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智慧体外诊断市场：人工智慧在体外诊断中的应用—按应用、技术、产品和使用者划分—高阶主管和顾问指南（2026-2030 年）
市场调查报告书
商品编码
2025437

智慧体外诊断市场：人工智慧在体外诊断中的应用—按应用、技术、产品和使用者划分—高阶主管和顾问指南（2026-2030 年）

Smart In Vitro Diagnostics. Artificial Intelligence for IVD Markets By Application, By Technology, By Product and By User. With Executive and Consultant Guides 2026-2030
出版日期: | 出版商: Howe Sound Research | 英文 417 Pages | 商品交期: 最快1-2个工作天内

价格

报告摘要：

未来20年，人工智慧（AI）将推动体外诊断（IVD）市场的成长。随着医生利用一切可用资讯对抗疾病，该市场正在迅速扩张。同时，製药公司看到了开发几乎任何治疗方法的潜力。探索这种全新的诊断方法将如何彻底改变医疗保健产业。

人工智慧 (AI) 正在迅速改变体外诊断 (IVD) 产业，它能够提高诊断准确性、加快数据解读速度并实现更个人化的医疗决策。包括机器学习、深度学习和高级数据分析在内的人工智慧技术正日益融入临床化学、分子诊断、病理学、基因组学、免疫检测、微生物学和就地检验(POCT) 等各个诊断流程。人工智慧与先进诊断技术的融合为提高疾病检测准确性、简化工作流程和优化医疗成本创造了新的机会。

随着医疗服务提供者、诊断公司和生命科学机构利用数据驱动的洞察来提升诊断效能，全球人工智慧驱动的体外诊断市场正在迅速扩张。这一增长的驱动因素包括诊断数据日益复杂化、精准医疗的扩展、数位病理学的应用以及基因组和分子检测技术的日益普及。人工智慧能够更有效地利用诊断讯息，提升临床检测的临床价值，并支持疾病的早期发现。

这份市场调查报告全面分析了人工智慧技术如何变革体外诊断（IVD）在多个诊断领域的格局。报告检验了技术趋势、市场驱动因素、竞争格局、监管考虑以及开发人工智慧诊断解决方案的企业所面临的策略机会。

人工智慧在诊断创新中的作用

诊断技术会产生庞大而复杂的资料集，需要精细的解读才能获得具有临床意义的见解。人工智慧能够自动分析高维度诊断数据，从而识别出传统分析方法难以发现的模式。

人工智慧演算法正日益广泛地应用于辅助解读医学影像、基因组序列、生物标记组合以及多种诊断结果。机器学习模型整合了包括检测结果、临床资讯、影像资料和基因组图谱在内的多种资料类型，从而有助于提高疾病分类和风险评估的准确性。

人工智慧辅助诊断可望助力疾病早期发现、提升患者分层水准并优化治疗方法选择。这些能力在肿瘤学、感染疾病管理、心血管疾病风险评估以及罕见疾病诊断等领域尤其重要。

在医疗保健系统中，人工智慧工具的采用正在迅速增加，以提高诊断效率并减少临床解释的差异。

此外，人工智慧技术能够从大规模资料集中不断学习，从而不断提高诊断效能。

主要应用领域

人工智慧正在体外诊断市场的多个细分领域中得到应用。

  • 数位病理学是人工智慧应用方面最先进的领域之一。机器学习演算法可以分析组织病理学影像，以识别癌症生物标记并对组织形态进行分类。
  • 在分子诊断和基因组学领域，我们正在利用人工智慧分析复杂的基因资料集，并识别具有临床意义的突变。
  • 在微生物检查室中，人工智慧工具被用于识别病原体和检测抗生素抗药性模式。
  • 在临床化学应用领域，预测分析用于识别患者检测数据中的趋势。
  • 在免疫检测检测中，正在利用人工智慧辅助分析多个生物标记组合。
  • 流式细胞技术和细胞分析技术利用人工智慧对细胞群体进行分类，并识别罕见的细胞表型。
  • 照护现场利用人工智慧驱动的分析工具，支援分散式医疗服务。
  • 此外，人工智慧技术还有潜力优化检查室工作流程并支援品管流程。

