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個人化醫療市場中的生成人工智慧 - 2018-2028 年全球產業規模、佔有率、趨勢、機會和預測，按個人化醫療治療、部署模型、最終用戶區域和競爭細分

Generative AI in Personalized Medicine Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Personalized Medicine Therapeutics, By Deployment Model, By End-User Region and Competition
出版日期: | 出版商: TechSci Research | 英文 181 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2022 年，全球個人化醫療市場中的生成人工智慧價值為 1.5212 億美元，預計在預測期內將強勁成長，到 2028 年CAGR為25.62%。全球個人化醫療市場中的生成人工智慧是一個充滿活力且快速發展的領域處於人工智慧 (AI) 和醫療保健的交叉點。隨著個人化醫療日益受到重視，利用生成式人工智慧已成為為個別患者量身定做醫療方案的變革力量。該市場的特點是應用先進演算法和機器學習技術來分析大量資料集，包括基因組學、蛋白質組學和患者記錄。主要目標是破解複雜的模式和相關性，以指導臨床醫生設計個人化的治療計劃。

生成式人工智慧透過模擬和產生新的分子結構並預測其生物效應，在藥物發現、治療最佳化和疾病預測中發揮關鍵作用。由於複雜疾病的日益普及以及人們日益認知到傳統一刀切的醫療保健方法的局限性，市場需求激增。該領域的公司正在積極開發創新的人工智慧解決方案，以提高診斷準確性、預測患者對特定療法的反應，並最終改善臨床結果。

將生成式人工智慧整合到醫療保健系統中也促進了科技公司、製藥公司和醫療保健提供者之間的合作。監管機構正在密切關注與在個人化醫療中使用人工智慧相關的倫理影響和資料隱私問題，有助於在市場內建立指導方針和標準。隨著全球個人化醫療市場中的生成人工智慧不斷擴大，主要趨勢包括多組學資料的整合、可解釋人工智慧的興起以增強信任和透明度，以及去中心化臨床試驗的出現。

市場概況
預測期 2024-2028
2022 年市場規模 1.5212億美元
2028 年市場規模 5.9190億美元
2023-2028 年CAGR 25.62%
成長最快的細分市場 醫院和診所
最大的市場 北美洲

隨著利害關係人認知到生成人工智慧徹底改變醫療保健服務的潛力，並迎來一個真正個人化醫療的時代，即根據每個患者獨特的基因組成和特徵量身定做治療方法，該市場有望實現顯著成長。儘管互通性、監管合規性和道德考量等挑戰仍然存在，但人工智慧技術的不斷進步和整個行業的協作努力正在推動市場走向未來，精準醫療將成為全球醫療保健實踐的基石。

目錄

第 1 章：產品概述

  • 市場定義
  • 市場範圍
    • 涵蓋的市場
    • 研究年份
    • 主要市場區隔

第 2 章：研究方法

  • 研究目的
  • 基線方法
  • 主要產業夥伴
  • 主要協會和二手資料來源
  • 預測方法
  • 數據三角測量與驗證
  • 假設和限制

第 3 章：執行摘要

  • 市場概況
  • 主要市場細分概述
  • 主要市場參與者概述
  • 重點地區/國家概況
  • 市場促進因素、挑戰、趨勢概述

第 4 章：全球個人化醫療中的生成人工智慧市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 個人化醫學治療（藥物、基因組醫學、設備）
    • 按部署模型（本地、基於雲端）
    • 按最終使用者（醫院和診所、門診手術中心、其他）
    • 按地區
    • 按公司分類 (2022)
  • 市場地圖
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按地區

第 5 章：亞太地區個人化醫療中的生成人工智慧市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國個人化醫療中的生成人工智慧
    • 印度個人化醫療中的生成人工智慧
    • 澳洲個人化醫療中的生成人工智慧
    • 日本個人化醫療中的生成人工智慧
    • 韓國個人化醫療中的生成人工智慧

