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市場調查報告書
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2021737

人工智慧在個人化醫療領域的市場：未來預測（至2034年）－按組件、技術、治療領域、資料類型、應用、最終使用者和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球個人化醫療人工智慧市場預計將在 2026 年達到 28 億美元，到 2034 年達到 573 億美元，預測期內複合年成長率為 38.2%。

在個人化醫療中，人工智慧指的是利用機器學習和數據驅動方法，為每位患者提供量身定做的醫療服務。人工智慧系統可以分析大量的基因、臨床和生活方式訊息，從而預測疾病風險、提案最佳治療方法並改善治療效果。這種方法透過提高診斷準確性、減少副作用以及輔助醫療專業人員提供個人化護理，推動了精準醫療的發展。最終，它能夠實現更準確、更有效率、更以病人為中心的醫療決策。

基因組和多組體學數據的快速成長

基因組學和多組體學數據的快速成長是人工智慧應用的主要驅動力。隨著定序成本的降低，可分析的遺傳資訊量呈指數級成長。人工智慧演算法，尤其是機器學習，擁有處理這些龐大而複雜的資料集、識別疾病標記和預測藥物反應的獨特能力。這種能力使得醫療模式從傳統的試驗誤法轉向精準治療性介入。此外，腫瘤學和罕見疾病領域對標靶治療的需求日益成長，使得人工智慧驅動的分析對於為患者匹配最有效的治療方法至關重要，從而加速了個人化醫療解決方案的普及。

限制因素：對資料隱私和缺乏互通性的擔憂。

資料隱私問題和缺乏標準化的資料互通性帶來了許多挑戰。醫療數據高度敏感，遵守 HIPAA 和 GDPR 等法規對人工智慧開發者而言是一項複雜的挑戰。此外，分散的電子健康記錄 (EHR) 系統通常以孤立且不相容的格式儲存數據，阻礙了創建訓練強大人工智慧模型所需的大型統一資料集。某些人工智慧演算法的「黑箱」特性也阻礙了其在臨床上的應用。由於醫生通常需要可解釋的輸出結果才能信任人工智慧主導的患者照護建議，因此人工智慧融入臨床工作流程的過程較為緩慢。

機會：與穿戴式裝置和物聯網裝置整合

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）との統合は、大きな成長機会をもたらします。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連続的なストリームにより、AIモデルは患者の健康状態を動的にモニタリングし、不利事件を予測し、治療計画をリアルタイムで調整することが可能になります。この機能は、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、遠端醫療や遠端患者監護の拡大は、従来の病院環境の外で個別化されたケアを提供できるAI搭載プラットフォームにとって好機となり、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。

威脅：演算法偏差和監管不確定性

演算法偏差對人工智慧在個人化醫療中的公平應用構成重大威脅。如果人工智慧模型主要基於特定族群的資料集進行訓練，其對被低估族群的預測準確率可能會顯著降低。這可能導致對少數族群群體的誤診或推薦無效治療方法，從而加劇現有的醫療保健不平等。此外，人工智慧技術的快速發展往往超越了旨在確保其安全性和有效性的法律規範，這不僅給開發者帶來不確定性，而且如果過早採用檢驗的工具，還會給患者帶來潛在風險。

新冠疫情的感染疾病

新冠疫情大大推動了人工智慧在個人化醫療領域的應用。疫苗快速研發和現有藥物再利用的迫切需求，促使人們以前所未有的速度利用人工智慧分析病毒基因組和宿主反應。封鎖措施加速了遠端醫療和遠端監測的普及，也因此增加了對用於遠端管理患者資料的人工智慧工具的需求。然而，疫情危機也給醫療系統帶來了沉重負擔，導致非新冠研究資源被轉移，並延誤了一些基於人工智慧診斷的臨床試驗。在後疫情時代，人們將繼續致力於建立具有韌性的、人工智慧主導的醫療衛生系統，使其能夠對未來的健康危機做出快速且個人化的反應。

在預測期內，軟體產業預計將佔據最大的市場佔有率。

軟體領域，尤其是人工智慧分析平台和臨床決策支援系統（CDSS），預計將佔據最大的市場佔有率。這種主導地位源於軟體在處理複雜的基因組和臨床數據並產生可操作的見解方面發揮的基礎性作用。醫院和研究機構正在大力投資這些平台，以提高診斷準確性並簡化藥物研發流程。基於雲端的軟體解決方案的擴充性和持續升級性進一步鞏固了主導地位，因為它們構成了任何個人化醫療舉措的核心基礎設施。