市场驱动因素

多种因素正在推动人工智慧驱动的体外诊断（IVD）市场成长。

  • 随着诊断资料量和复杂性的增加，对先进分析工具的需求也在增长。
  • 扩大精准医疗计画需要整合基因组数据和生物标记数据。
  • 某些诊断领域专业临床知识的匮乏，促使人们对自动化决策支援工具越来越感兴趣。
  • 医疗机构正在寻求能够提高效率并减少诊断差异的技术。
  • 云端运算和资料储存技术的进步使得管理大规模诊断资料集成为可能。
  • 数位医疗技术的发展正在推动人工智慧诊断工具的整合。
  • 监管机构正在不断改善相关框架，以支援人工智慧在医疗领域的应用。
  • 对医疗数据分析的投资正在推动诊断技术的创新。
  • 电子健康记录的日益普及使得将人工智慧工具整合到临床工作流程中成为可能。

市场区隔

IVD 市场的人工智慧可以按诊断应用、技术类型、最终用户和地区进行细分。

  • 按诊断应用划分，主要领域包括数位病理学、分子诊断、临床化学、微生物学、免疫检测和流式细胞技术。
  • 从技术类型来看，机器学习、深度学习、自然语言处理和电脑视觉技术是主要类别。
  • 最终用户包括临床检查室、医院、研究机构、製药公司和诊断药物开发公司。
  • 由于北美拥有强大的数位医疗基础设施和对医疗技术创新的大量投资，它已成为一个主要市场。
  • 在促进数位医疗解决方案普及的监管措施的支持下，欧洲已成为一个重要的市场。
  • 由于对医疗保健技术的投资增加以及诊断测试数量的上升，亚太市场正在扩张。
  • 新兴市场为人工智慧驱动的诊断解决方案提供了潜在机会，这些解决方案可以改善人们获得医疗专业知识的机会。

本报告包含18个国家和4个地区的详细数据。购买本报告的客户将获得全球任何国家的详细数据。

竞争格局

人工智慧驱动的体外诊断（IVD）市场包括诊断公司、软体开发公司、数据分析公司和数位健康公司。

竞争环境取决于演算法效能、资料品质、监管核准情况以及与检验资讯系统的整合能力。

诊断公司与人工智慧开发商之间的策略合作十分常见。

各公司正投资开发整合诊断平台，将检测设备与人工智慧数据分析软体结合。

取得资料和训练资料集是至关重要的竞争优势。

与医疗资讯科技系统的整合将影响临床检查室的采用。

与机器学习演算法相关的智慧财产权会影响市场定位。

各公司正在投资监管合规策略，以支援人工智慧诊断工具的商业化。

未来展望

人工智慧有望在塑造体外诊断的未来中发挥越来越重要的作用。

  • 机器学习演算法的进步可以提高诊断准确率，并实现疾病的早期检测。
  • 多体学资料集的整合有可能加速开发更个人化的诊断方法。
  • 检查室工作流程自动化有可能提高营运效率。
  • 人工智慧驱动的决策支援工具能够提高对复杂诊断结果的临床解读。
  • 数位病理学和基因组检测的扩展预计将增加对人工智慧驱动分析的需求。
  • 加强诊断设备製造商和数位医疗开发公司之间的合作可以加速创新。

总体而言，人工智慧正在变革体外诊断业界。数据分析技术的持续进步和医疗保健的数位化有望支撑市场持续成长，并为诊断领域的创新创造新的机会。

目录

第一章 市集指南

  • 战略情势分析
  • 企业主管、行销负责人和业务拓展负责人。
  • 管理顾问和投资顾问指南

第二章：引言与市场定义

  • 什么是智能诊断？
  • 市场定义
  • 调查方法
  • 展望：医疗保健和体外诊断行业

第三章 市场概览

  • 参与企业充满活力的市场
    • 学术研究办公室
    • 诊断测试开发人员
    • 测量仪器供应商
    • 化学品/试剂供应商
    • 病理检测用品供应商
    • 独立临床实验室
    • 国家/地区公共研究机构
    • 医院检查室
    • 医师诊所检查室(POLS)
    • 审计机构
    • 认证机构
  • 理解人工智慧
    • 人工智慧
    • 机器学习
    • 深度学习
    • 卷积类神经网路
    • 生成对抗网络
    • 限制
  • 人工智慧在体外诊断的应用
    • 感染疾病
    • 肿瘤学
    • 解剖病理学
    • 心臟病学
    • 糖尿病
    • 普通内科

