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市场调查报告书
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2021737

人工智慧在个人化医疗领域的市场：未来预测（至2034年）－按组件、技术、治疗领域、资料类型、应用、最终使用者和地区进行分析

AI in Personalized Medicine Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Technology Therapeutic Area, Data Type, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球个人化医疗人工智慧市场预计将在 2026 年达到 28 亿美元，到 2034 年达到 573 亿美元，预测期内复合年增长率为 38.2%。

在个人化医疗中，人工智慧指的是利用机器学习和数据驱动方法，为每位患者提供量身定制的医疗服务。人工智慧系统可以分析大量的基因、临床和生活方式讯息，从而预测疾病风险、提案最佳治疗方法并改善治疗效果。这种方法透过提高诊断准确性、减少副作用以及辅助医疗专业人员提供个人化护理，推动了精准医疗的发展。最终，它能够实现更准确、更有效率、更以病人为中心的医疗决策。

基因组和多组体学数据的快速成长

基因组学和多组体学数据的快速成长是人工智慧应用的主要驱动力。随着定序成本的降低，可分析的遗传资讯量呈指数级增长。人工智慧演算法，尤其是机器学习，拥有处理这些庞大而复杂的资料集、识别疾病标记和预测药物反应的独特能力。这种能力使得医疗模式从传统的试验误法转向精准治疗性介入。此外，肿瘤学和罕见疾病领域对标靶治疗的需求日益增长，使得人工智慧驱动的分析对于为患者匹配最有效的治疗方法至关重要，从而加速了个人化医疗解决方案的普及。

限制因素：对资料隐私和缺乏互通性的担忧。

资料隐私问题和缺乏标准化的资料互通性带来了许多挑战。医疗数据高度敏感，遵守 HIPAA 和 GDPR 等法规对人工智慧开发者而言是一项复杂的挑战。此外，分散的电子健康记录 (EHR) 系统通常以孤立且不相容的格式储存数据，阻碍了创建训练强大人工智慧模型所需的大型统一数据集。某些人工智慧演算法的「黑箱」特性也阻碍了其在临床上的应用。由于医生通常需要可解释的输出结果才能信任人工智慧主导的患者照护建议，因此人工智慧融入临床工作流程的过程较为缓慢。

机会：与穿戴式装置和物联网装置集成

AIとウェアラブル健康モニタリングデバイスおよびモノのインターネット（IoT）との统合は、大きな成长机会をもたらします。智慧型手錶や体内に埋め込まれたセンサーから得られる実世界のデータの连続的なストリームにより、AIモデルは患者の健康状态を动的にモニタリングし、不利事件を予测し、治疗计画をリアルタイムで调整することが可能になります。この机能は、糖尿病や心血管疾患などの慢性疾患の管理において特に価値があります。さらに、远端医疗や远端患者监护の拡大は、従来の病院环境の外で个别化されたケアを提供できるAI搭载プラットフォームにとって好机となり、アクセスの向上と患者のエンゲージメントの向上につながります。

威胁：演算法偏差和监管不确定性

演算法偏差对人工智慧在个人化医疗中的公平应用构成重大威胁。如果人工智慧模型主要基于特定族群的资料集进行训练，其对被低估族群的预测准确率可能会显着降低。这可能导致对少数族群群体的误诊或推荐无效治疗方法，从而加剧现有的医疗保健不平等。此外，人工智慧技术的快速发展往往超越了旨在确保其安全性和有效性的法律规范，这不仅给开发者带来不确定性，而且如果过早采用检验的工具，还会给患者带来潜在风险。