第 6 章：歐洲個人化醫療中的生成人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按國家/地區
  • 歐洲：國家分析
    • 法國
    • 德國
    • 西班牙
    • 義大利
    • 英國

第 7 章：北美個人化醫療中的生成人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 墨西哥
    • 加拿大

第 8 章：南美洲個人化醫療市場展望中的生成人工智慧

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 9 章：中東和非洲個人化醫療市場展望中的生成人工智慧

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 透過個人化醫學治療
    • 按部署模型
    • 按最終用戶
    • 按國家/地區
  • MEA：國家分析
    • 南非個人化醫療中的生成人工智慧
    • 沙烏地阿拉伯個人化醫療中的生成人工智慧
    • 阿拉伯聯合大公國個人化醫療中的生成人工智慧
    • 埃及個人化醫療中的生成人工智慧

第 10 章：市場動態

  • 促進要素
  • 挑戰

第 11 章：市場趨勢與發展

  • 最近的發展
  • 產品發布
  • 併購

第 12 章：個人化醫療市場中的全球生成人工智慧：SWOT 分析

第 13 章：波特的五力分析

  • 產業競爭
  • 新進入者的潛力
  • 供應商的力量
  • 客戶的力量
  • 替代產品的威脅

第14章：競爭格局

  • 強生公司
    • Business Overview
    • Company Snapshot
    • Products & Services
    • Current Capacity Analysis
    • Financials (In case of listed)
    • Recent Developments
    • SWOT Analysis
  • 蝴蝶網
  • 深度基因體學
  • 谷歌有限責任公司
  • IBM 沃森健康
  • 微軟公司
  • 艾多克
  • 英科醫療
  • 路徑AI
  • 騰訊控股有限公司

第 15 章：策略建議

第 16 章：關於我們與免責聲明

簡介目錄
Product Code: 20049

Global Generative AI in Personalized Medicine Market has valued at USD 152.12 million in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 25.62% through 2028. The Global Generative AI in Personalized Medicine Market is a dynamic and rapidly evolving sector at the intersection of artificial intelligence (AI) and healthcare. As personalized medicine gains prominence, leveraging generative AI has become a transformative force in tailoring medical treatments to individual patients. This market is characterized by the application of advanced algorithms and machine learning techniques to analyze vast datasets, including genomics, proteomics, and patient records. The primary goal is to decipher intricate patterns and correlations that can guide clinicians in designing personalized treatment plans.

Generative AI plays a pivotal role in drug discovery, treatment optimization, and disease prediction by simulating and generating new molecular structures and predicting their biological effects. The market witnesses a surge in demand due to the increasing prevalence of complex diseases and the growing recognition of the limitations of traditional one-size-fits-all approaches to healthcare. Companies in this space are actively developing innovative AI solutions to enhance diagnostic accuracy, predict patient responses to specific therapies, and ultimately improve clinical outcomes.

The integration of generative AI into healthcare systems is also fostering collaborations between technology firms, pharmaceutical companies, and healthcare providers. Regulatory bodies are closely monitoring the ethical implications and data privacy concerns associated with the use of AI in personalized medicine, contributing to the establishment of guidelines and standards within the market. As the Global Generative AI in Personalized Medicine Market continues to expand, key trends include the incorporation of multi-omics data, the rise of explainable AI to enhance trust and transparency, and the emergence of decentralized clinical trials.

Market Overview
Forecast Period2024-2028
Market Size 2022USD 152.12 Million
Market Size 2028USD 591.90 Million
CAGR 2023-202825.62%
Fastest Growing SegmentHospitals & Clinics
Largest MarketNorth America

The market is poised for significant growth as stakeholders recognize the potential of generative AI to revolutionize healthcare delivery and usher in an era of truly personalized medicine, where treatments are tailored to the unique genetic makeup and characteristics of each patient. While challenges such as interoperability, regulatory compliance, and ethical considerations persist, the ongoing advancements in AI technologies and collaborative efforts across the industry are driving the market towards a future where precision medicine becomes the cornerstone of healthcare practices worldwide.