第四章 市场趋势

  • 成长驱动因素
  • 成长阻碍因素
  • 测量仪器、自动化和诊断技术的发展趋势

第五章 近期趋势

第六章：主要企业概况

  • Adaptive Biotechnologies
  • Aidoc
  • Anumana
  • ARUP Laboratories
  • Atomwise
  • Bayesian Health
  • Behold.ai
  • BGI Genomics Co. Ltd
  • bioMerieux Diagnostics
  • Bio-Rad Laboratories, Inc
  • Cambridge Cognition
  • Cardiologs（Phillips）
  • CareDx
  • Caris Molecular Diagnostics
  • Cleerly
  • ClosedLoop AI
  • CloudMedX Health
  • Deepcell
  • Digital Diagnostics
  • EKF Diagnostics Holdings
  • Freenome
  • GE Healthcare
  • Glooko
  • Idoven
  • Illumina
  • Infohealth
  • Jade
  • K Health
  • Lunit
  • Luventix
  • MaxCyte
  • Mayo Clinic Laboratories
  • Medtronic
  • Merative
  • Nanox
  • NIOX Group
  • Niramai Health Analytix
  • NVIDIA
  • Oncohost
  • OraLiva
  • Owkin
  • Oxford Nanopore Technologies
  • Pacific Biosciences
  • Paige.AI
  • PathAI
  • Perthera
  • Philips Healthcare
  • Prognos
  • Qiagen
  • Qure.ai
  • Renalytix
  • Seegene
  • Siemens Healthineers
  • Sophia Genetics
  • Sysmex
  • Viz.ai

第七章：全球智慧诊断市场

  • 全球市场概览（按国家/地区划分）
  • 全球市场概览（按应用领域划分）
  • 全球市场概览（依技术划分）
  • 区域全球市场概览
  • 全球市场概览（按产品类别划分）

第八章 世界市场（按应用领域划分）

  • 癌症
  • 传染病检查
  • 代谢测试
  • 心臟检查
  • 糖尿病检测
  • 其他的

第九章 世界市场（依技术划分）

  • NGS技术
  • PCR技术
  • 化学/IA技术
  • 病理技术
  • 其他的

第十章：全球市场（依地域划分）

  • 研究
  • 药物研究
  • 临床
  • 其他的

第十一章 世界市场（依产品划分）

  • 装置
  • 分析
  • 软体
  • 服务
  • 其他的

第十二章附录

Product Code: TECHSMARTIVD 426

Report Overview:

Artificial Intelligence will drive IVD market growth over the next 20 years. The market is exploding as physicians use all the information they can get to battle disease. While Pharmaceutical Companies see the potential to make nearly any therapy viable. Find out how this new approach to diagnostics will change medical care forever.

Artificial intelligence (AI) is rapidly transforming the In Vitro Diagnostics (IVD) industry by improving diagnostic accuracy, accelerating data interpretation, and enabling more personalized healthcare decision-making. AI technologies, including machine learning, deep learning, and advanced data analytics, are increasingly integrated into diagnostic workflows across clinical chemistry, molecular diagnostics, pathology, genomics, immunoassays, microbiology, and point-of-care testing. The convergence of AI with advanced diagnostic technologies is creating new opportunities for improved disease detection, workflow efficiency, and healthcare cost optimization.

The global market for AI-enabled in vitro diagnostics is expanding quickly as healthcare providers, diagnostic companies, and life sciences organizations seek to leverage data-driven insights to enhance diagnostic performance. Growth is driven by increasing complexity of diagnostic data, expansion of precision medicine, adoption of digital pathology, and increasing use of genomic and molecular testing technologies. Artificial intelligence is enabling more effective utilization of diagnostic information, improving the clinical value of laboratory testing, and supporting earlier detection of disease.