新冠疫情的感染疾病

新冠疫情大大推动了人工智慧在个人化医疗领域的应用。疫苗快速研发和现有药物再利用的迫切需求，促使人们以前所未有的速度利用人工智慧分析病毒基因组和宿主反应。封锁措施加速了远端医疗和远端监测的普及，也因此增加了对用于远端管理患者资料的人工智慧工具的需求。然而，疫情危机也给医疗系统带来了沉重负担，导致非新冠研究资源被转移，并延误了一些基于人工智慧诊断的临床试验。在后疫情时代，人们将继续致力于建立具有韧性的、人工智慧主导的医疗卫生系统，使其能够对未来的健康危机做出快速且个人化的反应。

在预测期内，软体产业预计将占据最大的市场份额。

软体领域，尤其是人工智慧分析平台和临床决策支援系统（CDSS），预计将占据最大的市场份额。这种主导地位源于软体在处理复杂的基因组和临床数据并产生可操作的见解方面发挥的基础性作用。医院和研究机构正在大力投资这些平台，以提高诊断准确性并简化药物研发流程。基于云端的软体解决方案的扩充性和持续升级性进一步巩固了主导地位，因为它们构成了任何个人化医疗倡议的核心基础设施。

预计在预测期内，硬体产业将呈现最高的复合年增长率。

在预测期内，硬体领域预计将呈现最高的成长率，这主要得益于对高效能运算 (HPC) 基础设施日益增长的需求。利用基因组和影像资料集训练深度学习模型需要强大的运算能力，这推动了对先进处理器和人工智慧医疗设备的需求。此外，穿戴式健康监测设备的普及，能够为每位患者产生个人化数据，也促进了这项快速成长。随着人工智慧演算法日趋复杂，对支援这些演算法的专用硬体的需求也将持续加速成长。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这得益于其雄厚的研发投入、众多领先科技公司的强大实力以及先进的医疗基础设施。尤其值得一提的是，美国在人工智慧驱动的基因组检测和数位疗法的应用方面处于主导地位。个人化医疗的优惠报销政策和高昂的医疗费用支出正在推动先进人工智慧工具融入临床实践，从而巩固了该地区的领先地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于医疗系统的快速数位化、大规模的患者群体产生的大量数据集以及政府主导的精准医疗倡议的不断增加。中国、日本和印度等国家正在基因组研究和人工智慧基础设施进行大量投资。慢性病盛行率的上升和医疗旅游业的快速发展正在加速先进人工智慧技术的应用，以提供个人化和高效的医疗服务，从而推动市场大幅扩张。

免费客製化服务：

订阅本报告的用户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣量身定制的主要国家/地区的市场估算、预测和复合年增长率（註：基于可行性检查）
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章：执行摘要

  • 市场概览及主要亮点
  • 成长要素、挑战与机会
  • 竞争格局概述
  • 战略考虑和建议

第二章：分析框架

  • 分析的目标和范围
  • 相关人员分析
  • 分析的前提条件与限制
  • 分析方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 科技与创新趋势
  • 新兴市场和高成长市场
  • 监管和政策环境
  • 感染疾病的影响及恢復前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商议价能力
    • 买方的议价能力
    • 替代产品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球个人化医疗人工智慧市场：按组件划分

  • 软体
    • 人工智慧分析平台
    • 基因组分析软体
    • 临床决策支援系统（CDSS）
    • 预测建模工具
  • 硬体
    • 人工智慧驱动的医疗设备
    • 高效能运算基础设施
    • 穿戴式健康监测设备
  • 服务
    • 咨询服务
    • 整合和部署服务
    • 维护和支援服务

第六章：全球个人化医疗人工智慧市场：按技术划分

  • 机器学习（ML）
    • 深度学习
    • 神经网路
    • 随机森林演算法
    • 支援向量机
  • 自然语言处理（NLP）
    • 临床文字探勘
    • 医疗实体认可
    • 情绪与结果分析
  • 电脑视觉
  • 情境感知人工智慧处理
  • 专家系统
    • 基于规则的系统
    • 决定架构
    • 贝氏网络