Key Market Drivers

Rising Prevalence of Complex Diseases

The escalating prevalence of complex diseases has become a catalyst for the burgeoning growth of the global generative AI in personalized medicine market. The 21st century has witnessed a paradigm shift in healthcare, with an increasing focus on tailoring treatments to the unique genetic makeup of individuals. Complex diseases, characterized by multifaceted etiologies and intricate molecular mechanisms, present formidable challenges for traditional therapeutic approaches. As the incidence of conditions such as cancer, cardiovascular diseases, and neurological disorders continues to rise worldwide, there is a pressing need for innovative solutions that can decipher the intricate interplay of genetic, environmental, and lifestyle factors influencing disease progression.

generative artificial intelligence (AI) has emerged as a transformative force, offering unprecedented capabilities in data analysis, pattern recognition, and prediction. The ability of generative AI algorithms to sift through vast datasets, including genomic information, clinical records, and patient outcomes, enables the identification of subtle patterns and correlations that elude traditional analytical methods. This, in turn, empowers healthcare professionals with valuable insights into the underlying mechanisms of diseases, paving the way for more precise and personalized therapeutic interventions.

The demand for personalized medicine is intricately linked to the rising prevalence of complex diseases, as conventional one-size-fits-all approaches often prove insufficient in addressing the unique genetic variations and molecular profiles of individual patients. Generative AI plays a pivotal role in this shift towards personalized medicine by aiding in the identification of biomarkers, predicting patient responses to specific treatments, and optimizing therapeutic regimens based on individualized data. The integration of AI-driven technologies into the realm of healthcare not only enhances diagnostic accuracy but also streamlines the drug discovery and development process, ultimately leading to more effective and targeted therapies.

The global generative AI in personalized medicine market is witnessing a surge in investment and innovation, with pharmaceutical companies, research institutions, and technology firms actively contributing to its growth. As the healthcare industry embraces the potential of generative AI to unlock the complexities of diseases and deliver personalized treatment strategies, the market is poised for substantial expansion.

Advancements in Genomic Research

Advancements in genomic research stand as a driving force behind the flourishing Global Generative AI in Personalized Medicine Market. The completion of the Human Genome Project marked a watershed moment, providing an exhaustive blueprint of human DNA. Since then, ongoing breakthroughs in genomic technologies have exponentially increased the volume and complexity of genetic data available. Generative AI, equipped with sophisticated algorithms, has emerged as an indispensable tool to navigate and extract meaningful insights from this vast genomic landscape.

By rapidly analyzing and interpreting individual genetic variations, generative AI contributes to the identification of specific biomarkers, disease susceptibilities, and therapeutic targets. This capability is pivotal in tailoring medical treatments to the unique genetic makeup of each patient. As the field of genomics continues to evolve, with initiatives like precision medicine becoming mainstream, the demand for generative AI solutions in personalized medicine intensifies. The ability of generative AI to decipher the intricacies of genetic information positions it as a linchpin in the development and implementation of personalized treatment plans.

The synergy between advancements in genomic research and the computational power of generative AI not only expedites the understanding of genetic complexities but also catalyzes innovations in drug discovery, disease prediction, and treatment optimization. As the Generative AI in Personalized Medicine Market capitalizes on these advancements, it propels healthcare into an era where genomic insights, powered by AI, pave the way for a more targeted and effective approach to patient care. The seamless integration of generative AI and genomic research not only enhances diagnostic precision but also accelerates the realization of truly personalized medicine, where healthcare interventions are tailored to the distinct genetic profile of each individual. In essence, the symbiotic relationship between the relentless progress in genomic research and the transformative capabilities of generative AI underscores the pivotal role this dynamic duo plays in reshaping the landscape of personalized medicine and steering the healthcare industry towards a more individualized and effective future.