This market research report provides comprehensive analysis of how AI technologies are reshaping the IVD landscape across multiple diagnostic segments. The report examines technology trends, market drivers, competitive dynamics, regulatory considerations, and strategic opportunities for companies developing AI-enabled diagnostic solutions.

Role of Artificial Intelligence in Diagnostic Innovation

Diagnostic technologies generate large and complex datasets that require sophisticated interpretation to produce clinically meaningful insights. Artificial intelligence enables automated analysis of high-dimensional diagnostic data, allowing identification of patterns that may not be easily detectable using conventional analytical methods.

AI algorithms are increasingly used to support interpretation of medical images, genomic sequences, biomarker panels, and multiplex diagnostic results. Machine learning models can integrate diverse data types, including laboratory results, clinical information, imaging data, and genomic profiles, to improve disease classification and risk assessment.

AI-enabled diagnostics may support earlier detection of disease, improved patient stratification, and more targeted treatment selection. These capabilities are particularly important in oncology, infectious disease management, cardiovascular disease risk assessment, and rare disease diagnosis.

Healthcare systems are increasingly adopting AI tools to improve diagnostic efficiency and reduce variability in clinical interpretation.

AI technologies also support continuous learning from large datasets, enabling ongoing improvement in diagnostic performance.

Key Application Areas

Artificial intelligence is being applied across multiple IVD market segments.

  • Digital pathology represents one of the most advanced areas of AI adoption. Machine learning algorithms can analyze histopathology images to identify cancer biomarkers and classify tissue morphology.
  • Molecular diagnostics and genomics applications use AI to interpret complex genetic datasets and identify clinically relevant variants.
  • Microbiology laboratories use AI tools to identify pathogens and detect antimicrobial resistance patterns.
  • Clinical chemistry applications use predictive analytics to identify trends in patient laboratory data.
  • Immunoassay testing benefits from AI-assisted interpretation of multiplex biomarker panels.
  • Flow cytometry and cell analysis technologies use AI to classify cellular populations and identify rare cell phenotypes.
  • Point-of-care diagnostics benefit from AI-enabled interpretation tools that support decentralized healthcare delivery.
  • AI technologies may also support laboratory workflow optimization and quality control processes.

Market Drivers

Several factors are driving growth in the AI-enabled IVD market.

  • Increasing volume and complexity of diagnostic data is creating demand for advanced analytical tools.
  • Expansion of precision medicine initiatives requires integration of genomic and biomarker data.
  • Shortage of specialized clinical expertise in some diagnostic disciplines is increasing interest in automated decision support tools.
  • Healthcare providers are seeking technologies that improve efficiency and reduce diagnostic variability.
  • Advances in cloud computing and data storage technologies enable management of large diagnostic datasets.
  • Growth in digital health technologies supports integration of AI-enabled diagnostic tools.
  • Regulatory agencies are increasingly developing frameworks supporting use of AI in healthcare applications.
  • Investment in healthcare data analytics is supporting innovation in diagnostic technologies.
  • Increasing adoption of electronic health records enables integration of AI tools into clinical workflows.

Market Segmentation

The Artificial Intelligence in IVD market can be segmented by diagnostic application, technology type, end user, and geographic region.

  • By diagnostic application, digital pathology, molecular diagnostics, clinical chemistry, microbiology, immunoassay testing, and flow cytometry represent key segments.
  • By technology type, machine learning, deep learning, natural language processing, and computer vision technologies represent major categories.
  • End users include clinical laboratories, hospitals, research institutions, pharmaceutical companies, and diagnostic developers.
  • North America represents a major market due to strong digital health infrastructure and investment in healthcare technology innovation.
  • Europe represents a significant market supported by regulatory initiatives encouraging adoption of digital health solutions.
  • Asia-Pacific markets are expanding due to increasing investment in healthcare technology and growing diagnostic testing volumes.
  • Emerging markets represent potential opportunities for AI-enabled diagnostic solutions that improve access to healthcare expertise.

