然而，它也引起了对资料隐私和安全的严重担忧，特别是因为所涉及的患者资讯的敏感性。

医疗保健系统中的生成式人工智慧通常需要存取大量敏感的患者资料，包括电子健康记录 (EHR)、医学影像和个人健康资讯 (PHI)。这些资料是高度机密的，必须受到保护，以维持患者的信任并遵守法律标准。

在美国，HIPAA 制定了处理 PHI 的严格准则。医疗保健组织必须确保他们使用的任何技术都符合这些法规。这包括实施保护措施来保护 PHI 的机密性、完整性和可用性。例如，医疗保健环境中使用的任何生成式人工智慧工具都必须经过彻底的安全审查，并与提供者签署业务合作协议（BAA）以确保合规性。

根据国家生物技术资讯中心 (NCBI) 2024 年 3 月的研究出版物，生成式人工智慧在医疗保健中的整合提供了变革潜力，但由于其广泛的资料要求和固有的不透明性，它也带来了重大的隐私和安全风险。生成式人工智慧系统需要存取大量敏感的患者资料，包括电子健康记录 (EHR)、医学影像和个人健康资讯 (PHI)。因此，上述因素可能限制全球生成式人工智慧在医疗保健市场的潜在成长。

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.

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