Drug Discovery and Development

Traditional drug discovery has long been a laborious and resource-intensive process, marked by high failure rates and protracted timelines. However, the integration of generative AI into this domain has ushered in a new era of efficiency and precision. AI-driven algorithms excel in the analysis of vast datasets, including molecular structures, biological pathways, and clinical trial outcomes. This analytical prowess enables researchers to identify potential drug candidates more rapidly and accurately than conventional methods, significantly reducing the time and costs associated with drug development.

In the realm of personalized medicine, where the focus is on tailoring treatments to the unique genetic and molecular profiles of individual patients, generative AI plays a pivotal role. The ability of AI algorithms to analyze genomic data and discern intricate patterns allows for the identification of specific biomarkers associated with diseases. This information is instrumental in the development of targeted therapies, ensuring that interventions are not only more efficacious but also tailored to the genetic nuances of each patient. The precision offered by generative AI in predicting patient responses to different drugs facilitates the selection of the most appropriate and effective treatment strategies, thereby enhancing overall therapeutic outcomes.

Pharmaceutical companies and research institutions are increasingly leveraging generative AI technologies to expedite the drug discovery and development process. Virtual screening of compounds, predictive modeling of drug interactions, and the identification of novel targets are among the many applications where AI proves invaluable. This synergy between generative AI and drug development aligns with the paradigm shift towards personalized medicine, where the goal is to move beyond a one-size-fits-all approach and deliver treatments that are finely tuned to the genetic makeup of each patient.

As the global demand for personalized medicine continues to rise, driven by an increasing prevalence of complex diseases and a growing emphasis on precision healthcare, the market for generative AI in this sector is experiencing a robust upswing. The convergence of cutting-edge technologies, such as machine learning, big data analytics, and genomics, positions generative AI as a transformative tool in the pursuit of more effective, personalized, and targeted therapies.

Key Market Challenges

Interoperability Challenges

Interoperability challenges stand as a significant hurdle in the path of the Global Generative AI in Personalized Medicine Market. As this innovative sector seeks to leverage the power of generative AI in tailoring medical treatments to individual patients, the seamless exchange of healthcare information becomes imperative. However, the healthcare ecosystem is characterized by a multitude of systems, platforms, and data formats, resulting in a fragmented landscape that inhibits effective interoperability.

One of the primary issues hindering interoperability in the Generative AI in Personalized Medicine Market is the lack of standardized formats and protocols for sharing healthcare data. Electronic health records (EHRs), laboratory results, and genomic information often reside in siloed systems that operate on different standards. This fragmentation makes it challenging to integrate these diverse datasets, hindering the efficient flow of information required for generative AI applications. The absence of standardized data formats creates a barrier to the seamless collaboration between healthcare providers, research institutions, and technology developer.

Additionally, interoperability challenges extend to the diverse range of devices and technologies used in healthcare settings. From diagnostic equipment to wearable devices that collect patient-generated data, the integration of these technologies with generative AI platforms becomes complex due to differing communication protocols and data structures. The lack of a standardized framework to enable interoperability between these devices impedes the comprehensive data exchange necessary for generative AI to reach its full potential in personalized medicine.

Data Privacy and Security Concerns

The burgeoning Global Generative AI in Personalized Medicine Market is encountering a formidable obstacle in the form of data privacy and security concerns. As the integration of generative AI technologies in healthcare becomes more widespread, the reliance on vast and sensitive datasets, including patient records and genomic information, raises ethical questions and challenges regarding the protection of personal health information.

One of the primary concerns in the Generative AI in Personalized Medicine Market revolves around the delicate nature of healthcare data. Patient information, often containing highly sensitive details about medical conditions, genetic predispositions, and treatment histories, is a prime target for cyber threats. Unauthorized access to such information not only jeopardizes individual privacy but also poses ethical challenges regarding the responsible use of patient data in the development of generative AI applications.

Ensuring patient consent and maintaining data ownership become critical aspects of navigating the intricate landscape of data privacy in the context of generative AI. As generative AI relies on extensive health-related datasets for training and analysis, establishing transparent and ethical frameworks for obtaining patient consent and clearly defining data ownership rights becomes paramount. Striking a balance between facilitating data accessibility for research purposes and safeguarding patient privacy is an ongoing challenge that the Generative AI in Personalized Medicine Market must address.