第七章：全球人工智慧在个人化医疗领域的市场：按治疗领域划分

  • 瘤
  • 循环系统疾病
  • 神经系统疾病
  • 感染疾病
  • 罕见疾病
  • 呼吸系统疾病

第八章：全球个人化医疗人工智慧市场：按资料类型划分

  • 基因组数据
  • 临床数据
  • 影像资料
  • 真实世界数据（RWD）
  • 患者产生的数据

第九章：全球人工智慧在个人化医疗领域的市场：按应用划分

  • 药物发现与开发
    • 目标识别
    • 分子建模
    • 虚拟筛检
  • 基因组学和多体学分析
    • 基因组学
    • 蛋白质体学
    • 代谢体学
    • 药物基因体学
  • 临床决策支持
    • 诊断支持
    • 治疗方法方案
    • 疾病风险预测
  • 个人化治疗方案
  • 生物标记发现
  • 病患监测和预测分析

第十章：全球个人化医疗人工智慧市场：按最终用户划分

  • 医院和医疗保健机构
  • 製药和生物技术公司
  • 研究机构和学术机构
  • 诊断检查室
  • CRO（委外研发机构）
  • 其他最终用户

第十一章：全球个人化医疗人工智慧市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 产业加值网络与供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 企业合併（M&A）
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • NVIDIA Corporation
  • Google LLC
  • Microsoft Corporation
  • IBM Corporation
  • Illumina, Inc.
  • GE HealthCare
  • Siemens Healthineers AG
  • Tempus AI
  • Exscientia plc
  • Insilico Medicine
  • BenevolentAI
  • PathAI, Inc.
  • Guardant Health, Inc.
  • Deep Genomics
  • Paige AI, Inc.
Product Code: SMRC35005

According to Stratistics MRC, the Global AI in Personalized Medicine Market is accounted for $2.8 billion in 2026 and is expected to reach $57.3 billion by 2034, growing at a CAGR of 38.2% during the forecast period. AI in Personalized Medicine involves leveraging machine learning and data-driven techniques to customize healthcare for each patient. By examining extensive genetic, clinical, and lifestyle information, AI systems can forecast disease likelihood, recommend optimal therapies, and improve treatment effectiveness. This approach advances precision medicine by enhancing diagnostic precision, minimizing side effects, and assisting healthcare providers in delivering individualized care. Ultimately, it empowers more accurate, efficient, and patient-focused medical decision-making.

Market Dynamics:

Driver:

Exponential growth in genomic and multi-omics data

The exponential growth in genomic and multi-omics data is a primary driver for AI integration. As sequencing costs decline, the volume of genetic information available for analysis has surged. AI algorithms, particularly machine learning, are uniquely capable of processing these vast, complex datasets to identify disease markers and predict drug responses. This capability enables the shift from traditional trial-and-error medicine to precise therapeutic interventions. Furthermore, the increasing demand for targeted therapies in oncology and rare diseases necessitates AI-driven analytics to match patients with the most effective treatments, accelerating the adoption of personalized medicine solutions.

Restraint: Data privacy concerns and lack of interoperability

Significant challenges arise from data privacy concerns and the lack of standardized data interoperability. Healthcare data is highly sensitive, and navigating regulations like HIPAA and GDPR creates complexity for AI developers. Additionally, fragmented electronic health record (EHR) systems often store data in siloed, incompatible formats, hindering the creation of large, unified datasets required to train robust AI models. The "black box" nature of some AI algorithms also poses a barrier to clinical adoption, as physicians often require explainable outputs to trust AI-driven recommendations for patient care, slowing integration into clinical workflows.

Opportunity: Integration with wearables and IoT devices

The integration of AI with wearable health monitoring devices and the Internet of Things (IoT) presents a significant growth opportunity. Continuous streams of real-world data from smartwatches and implantable sensors allow AI models to monitor patient health dynamically, predict adverse events, and adjust treatment plans in real-time. This capability is particularly valuable for managing chronic diseases like diabetes and cardiovascular conditions. Moreover, the expansion of telehealth and remote patient monitoring creates a fertile ground for AI-powered platforms that can deliver personalized care outside traditional hospital settings, improving accessibility and patient engagement.