The report includes detailed breakouts for 18 Countries and 4 Regions. A detailed breakout for any country in the world is available to purchasers of the report.

Competitive Landscape

The AI-enabled IVD market includes diagnostic companies, software developers, data analytics firms, and digital health companies.

Competition is influenced by algorithm performance, data quality, regulatory approval status, and integration capabilities with laboratory information systems.

Strategic partnerships between diagnostic companies and artificial intelligence developers are common.

Companies are investing in development of integrated diagnostic platforms combining laboratory instrumentation with AI-enabled data interpretation software.

Data access and training datasets represent important competitive advantages.

Integration with healthcare IT systems influences adoption by clinical laboratories.

Intellectual property related to machine learning algorithms may influence market positioning.

Companies are investing in regulatory compliance strategies to support commercialization of AI-enabled diagnostic tools.

Future Outlook

Artificial intelligence is expected to play an increasingly important role in shaping the future of in vitro diagnostics.

  • Advances in machine learning algorithms may improve diagnostic accuracy and enable earlier disease detection.
  • Integration of multi-omics datasets may support development of more personalized diagnostic approaches.
  • Automation of laboratory workflows may improve operational efficiency.
  • AI-enabled decision support tools may improve clinical interpretation of complex diagnostic results.
  • Expansion of digital pathology and genomic testing is expected to increase demand for AI-based analytics.
  • Increasing collaboration between diagnostic companies and digital health developers may accelerate innovation.

Overall, artificial intelligence represents a transformative force within the in vitro diagnostics industry. Continued advances in data analytics technologies and healthcare digitalization are expected to support sustained market growth and create new opportunities for diagnostic innovation.

Table of Contents

1 Market Guides

  • 1.1 Strategic Situation Analysis
  • 1.2 Guide for Executives, Marketing, and Business Development Staff
  • 1.3 Guide for Management Consultants and Investment Advisors

2 Introduction and Market Definition

  • 2.1 What are Smart Diagnostics?
  • 2.2 Market Definition
    • 2.2.1 Revenue Market Size
  • 2.3 Methodology
    • 2.3.1 Methodology
    • 2.3.2 Sources
    • 2.3.3 Authors
  • 2.4 Perspective: Healthcare and the IVD Industry
    • 2.4.1 Global Healthcare Spending
    • 2.4.2 Spending on Diagnostics
    • 2.4.3 Important Role of Insurance for Diagnostics

3 Market Overview

  • 3.1 Players in a Dynamic Market
    • 3.1.1 Academic Research Lab
    • 3.1.2 Diagnostic Test Developer
    • 3.1.3 Instrumentation Supplier
    • 3.1.4 Chemical/Reagent Supplier
    • 3.1.5 Pathology Supplier
    • 3.1.6 Independent Clinical Laboratory
    • 3.1.7 Public National/regional Laboratory
    • 3.1.8 Hospital Laboratory
    • 3.1.9 Physicians Office Lab (POLS)
    • 3.1.10 Audit Body
    • 3.1.11 Certification Body
  • 3.2 Understanding Artificial Intelligence
    • 3.2.1 Artificial intelligence
    • 3.2.2 Machine learning
    • 3.2.3 Deep learning
    • 3.2.4 Convolutional neural networks
    • 3.2.5 Generative adversarial networks
    • 3.2.6 Limitations
  • 3.3 AI Applications in IVD
    • 3.3.1 Infectious Disease
      • 3.3.1.1 Known vs. Unknown
      • 3.3.1.2 TMI
      • 3.3.1.3 Disease surveillance
      • 3.3.1.4 Outbreak detection
      • 3.3.1.5 Contact tracing
      • 3.3.1.6 Forecasting
      • 3.3.1.7 Drug discovery
      • 3.3.1.8 Resource allocation
    • 3.3.2 Oncology
      • 3.3.2.1 Electronic health records
      • 3.3.2.2 Genomic analysis
      • 3.3.2.3 Treatment planning
      • 3.3.2.4 Clinical trial matching
    • 3.3.3 Anatomic Pathology
      • 3.3.3.1 Image analysis
      • 3.3.3.2 Tumor segmentation
      • 3.3.3.3 Disease classification
      • 3.3.3.4 Predictive modeling
      • 3.3.3.5 Quality control
      • 3.3.3.6 Digital pathology
    • 3.3.4 Cardiology
      • 3.3.4.1 Electrocardiogram analysis
      • 3.3.4.2 Electronic health records
      • 3.3.4.3 Genomic analysis
      • 3.3.4.4 Treatment planning
      • 3.3.4.5 Prediction of outcomes
    • 3.3.5 Diabetes
      • 3.3.5.1 Diagnosis
      • 3.3.5.2 Blood glucose monitoring
      • 3.3.5.3 Personalized treatment plans
      • 3.3.5.4 Medication management
      • 3.3.5.5 Diabetes education
      • 3.3.5.6 Predictive analytics
    • 3.3.6 General Medicine
      • 3.3.6.1 Diagnosis
      • 3.3.6.2 Predictive Analytics
      • 3.3.6.3 Personalized Treatment Plans
      • 3.3.6.4 Medication Management
      • 3.3.6.5 Disease Monitoring
      • 3.3.6.6 Telemedicine