Key Market Trends

Integration of Multi-Omics Data

The integration of multi-omics data is proving to be a pivotal factor in propelling the global generative AI in personalized medicine market to new heights. As the field of genomics expands and evolves, the need to comprehensively understand the intricate interplay of various biological layers-genomics, transcriptomics, proteomics, metabolomics, and beyond-has become increasingly apparent. Multi-omics data integration involves the amalgamation of information from diverse molecular levels, providing a holistic view of the molecular landscape within an individual. This integration is particularly crucial in the context of personalized medicine, where the goal is to tailor healthcare interventions based on the unique genetic and molecular makeup of each patient.

Generative AI, with its capacity for advanced pattern recognition and complex data analysis, is ideally suited to tackle the challenges posed by multi-omics data. The sheer volume and complexity of information generated by different omics technologies require sophisticated computational approaches to extract meaningful insights. Generative AI algorithms excel in deciphering patterns and relationships within these multi-dimensional datasets, uncovering hidden correlations that can be pivotal in understanding disease mechanisms and predicting individual responses to treatments.

The synergy between multi-omics data integration and generative AI is reshaping the landscape of personalized medicine by enabling a more precise and comprehensive understanding of diseases. The identification of biomarkers across multiple omics layers allows for a nuanced characterization of diseases, facilitating the development of targeted therapies. By leveraging generative AI, researchers can discern complex molecular signatures indicative of disease subtypes, progression trajectories, and potential therapeutic targets.

The global market for generative AI in personalized medicine is witnessing a substantial upswing as the integration of multi-omics data becomes a cornerstone in advancing precision healthcare. Pharmaceutical companies, research institutions, and healthcare providers are increasingly recognizing the value of combining genomic, proteomic, and metabolomic information to tailor treatments to individual patients. This integration not only enhances diagnostic accuracy but also fuels innovation in drug discovery and development, paving the way for more effective and personalized therapeutic interventions.

Decentralized Clinical Trials

The adoption of decentralized clinical trials (DCTs) is emerging as a driving force behind the surging global market for generative AI in personalized medicine. Traditional clinical trial models often face challenges such as participant recruitment hurdles, geographical limitations, and the burden of in-person visits. Decentralized clinical trials leverage digital technologies, wearables, and remote monitoring to overcome these obstacles, allowing for greater participant diversity and inclusion while enhancing the efficiency of data collection. Generative AI, with its capacity for advanced analytics and pattern recognition, complements the decentralized approach by processing the wealth of data generated in these trials.

Generative AI facilitates the analysis of real-time, patient-generated data from wearables and other remote monitoring devices, providing continuous insights into individual health parameters. The ability to process and interpret this data in near real-time allows for a more dynamic and personalized understanding of patient responses to treatments. Machine learning algorithms can identify subtle patterns and correlations within the vast datasets, aiding in the identification of biomarkers, predicting individual responses to interventions, and optimizing personalized treatment regimens.

The decentralization of clinical trials aligns with the broader shift toward personalized medicine, as it enables the inclusion of a more diverse and representative participant pool. This diversity is crucial for capturing the variability in treatment responses based on genetic, environmental, and lifestyle factors. Generative AI serves as a linchpin in this decentralized paradigm by transforming the copious amounts of patient-generated data into actionable insights. The integration of AI-driven analytics into decentralized clinical trials not only accelerates the pace of data analysis but also enhances the overall quality and depth of information gleaned from participants.

Pharmaceutical companies, contract research organizations (CROs), and other stakeholders are increasingly recognizing the value of combining decentralized clinical trials with generative AI to streamline the drug development process and deliver more personalized healthcare solutions. The global market for generative AI in personalized medicine is witnessing a notable upswing as a result of this convergence, with innovative applications ranging from remote patient monitoring to predictive modeling of treatment outcomes.