Threat: Algorithmic bias and regulatory uncertainty

Algorithmic bias poses a critical threat to the equitable deployment of AI in personalized medicine. If AI models are trained predominantly on datasets from specific demographic groups, their predictive accuracy may be significantly lower for underrepresented populations. This can lead to misdiagnosis or ineffective treatment recommendations for minority groups, exacerbating existing healthcare disparities. Additionally, the rapid pace of AI development often outstrips the regulatory frameworks designed to ensure safety and efficacy, creating uncertainty for developers and potential risks for patients if unvalidated tools are adopted prematurely.

Covid-19 Impact

The pandemic acted as a powerful catalyst for AI adoption in personalized medicine. The urgent need for rapid vaccine development and repurposing of existing drugs saw AI used to analyze viral genomics and host responses at unprecedented speeds. Lockdowns accelerated the adoption of telemedicine and remote monitoring, driving demand for AI tools to manage patient data remotely. However, the crisis also overwhelmed healthcare systems, diverting resources from non-COVID research and delaying some clinical trials for AI-based diagnostics. Post-pandemic, there is a sustained focus on building resilient, AI-driven healthcare systems capable of rapid, personalized responses to future health crises.

The software segment is expected to be the largest during the forecast period

The software segment, particularly AI analytics platforms and clinical decision support systems (CDSS), is expected to account for the largest market share. This dominance is driven by the foundational role of software in processing complex genomic and clinical data to generate actionable insights. Hospitals and research institutes are heavily investing in these platforms to enhance diagnostic accuracy and streamline drug discovery. The scalability and continuous upgradability of cloud-based software solutions further solidify their market leadership, as they form the core infrastructure for any personalized medicine initiative.

The hardware segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the hardware segment is predicted to witness the highest growth rate, driven by the increasing need for high-performance computing (HPC) infrastructure. The immense computational power required to train deep learning models on genomic and imaging datasets is fueling demand for advanced processors and AI-enabled medical devices. Additionally, the proliferation of wearable health monitoring devices that generate personalized patient data is contributing to this rapid expansion. As AI algorithms become more complex, the demand for specialized hardware to support them will continue to accelerate.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by substantial R&D investments, a strong presence of key technology players, and a sophisticated healthcare infrastructure. The United States, in particular, leads in the adoption of AI-driven genomic testing and digital therapeutics. Favorable reimbursement frameworks for personalized medicine and high healthcare expenditure support the integration of advanced AI tools into clinical practice, solidifying the region's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization of healthcare systems, large patient populations generating vast datasets, and increasing government initiatives for precision medicine. Countries like China, Japan, and India are investing heavily in genomics research and AI infrastructure. The growing prevalence of chronic diseases and a burgeoning medical tourism sector are accelerating the adoption of advanced AI technologies to offer personalized and efficient care, driving significant market expansion.

Key players in the market

Some of the key players in AI in Personalized Medicine Market include NVIDIA Corporation, Google LLC, Microsoft Corporation, IBM Corporation, Illumina, Inc., GE HealthCare, Siemens Healthineers AG, Tempus AI, Exscientia plc, Insilico Medicine, BenevolentAI, PathAI, Inc., Guardant Health, Inc., Deep Genomics, and Paige AI, Inc.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Context-Aware AI Processing
  • Expert Systems

Therapeutic Areas Covered:

  • Oncology
  • Cardiology
  • Neurology
  • Infectious Diseases
  • Rare Diseases
  • Respiratory Disorders

Data Types Covered:

  • Genomic Data
  • Clinical Data
  • Imaging Data
  • Real-World Data (RWD)
  • Patient-Generated Data

Applications Covered:

  • Drug Discovery & Development
  • Genomics & Multi-Omics Analysis
  • Clinical Decision Support
  • Personalized Treatment Planning
  • Biomarker Discovery
  • Patient Monitoring & Predictive Analytics

End Users Covered:

  • Hospitals & Healthcare Providers
  • Pharmaceutical & Biotechnology Companies
  • Research Institutes & Academic Centers
  • Diagnostic Laboratories
  • Contract Research Organizations (CROs)
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Personalized Medicine Market, By Component

  • 5.1 Software
    • 5.1.1 AI Analytics Platforms
    • 5.1.2 Genomic Analysis Software
    • 5.1.3 Clinical Decision Support Systems
    • 5.1.4 Predictive Modeling Tools
  • 5.2 Hardware
    • 5.2.1 AI-Enabled Medical Devices
    • 5.2.2 High-Performance Computing Infrastructure
    • 5.2.3 Wearable Health Monitoring Devices
  • 5.3 Services
    • 5.3.1 Consulting Services
    • 5.3.2 Integration & Deployment Services
    • 5.3.3 Maintenance & Support Services

6 Global AI in Personalized Medicine Market, By Technology

  • 6.1 Machine Learning
    • 6.1.1 Deep Learning
    • 6.1.2 Neural Networks
    • 6.1.3 Random Forest Algorithms
    • 6.1.4 Support Vector Machines
  • 6.2 Natural Language Processing (NLP)
    • 6.2.1 Clinical Text Mining
    • 6.2.2 Medical Entity Recognition
    • 6.2.3 Sentiment and Outcome Analysis
  • 6.3 Computer Vision
  • 6.4 Context-Aware AI Processing
  • 6.5 Expert Systems
    • 6.5.1 Rule-Based Systems
    • 6.5.2 Decision Trees
    • 6.5.3 Bayesian Networks

7 Global AI in Personalized Medicine Market, By Therapeutic Area

  • 7.1 Oncology
  • 7.2 Cardiology
  • 7.3 Neurology
  • 7.4 Infectious Diseases
  • 7.5 Rare Diseases
  • 7.6 Respiratory Disorders

8 Global AI in Personalized Medicine Market, By Data Type

  • 8.1 Genomic Data
  • 8.2 Clinical Data
  • 8.3 Imaging Data
  • 8.4 Real-World Data (RWD)
  • 8.5 Patient-Generated Data

9 Global AI in Personalized Medicine Market, By Application

  • 9.1 Drug Discovery & Development
    • 9.1.1 Target Identification
    • 9.1.2 Molecular Modeling
    • 9.1.3 Virtual Screening
  • 9.2 Genomics & Multi-Omics Analysis
    • 9.2.1 Genomics
    • 9.2.2 Proteomics
    • 9.2.3 Metabolomics
    • 9.2.4 Pharmacogenomics
  • 9.3 Clinical Decision Support
    • 9.3.1 Diagnosis Support
    • 9.3.2 Treatment Selection
    • 9.3.3 Disease Risk Prediction
  • 9.4 Personalized Treatment Planning
  • 9.5 Biomarker Discovery
  • 9.6 Patient Monitoring & Predictive Analytics

10 Global AI in Personalized Medicine Market, By End User

  • 10.1 Hospitals & Healthcare Providers
  • 10.2 Pharmaceutical & Biotechnology Companies
  • 10.3 Research Institutes & Academic Centers
  • 10.4 Diagnostic Laboratories
  • 10.5 Contract Research Organizations (CROs)
  • 10.6 Other End Users

11 Global AI in Personalized Medicine Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA Corporation
  • 14.2 Google LLC
  • 14.3 Microsoft Corporation
  • 14.4 IBM Corporation
  • 14.5 Illumina, Inc.
  • 14.6 GE HealthCare
  • 14.7 Siemens Healthineers AG
  • 14.8 Tempus AI
  • 14.9 Exscientia plc
  • 14.10 Insilico Medicine
  • 14.11 BenevolentAI
  • 14.12 PathAI, Inc.
  • 14.13 Guardant Health, Inc.
  • 14.14 Deep Genomics
  • 14.15 Paige AI, Inc.