4 Market Trends

  • 4.1 Factors Driving Growth
    • 4.1.1 Outcome Improvement
    • 4.1.2 The Aging Effect
    • 4.1.3 Cost Containment
    • 4.1.4 Physician Impact
    • 4.1.5 Cost of Intelligence
  • 4.2 Factors Limiting Growth
    • 4.2.1 State of knowledge
    • 4.2.2 Genetic Blizzard
    • 4.2.3 Protocol Resistance
    • 4.2.4 Regulation and coverage
  • 4.3 Instrumentation, Automation and Diagnostic Trends
    • 4.3.1 Traditional Automation and Centralization
    • 4.3.2 The New Automation, Decentralization and Point Of Care
    • 4.3.3 Instruments Key to Market Share
    • 4.3.4 Bioinformatics Plays a Role
    • 4.3.5 PCR Takes Command
    • 4.3.6 Next Generation Sequencing Fuels a Revolution
    • 4.3.7 NGS Impact on Pricing
    • 4.3.8 Whole Genome Sequencing, A Brave New World
    • 4.3.9 Companion Diagnostics Blurs Diagnosis and Treatment
    • 4.3.10 Shifting Role of Diagnostics

5 Recent Developments

  • 5.1 Recent Developments – Importance and How to Use This Section
    • 5.1.1 Importance of These Developments
    • 5.1.2 How to Use This Section
  • 5.2 Ataraxis AI Nabs Financing
  • 5.3 Myriad Genetics Licenses Image Analysis Technology
  • 5.4 Danaher, AI Firm I Form Investment Partnership
  • 5.5 Cardio Dx AI-Based Tests Receive Final CMS Pricing
  • 5.6 Ataraxis AI Launches AI Cancer Dx
  • 5.7 Tempus Immuno-Oncology Portfolio AI-enabled
  • 5.8 AI enables precision diagnosis of cervical cancer
  • 5.9 UK to Rollout Digital Pathology Across NHS
  • 5.10 AI Based Next-Generation Colorectal Cancer Test
  • 5.11 Evident, Corista, Sakura Finetek, Visiopharm Form Digital Pathology Alliance
  • 5.12 Viome Life Sciences Raises $86.5M in Oversubscribed Series C Round
  • 5.13 Becton Dickinson Gets Clearance for AI-Based Bacterial Imaging
  • 5.14 Paige, Leica Biosystems Expand Digital Pathology Partnership
  • 5.15 Clarapath Acquires Digital Pathology Company Crosscope
  • 5.16 CanSense to Develop Colorectal Cancer Test
  • 5.17 Owkin-led Machine Learning Study IDs Cancer Treatment Biomarkers
  • 5.18 Guardant Health to Integrate Lunit's AI PD-L1 Algorithm
  • 5.19 Vesale Bioscience to Develop AI Phage Therapy Diagnostic Platform
  • 5.20 Caris Life Sciences To Use AI and Machine Learning
  • 5.21 Numares Health To Develop AI for “Metabolite Constellations”
  • 5.22 Sepsis Testing Startup DeepUll to Use AI for Medical Decisions
  • 5.23 Viome Life Sciences Raises $67M in Series C Financing For AI Cancer Dx
  • 5.24 ADM Diagnostics Wins Grant for Brain Injury Test Development
  • 5.25 Paige to Develop New AI-based Pathology Test
  • 5.26 Aiforia Gains CE-IVD Mark for AI-Powered Histopathology
  • 5.27 Genetic Profiling May Identify Patients Who Do Not Need Radiation Therapy
  • 5.28 Thermo Fisher Introduces Homologous Score for Cancer Profiling
  • 5.29 Genomic Test IDs Cancer Cells Early