Segmental Insights

Personalized Medicine Therapeutics Insights

Based on the Personalized Medicine Therapeutics, Pharmaceuticals emerged as the dominant segment in the global market for Global Generative AI in Personalized Medicine in 2022. Generative AI, with its ability to analyze vast and complex datasets, becomes an invaluable tool in the development and optimization of personalized pharmaceuticals. The intricate nature of biological systems, coupled with the vast variability among individuals, requires advanced computational approaches to decipher patterns and identify optimal treatment strategies. Generative AI excels in simulating molecular interactions, predicting drug responses, and optimizing treatment regimens based on individual patient data.

Deployment Model Insights

Based on the Deployment Model, Cloud Based emerged as the dominant segment in the global market for Global Generative AI in Personalized Medicine Market in 2022. The nature of generative AI applications involves the processing and analysis of vast datasets, including genomics, clinical records, and other healthcare-related information. Cloud-based deployment provides the scalability and computational power required to handle these extensive datasets efficiently. The ability to scale resources dynamically allows organizations to manage the computational demands of generative AI applications, ensuring optimal performance and responsiveness. Secondly, cloud-based deployment models offer accessibility and flexibility, crucial factors in the rapidly evolving field of personalized medicine.

Regional Insights

North America emerged as the dominant player in the Global Generative AI in Personalized Medicine Market in 2022, holding the largest market share. North America houses several prominent technology companies that play a pivotal role in shaping the landscape of artificial intelligence, including generative AI. The collaboration between healthcare institutions and tech companies in the region fosters the development and implementation of advanced AI solutions tailored for personalized medicine applications. North America consistently allocates a significant portion of its GDP to healthcare. The high healthcare expenditure indicates a willingness to invest in state-of-the-art technologies that can improve patient outcomes, enhance diagnostic accuracy, and advance personalized medicine. Generative AI, with its potential to revolutionize healthcare practices, aligns with the region's commitment to delivering high-quality patient care...

Key Market Players

Syntegra

NioyaTech

Saxon

IBM Watson

Microsoft Corporation

Google LLC

Tencent Holdings Ltd.

Neuralink Corporation

Johnson & Johnson

OpenAI

Oracle

Report Scope:

In this report, the Global Generative AI in Personalized Medicine Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Global Generative AI in Personalized Medicine Market, By Personalized Medicine Therapeutics:

  • Pharmaceutical
  • Genomic Medicine
  • Devices

Global Generative AI in Personalized Medicine Market, By Deployment Model:

  • On-premises
  • Cloud Based

Global Generative AI in Personalized Medicine Market, By End-User:

  • Hospitals and Clinics
  • Ambulatory Surgical Centers
  • Others

Global Generative AI in Personalized Medicine Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
  • Asia-Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Kuwait
  • Turkey
  • Egypt

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Generative AI in Personalized Medicine Market.

Available Customizations:

  • Global Generative AI in Personalized Medicine Market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Global Generative AI in Personalized Medicine Market Outlook

  • 4.1. Market Size & Forecast
    • 4.1.1. By Value
  • 4.2. Market Share & Forecast
    • 4.2.1. By Personalized Medicine Therapeutics (Pharmaceutical, Genomic Medicine, Devices)
    • 4.2.2. By Deployment Model (On-premises, Cloud Based)
    • 4.2.3. By End-User (Hospitals and Clinics, Ambulatory Surgical Centers, Others)
    • 4.2.4. By Region
    • 4.2.5. By Company (2022)
  • 4.3. Market Map
    • 4.3.1. By Personalized Medicine Therapeutics
    • 4.3.2. By Deployment Model
    • 4.3.3. By End-User
    • 4.3.4. By Region