List of Tables

  • Table 1 Global AI in Personalized Medicine Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Personalized Medicine Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Personalized Medicine Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Personalized Medicine Market Outlook, By AI Analytics Platforms (2023-2034) ($MN)
  • Table 5 Global AI in Personalized Medicine Market Outlook, By Genomic Analysis Software (2023-2034) ($MN)
  • Table 6 Global AI in Personalized Medicine Market Outlook, By Clinical Decision Support Systems (2023-2034) ($MN)
  • Table 7 Global AI in Personalized Medicine Market Outlook, By Predictive Modeling Tools (2023-2034) ($MN)
  • Table 8 Global AI in Personalized Medicine Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 9 Global AI in Personalized Medicine Market Outlook, By AI-Enabled Medical Devices (2023-2034) ($MN)
  • Table 10 Global AI in Personalized Medicine Market Outlook, By High-Performance Computing Infrastructure (2023-2034) ($MN)
  • Table 11 Global AI in Personalized Medicine Market Outlook, By Wearable Health Monitoring Devices (2023-2034) ($MN)
  • Table 12 Global AI in Personalized Medicine Market Outlook, By Services (2023-2034) ($MN)
  • Table 13 Global AI in Personalized Medicine Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 14 Global AI in Personalized Medicine Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 15 Global AI in Personalized Medicine Market Outlook, By Maintenance & Support Services (2023-2034) ($MN)
  • Table 16 Global AI in Personalized Medicine Market Outlook, By Technology (2023-2034) ($MN)
  • Table 17 Global AI in Personalized Medicine Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 18 Global AI in Personalized Medicine Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 19 Global AI in Personalized Medicine Market Outlook, By Neural Networks (2023-2034) ($MN)
  • Table 20 Global AI in Personalized Medicine Market Outlook, By Random Forest Algorithms (2023-2034) ($MN)
  • Table 21 Global AI in Personalized Medicine Market Outlook, By Support Vector Machines (2023-2034) ($MN)
  • Table 22 Global AI in Personalized Medicine Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 23 Global AI in Personalized Medicine Market Outlook, By Clinical Text Mining (2023-2034) ($MN)
  • Table 24 Global AI in Personalized Medicine Market Outlook, By Medical Entity Recognition (2023-2034) ($MN)
  • Table 25 Global AI in Personalized Medicine Market Outlook, By Sentiment and Outcome Analysis (2023-2034) ($MN)
  • Table 26 Global AI in Personalized Medicine Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 27 Global AI in Personalized Medicine Market Outlook, By Context-Aware AI Processing (2023-2034) ($MN)
  • Table 28 Global AI in Personalized Medicine Market Outlook, By Expert Systems (2023-2034) ($MN)
  • Table 29 Global AI in Personalized Medicine Market Outlook, By Rule-Based Systems (2023-2034) ($MN)
  • Table 30 Global AI in Personalized Medicine Market Outlook, By Decision Trees (2023-2034) ($MN)
  • Table 31 Global AI in Personalized Medicine Market Outlook, By Bayesian Networks (2023-2034) ($MN)
  • Table 32 Global AI in Personalized Medicine Market Outlook, By Therapeutic Area (2023-2034) ($MN)
  • Table 33 Global AI in Personalized Medicine Market Outlook, By Oncology (2023-2034) ($MN)
  • Table 34 Global AI in Personalized Medicine Market Outlook, By Cardiology (2023-2034) ($MN)
  • Table 35 Global AI in Personalized Medicine Market Outlook, By Neurology (2023-2034) ($MN)