6 Profiles of Key Players

  • 6.1 Adaptive Biotechnologies
  • 6.2 Aidoc
  • 6.3 Anumana
  • 6.4 ARUP Laboratories
  • 6.5 Atomwise
  • 6.6 Bayesian Health
  • 6.7 Behold.ai
  • 6.8 BGI Genomics Co. Ltd
  • 6.9 bioMerieux Diagnostics
  • 6.10 Bio-Rad Laboratories, Inc
  • 6.11 Cambridge Cognition
  • 6.12 Cardiologs (Phillips)
  • 6.13 CareDx
  • 6.14 Caris Molecular Diagnostics
  • 6.15 Cleerly
  • 6.16 ClosedLoop AI
  • 6.17 CloudMedX Health
  • 6.18 Deepcell
  • 6.19 Digital Diagnostics
  • 6.20 EKF Diagnostics Holdings
  • 6.21 Freenome
  • 6.22 GE Healthcare
  • 6.23 Glooko
  • 6.24 Idoven
  • 6.25 Illumina
  • 6.26 Infohealth
  • 6.27 Jade
  • 6.28 K Health
  • 6.29 Lunit
  • 6.30 Luventix
  • 6.31 MaxCyte
  • 6.32 Mayo Clinic Laboratories
  • 6.33 Medtronic
  • 6.34 Merative
  • 6.35 Nanox
  • 6.36 NIOX Group
  • 6.37 Niramai Health Analytix
  • 6.38 NVIDIA
  • 6.39 Oncohost
  • 6.40 OraLiva
  • 6.41 Owkin
  • 6.42 Oxford Nanopore Technologies
  • 6.43 Pacific Biosciences
  • 6.44 Paige.AI
  • 6.45 PathAI
  • 6.46 Perthera
  • 6.47 Philips Healthcare
  • 6.48 Prognos
  • 6.49 Qiagen
  • 6.50 Qure.ai
  • 6.51 Renalytix
  • 6.52 Seegene
  • 6.53 Siemens Healthineers
  • 6.54 Sophia Genetics
  • 6.55 Sysmex
  • 6.56 Viz.ai

7 The Global Market for Smart Diagnostics

  • 7.1 Global Market Overview by Country
    • 7.1.1 Table – Global Market by Country
    • 7.1.2 Chart - Global Market by Country
  • 7.2 Global Market by Application - Overview
    • 7.2.1 Table – Global Market by Application
    • 7.2.2 Chart – Global Market by Application – Base/Final Year Comparison
    • 7.2.3 Chart – Global Market by Application – Base Year
    • 7.2.4 Chart – Global Market by Application – Final Year
    • 7.2.5 Chart – Global Market by Application – Share by Year
    • 7.2.6 Chart – Global Market by Application – Segment Growth
  • 7.3 Global Market by Technology - Overview
    • 7.3.1 Table – Global Market by Technology
    • 7.3.2 Chart – Global Market by Technology – Base/Final Year Comparison
    • 7.3.3 Chart – Global Market by Technology – Base Year
    • 7.3.4 Chart – Global Market by Technology – Final Year
    • 7.3.5 Chart – Global Market by Technology – Share by Year
    • 7.3.6 Chart – Global Market by Technology – Segment Growth
  • 7.4 Global Market by Place - Overview
    • 7.4.1 Table – Global Market by Place
    • 7.4.2 Chart – Global Market by Place – Base/Final Year Comparison
    • 7.4.3 Chart – Global Market by Place – Base Year
    • 7.4.4 Chart – Global Market by Place – Final Year
    • 7.4.5 Chart – Global Market by Place – Share by Year
    • 7.4.6 Chart – Global Market by Place – Segment Growth
  • 7.5 Global Market by Product - Overview
    • 7.5.1 Table – Global Market by Product
    • 7.5.2 Chart – Global Market by Product – Base/Final Year Comparison
    • 7.5.3 Chart – Global Market by Product – Base Year
    • 7.5.4 Chart – Global Market by Product – Final Year
    • 7.5.5 Chart – Global Market by Product – Share by Year
    • 7.5.6 Chart – Global Market by Product – Segment Growth