5. Asia Pacific Generative AI in Personalized Medicine Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Personalized Medicine Therapeutics
    • 5.2.2. By Deployment Model
    • 5.2.3. By End-User
    • 5.2.4. By Country
  • 5.3. Asia Pacific: Country Analysis
    • 5.3.1. China Generative AI in Personalized Medicine Market Outlook
      • 5.3.1.1. Market Size & Forecast
        • 5.3.1.1.1. By Value
      • 5.3.1.2. Market Share & Forecast
        • 5.3.1.2.1. By Personalized Medicine Therapeutics
        • 5.3.1.2.2. By Deployment Model
        • 5.3.1.2.3. By End-User
    • 5.3.2. India Generative AI in Personalized Medicine Market Outlook
      • 5.3.2.1. Market Size & Forecast
        • 5.3.2.1.1. By Value
      • 5.3.2.2. Market Share & Forecast
        • 5.3.2.2.1. By Personalized Medicine Therapeutics
        • 5.3.2.2.2. By Deployment Model
        • 5.3.2.2.3. By End-User
    • 5.3.3. Australia Generative AI in Personalized Medicine Market Outlook
      • 5.3.3.1. Market Size & Forecast
        • 5.3.3.1.1. By Value
      • 5.3.3.2. Market Share & Forecast
        • 5.3.3.2.1. By Personalized Medicine Therapeutics
        • 5.3.3.2.2. By Deployment Model
        • 5.3.3.2.3. By End-User
    • 5.3.4. Japan Generative AI in Personalized Medicine Market Outlook
      • 5.3.4.1. Market Size & Forecast
        • 5.3.4.1.1. By Value
      • 5.3.4.2. Market Share & Forecast
        • 5.3.4.2.1. By Personalized Medicine Therapeutics
        • 5.3.4.2.2. By Deployment Model
        • 5.3.4.2.3. By End-User
    • 5.3.5. South Korea Generative AI in Personalized Medicine Market Outlook
      • 5.3.5.1. Market Size & Forecast
        • 5.3.5.1.1. By Value
      • 5.3.5.2. Market Share & Forecast
        • 5.3.5.2.1. By Personalized Medicine Therapeutics
        • 5.3.5.2.2. By Deployment Model
        • 5.3.5.2.3. By End-User

6. Europe Generative AI in Personalized Medicine Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Personalized Medicine Therapeutics
    • 6.2.2. By Deployment Model
    • 6.2.3. By End-User
    • 6.2.4. By Country
  • 6.3. Europe: Country Analysis
    • 6.3.1. France Generative AI in Personalized Medicine Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Personalized Medicine Therapeutics
        • 6.3.1.2.2. By Deployment Model
        • 6.3.1.2.3. By End-User
    • 6.3.2. Germany Generative AI in Personalized Medicine Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Personalized Medicine Therapeutics
        • 6.3.2.2.2. By Deployment Model
        • 6.3.2.2.3. By End-User
    • 6.3.3. Spain Generative AI in Personalized Medicine Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Personalized Medicine Therapeutics
        • 6.3.3.2.2. By Deployment Model
        • 6.3.3.2.3. By End-User
    • 6.3.4. Italy Generative AI in Personalized Medicine Market Outlook
      • 6.3.4.1. Market Size & Forecast
        • 6.3.4.1.1. By Value
      • 6.3.4.2. Market Share & Forecast
        • 6.3.4.2.1. By Personalized Medicine Therapeutics
        • 6.3.4.2.2. By Deployment Model
        • 6.3.4.2.3. By End-User
    • 6.3.5. United Kingdom Generative AI in Personalized Medicine Market Outlook
      • 6.3.5.1. Market Size & Forecast
        • 6.3.5.1.1. By Value
      • 6.3.5.2. Market Share & Forecast
        • 6.3.5.2.1. By Personalized Medicine Therapeutics
        • 6.3.5.2.2. By Deployment Model
        • 6.3.5.2.3. By End-User