預計在預測期內，硬體產業將呈現最高的複合年成長率。

在預測期內，硬體領域預計將呈現最高的成長率，這主要得益於對高效能運算 (HPC) 基礎設施日益成長的需求。利用基因組和影像資料集訓練深度學習模型需要強大的運算能力，這推動了對先進處理器和人工智慧醫療設備的需求。此外，穿戴式健康監測設備的普及，能夠為每位患者產生個人化數據，也促進了這項快速成長。隨著人工智慧演算法日趨複雜，對支援這些演算法的專用硬體的需求也將持續加速成長。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其雄厚的研發投入、眾多領先科技公司的強大實力以及先進的醫療基礎設施。尤其值得一提的是，美國在人工智慧驅動的基因組檢測和數位療法的應用方面處於主導地位。個人化醫療的優惠報銷政策和高昂的醫療費用支出正在推動先進人工智慧工具融入臨床實踐，從而鞏固了該地區的領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於醫療系統的快速數位化、大規模的患者群體產生的大量資料集以及政府主導的精準醫療舉措的不斷增加。中國、日本和印度等國家正在基因組研究和人工智慧基礎設施進行大量投資。慢性病盛行率的上升和醫療旅遊業的快速發展正在加速先進人工智慧技術的應用，以提供個人化和高效的醫療服務，從而推動市場大幅擴張。

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訂閱本報告的用戶可享有以下免費自訂選項之一：

  • 公司簡介
    • 對其他公司（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣量身定做的主要國家/地區的市場估算、預測和複合年成長率（註：基於可行性檢查）
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 成長要素、挑戰與機遇
  • 競爭格局概述
  • 戰略考慮和建議

第2章：分析框架

  • 分析的目標和範圍
  • 相關人員分析
  • 分析的前提條件與限制
  • 分析方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 科技與創新趨勢
  • 新興市場和高成長市場
  • 監管和政策環境
  • 感染疾病的影響及恢復前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商議價能力
    • 買方的議價能力
    • 替代產品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球個人化醫療人工智慧市場：按組件分類

  • 軟體
    • 人工智慧分析平台
    • 基因組分析軟體
    • 臨床決策支援系統（CDSS）
    • 預測建模工具
  • 硬體
    • 人工智慧驅動的醫療設備
    • 高效能運算基礎設施
    • 穿戴式健康監測設備
  • 服務
    • 諮詢服務
    • 整合和部署服務
    • 維護和支援服務

第6章：全球個人化醫療人工智慧市場：按技術分類

  • 機器學習（ML）
    • 深度學習
    • 神經網路
    • 隨機森林演算法
    • 支援向量機
  • 自然語言處理（NLP）
    • 臨床文字探勘
    • 醫療實體認可
    • 情緒與結果分析
  • 電腦視覺
  • 情境感知人工智慧處理
  • 專家系統
    • 基於規則的系統
    • 決定架構
    • 貝氏網路

第7章：全球人工智慧在個人化醫療領域的市場：按治療領域分類

  • 瘤
  • 循環系統疾病
  • 神經系統疾病
  • 感染疾病
  • 罕見疾病
  • 呼吸系統疾病

第8章：全球個人化醫療人工智慧市場：按資料類型分類

  • 基因組數據
  • 臨床數據
  • 影像資料
  • 真實世界數據（RWD）
  • 患者產生的數據

第9章：全球人工智慧在個人化醫療領域的市場：按應用分類

  • 藥物發現與開發
    • 目標識別
    • 分子建模
    • 虛擬篩檢
  • 基因組學和多體學分析
    • 基因組學
    • 蛋白質體學
    • 代謝體學
    • 藥物基因體學
  • 臨床決策支持
    • 診斷支持
    • 治療方法方案
    • 疾病風險預測
  • 個人化治療方案
  • 生物標記發現
  • 病患監測和預測分析

第10章：全球個人化醫療人工智慧市場：按最終用戶分類

  • 醫院和醫療保健機構
  • 製藥和生物技術公司
  • 研究機構和學術機構
  • 診斷檢查室
  • CRO（委外研發機構）
  • 其他最終用戶

第11章：全球個人化醫療人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 產業加值網路與供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 企業合併（M&A）
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • 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.