  • Table 36 Global AI in Personalized Medicine Market Outlook, By Infectious Diseases (2023-2034) ($MN)
  • Table 37 Global AI in Personalized Medicine Market Outlook, By Rare Diseases (2023-2034) ($MN)
  • Table 38 Global AI in Personalized Medicine Market Outlook, By Respiratory Disorders (2023-2034) ($MN)
  • Table 39 Global AI in Personalized Medicine Market Outlook, By Data Type (2023-2034) ($MN)
  • Table 40 Global AI in Personalized Medicine Market Outlook, By Genomic Data (2023-2034) ($MN)
  • Table 41 Global AI in Personalized Medicine Market Outlook, By Clinical Data (2023-2034) ($MN)
  • Table 42 Global AI in Personalized Medicine Market Outlook, By Imaging Data (2023-2034) ($MN)
  • Table 43 Global AI in Personalized Medicine Market Outlook, By Real-World Data (RWD) (2023-2034) ($MN)
  • Table 44 Global AI in Personalized Medicine Market Outlook, By Patient-Generated Data (2023-2034) ($MN)
  • Table 45 Global AI in Personalized Medicine Market Outlook, By Application (2023-2034) ($MN)
  • Table 46 Global AI in Personalized Medicine Market Outlook, By Drug Discovery & Development (2023-2034) ($MN)
  • Table 47 Global AI in Personalized Medicine Market Outlook, By Target Identification (2023-2034) ($MN)
  • Table 48 Global AI in Personalized Medicine Market Outlook, By Molecular Modeling (2023-2034) ($MN)
  • Table 49 Global AI in Personalized Medicine Market Outlook, By Virtual Screening (2023-2034) ($MN)
  • Table 50 Global AI in Personalized Medicine Market Outlook, By Genomics & Multi-Omics Analysis (2023-2034) ($MN)
  • Table 51 Global AI in Personalized Medicine Market Outlook, By Genomics (2023-2034) ($MN)
  • Table 52 Global AI in Personalized Medicine Market Outlook, By Proteomics (2023-2034) ($MN)
  • Table 53 Global AI in Personalized Medicine Market Outlook, By Metabolomics (2023-2034) ($MN)
  • Table 54 Global AI in Personalized Medicine Market Outlook, By Pharmacogenomics (2023-2034) ($MN)
  • Table 55 Global AI in Personalized Medicine Market Outlook, By Clinical Decision Support (2023-2034) ($MN)
  • Table 56 Global AI in Personalized Medicine Market Outlook, By Diagnosis Support (2023-2034) ($MN)
  • Table 57 Global AI in Personalized Medicine Market Outlook, By Treatment Selection (2023-2034) ($MN)
  • Table 58 Global AI in Personalized Medicine Market Outlook, By Disease Risk Prediction (2023-2034) ($MN)
  • Table 59 Global AI in Personalized Medicine Market Outlook, By Personalized Treatment Planning (2023-2034) ($MN)
  • Table 60 Global AI in Personalized Medicine Market Outlook, By Biomarker Discovery (2023-2034) ($MN)
  • Table 61 Global AI in Personalized Medicine Market Outlook, By Patient Monitoring & Predictive Analytics (2023-2034) ($MN)
  • Table 62 Global AI in Personalized Medicine Market Outlook, By End User (2023-2034) ($MN)
  • Table 63 Global AI in Personalized Medicine Market Outlook, By Hospitals & Healthcare Providers (2023-2034) ($MN)
  • Table 64 Global AI in Personalized Medicine Market Outlook, By Pharmaceutical & Biotechnology Companies (2023-2034) ($MN)
  • Table 65 Global AI in Personalized Medicine Market Outlook, By Research Institutes & Academic Centers (2023-2034) ($MN)
  • Table 66 Global AI in Personalized Medicine Market Outlook, By Diagnostic Laboratories (2023-2034) ($MN)
  • Table 67 Global AI in Personalized Medicine Market Outlook, By Contract Research Organizations (CROs) (2023-2034) ($MN)
  • Table 68 Global AI in Personalized Medicine Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.