7. North America Generative AI in Personalized Medicine Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Personalized Medicine Therapeutics
    • 7.2.2. By Deployment Model
    • 7.2.3. By End-User
    • 7.2.4. By Country
  • 7.3. North America: Country Analysis
    • 7.3.1. United States Generative AI in Personalized Medicine Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Personalized Medicine Therapeutics
        • 7.3.1.2.2. By Deployment Model
        • 7.3.1.2.3. By End-User
    • 7.3.2. Mexico Generative AI in Personalized Medicine Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Personalized Medicine Therapeutics
        • 7.3.2.2.2. By Deployment Model
        • 7.3.2.2.3. By End-User
    • 7.3.3. Canada Generative AI in Personalized Medicine Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Personalized Medicine Therapeutics
        • 7.3.3.2.2. By Deployment Model
        • 7.3.3.2.3. By End-User

8. South America Generative AI in Personalized Medicine Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Personalized Medicine Therapeutics
    • 8.2.2. By Deployment Model
    • 8.2.3. By End-User
    • 8.2.4. By Country
  • 8.3. South America: Country Analysis
    • 8.3.1. Brazil Generative AI in Personalized Medicine Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Personalized Medicine Therapeutics
        • 8.3.1.2.2. By Deployment Model
        • 8.3.1.2.3. By End-User
    • 8.3.2. Argentina Generative AI in Personalized Medicine Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Personalized Medicine Therapeutics
        • 8.3.2.2.2. By Deployment Model
        • 8.3.2.2.3. By End-User
    • 8.3.3. Colombia Generative AI in Personalized Medicine Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Personalized Medicine Therapeutics
        • 8.3.3.2.2. By Deployment Model
        • 8.3.3.2.3. By End-User

9. Middle East and Africa Generative AI in Personalized Medicine Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Personalized Medicine Therapeutics
    • 9.2.2. By Deployment Model
    • 9.2.3. By End-User
    • 9.2.4. By Country
  • 9.3. MEA: Country Analysis
    • 9.3.1. South Africa Generative AI in Personalized Medicine Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Personalized Medicine Therapeutics
        • 9.3.1.2.2. By Deployment Model
        • 9.3.1.2.3. By End-User
    • 9.3.2. Saudi Arabia Generative AI in Personalized Medicine Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Personalized Medicine Therapeutics
        • 9.3.2.2.2. By Deployment Model
        • 9.3.2.2.3. By End-User
    • 9.3.3. UAE Generative AI in Personalized Medicine Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Personalized Medicine Therapeutics
        • 9.3.3.2.2. By Deployment Model
        • 9.3.3.2.3. By End-User
    • 9.3.4. Egypt Generative AI in Personalized Medicine Market Outlook
      • 9.3.4.1. Market Size & Forecast
        • 9.3.4.1.1. By Value
      • 9.3.4.2. Market Share & Forecast
        • 9.3.4.2.1. By Personalized Medicine Therapeutics
        • 9.3.4.2.2. By Deployment Model
        • 9.3.4.2.3. By End-User

10. Market Dynamics

  • 10.1. Drivers
  • 10.2. Challenges

11. Market Trends & Developments

  • 11.1. Recent Developments
  • 11.2. Product Launches
  • 11.3. Mergers & Acquisitions

12. Global Generative AI in Personalized Medicine Market: SWOT Analysis

13. Porter's Five Forces Analysis

  • 13.1. Competition in the Industry
  • 13.2. Potential of New Entrants
  • 13.3. Power of Suppliers
  • 13.4. Power of Customers
  • 13.5. Threat of Substitute Product
  • 13.6.

14. Competitive Landscape

  • 14.1. Johnson & Johnson
    • 14.1.1. Business Overview
    • 14.1.2. Company Snapshot
    • 14.1.3. Products & Services
    • 14.1.4. Current Capacity Analysis
    • 14.1.5. Financials (In case of listed)
    • 14.1.6. Recent Developments
    • 14.1.7. SWOT Analysis
  • 14.2. Butterfly Network
  • 14.3. Deep Genomics
  • 14.4. Google LLC
  • 14.5. IBM Watson Health
  • 14.6. Microsoft Corporation
  • 14.7. Aidoc
  • 14.8. Insilico Medicine
  • 14.9. PathAI
  • 14.10. Tencent Holdings Ltd.

15. Strategic Recommendations

16. About Us & Disclaimer