首頁 > 市場調查報告書 > 醫療設備

e保健體外診斷藥物開發人工智能人工智能藥物研發診斷用人工智能

市場調查報告書

商品編碼

1629820

全球醫療保健市場的生成式人工智慧 - 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個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

2023年，全球醫療保健市場的生成式人工智慧規模達到17.5億美元，預計2031年將達到200.1億美元，2024-2031年預測期間複合年成長率為35.8%。

醫療保健中的生成式人工智慧是指利用先進的人工智慧技術，可以根據現有的醫療保健資訊創建新的資料、見解和內容。這種創新方法採用複雜的演算法（包括機器學習和深度學習技術）來分析大量非結構化資料，例如醫療記錄、影像資料和臨床記錄。主要目標是增強醫療保健服務的各個方面，包括診斷、治療計劃、患者參與和營運效率。

醫療保健領域的生成式人工智慧可以產生與現實世界醫療保健資料非常相似的合成資料。此功能對於在不損害患者隱私的情況下訓練機器學習模型特別有用，使其對於研究和開發目的非常有價值。透過分析複雜的醫學影像（例如 MRI 和 CT 掃描），生成式 AI 可以識別人類從業者可能難以檢測到的模式。這項增強功能提高了診斷準確性並支援早期疾病檢測。

由人工智慧驅動的虛擬助理透過回答與健康相關的查詢、發送用藥提醒和提供個人化的健康建議，為患者提供互動支援。此功能增強了患者參與度並促進了更以患者為中心的醫療保健體驗。這些因素推動了全球生成式人工智慧在醫療保健市場的擴張。

市場動態：

驅動程式和限制

對個人化醫療保健解決方案的需求不斷成長

對個人化醫療保健解決方案不斷成長的需求正在顯著推動全球生成人工智慧在醫療保健市場的成長，並預計將在整個市場預測期內推動這一成長。

醫療保健行業擴大採用個人化醫療，即根據患者的遺傳特徵、病史和生活方式因素，根據患者的具體需求量身定做治療計劃。醫療保健領域的生成人工智慧在這一轉變中發揮著至關重要的作用，它透過分析大型資料集來識別為個人化治療策略提供資訊的模式和相關性。例如，人工智慧演算法可以預測不同患者對特定治療的反應，使醫療保健提供者能夠最佳化治療方法以改善結果。

醫療保健領域的生成式人工智慧擅長處理大量非結構化資料，包括電子健康記錄 (EHR)、基因組資料和臨床記錄。這項功能使醫療保健提供者能夠為患者創建全面的健康檔案，從而更有效地制定干涉措施。透過綜合不同的資料類型，產生人工智慧有助於識別特定於個別患者的風險因素和健康趨勢，促進主動護理和早期介入。

此外，該行業的主要參與者都有關鍵舉措和產品發布，這將推動全球生成式人工智慧在醫療保健市場的成長。例如，根據 Microsoft Azure 2023 年 6 月的新聞，生成式 AI 有可能使醫療保健提供者提高效率、個人化護理和增強決策流程，從而徹底改變醫學研究、診斷、治療和患者護理。醫療保健領域的生成式人工智慧使研究人員能夠快速有效地分析大量醫療資料。它可以自動執行資料擷取和文件審查，從而顯著減少管理任務所花費的時間。

同樣，2024年4月，世界衛生組織（WHO）宣布推出SARAH，即健康智慧人工智慧資源助理。這項創新的數位健康促進者原型由產生人工智慧 (AI) 提供支持，旨在在世界衛生日之前加強公眾健康參與，該日的主題是「我的健康，我的權利」。

此外，2024 年 10 月，Amazon One Medical 將先進的 AI 技術整合到其醫療保健服務中，利用 AWS 生成式 AI 服務（包括 Amazon Bedrock 和 AWS HealthScribe）幫助醫生節省時間並增強患者護理。所有這些因素都需要醫療保健市場中的全球生成式人工智慧。

此外，遠距醫療整合的需求不斷成長，有助於全球生成式人工智慧在醫療保健市場的擴張。

資料安全和隱私問題

資料安全和隱私問題將阻礙全球產生人工智慧在醫療保健市場的成長。生成式人工智慧在醫療保健中的整合為改善患者護理和營運效率提供了大量機會。然而，它也引起了對資料隱私和安全的嚴重擔憂，特別是因為所涉及的患者資訊的敏感性。

醫療保健系統中的生成式人工智慧通常需要存取大量敏感的患者資料，包括電子健康記錄 (EHR)、醫學影像和個人健康資訊 (PHI)。這些資料是高度機密的，必須受到保護，以維持患者的信任並遵守法律標準。

在美國，HIPAA 制定了處理 PHI 的嚴格準則。醫療保健組織必須確保他們使用的任何技術都符合這些法規。這包括實施保護措施來保護 PHI 的機密性、完整性和可用性。例如，醫療保健環境中使用的任何生成式人工智慧工具都必須經過徹底的安全審查，並與提供者簽署業務合作協議（BAA）以確保合規性。

根據國家生物技術資訊中心 (NCBI) 2024 年 3 月的研究出版物，生成式人工智慧在醫療保健中的整合提供了變革潛力，但由於其廣泛的資料要求和固有的不透明性，它也帶來了重大的隱私和安全風險。生成式人工智慧系統需要存取大量敏感的患者資料，包括電子健康記錄 (EHR)、醫學影像和個人健康資訊 (PHI)。因此，上述因素可能限制全球生成式人工智慧在醫療保健市場的潛在成長。

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