8 Global Markets – By Application

  • 8.1 Cancer
    • 8.1.1 Table Cancer Testing – by Country
    • 8.1.2 Chart - Cancer Testing Growth
  • 8.2 Infectious Disease Testing
    • 8.2.1 Table Infectious Disease Testing – by Country
    • 8.2.2 Chart – Infectious Disease Testing Growth
  • 8.3 Metabolic Testing
    • 8.3.1 Table Metabolic Testing – by Country
    • 8.3.2 Chart - Metabolic Testing Growth
  • 8.4 Cardiac Testing
    • 8.4.1 Table Cardiac Testing – by Country
    • 8.4.2 Chart - Cardiac Testing Growth
  • 8.5 Diabetes Testing
    • 8.5.1 Table Diabetes Testing – by Country
    • 8.5.2 Chart - Diabetes Testing Growth
  • 8.6 Other Disease Testing
    • 8.6.1 Table Other Disease Testing – by Country
    • 8.6.2 Chart – Other Disease Testing Growth

9 Global Markets – By Technology

  • 9.1 NGS Technology
    • 9.1.1 Table NGS Technology – by Country
    • 9.1.2 Chart – NGS Technology Growth
  • 9.2 PCR Technology
    • 9.2.1 Table PCR Technology – by Country
    • 9.2.2 Chart – PCR Technology Growth
  • 9.3 Chemistry/IA Technology
    • 9.3.1 Table Chemistry/IA Technology – by Country
    • 9.3.2 Chart - Chemistry/IA Technology Growth
  • 9.4 Pathology Technology
    • 9.4.1 Table Pathology Technology – by Country
    • 9.4.2 Chart - Pathology Technology Growth
  • 9.5 Other Technology
    • 9.5.1 Table Other Technology – by Country
    • 9.5.2 Chart - Other Technology Growth

10 Global Markets – By Place

  • 10.1 Research
    • 10.1.1 Table Research – by Country
    • 10.1.2 Chart – Research Growth
  • 10.2 Pharmaceutical Research
    • 10.2.1 Table Pharmaceutical Research – by Country
    • 10.2.2 Chart - Pharmaceutical Research Growth
  • 10.3 Clinical
    • 10.3.1 Table Clinical – by Country
    • 10.3.2 Chart - Clinical Growth
  • 10.4 Other Place
    • 10.4.1 Table Other Place – by Country
    • 10.4.2 Chart – Other Place Growth

11 Global Markets – By Product

  • 11.1 Instruments
    • 11.1.1 Table Instruments – by Country
    • 11.1.2 Chart – Instruments Growth
  • 11.2 Assay
    • 11.2.1 Table Assay – by Country
    • 11.2.2 Chart - Assay Growth
  • 11.3 Software
    • 11.3.1 Table Software – by Country
    • 11.3.2 Chart - Software Growth
  • 11.4 Services
    • 11.4.1 Table Services – by Country
    • 11.4.2 Chart - Services Growth
  • 11.5 Other Product
    • 11.5.1 Table Other Product – by Country
    • 11.5.2 Chart – Other Product Growth

12 Appendices

  • 12.1 United States Clinical Laboratory Fees Schedule
    • 12.1.1 Laboratory Fees Schedule
    • 12.1.2 The Most Used IVD Assays
    • 12.1.3 The Highest Grossing Assays

Table of Tables

  • Table 1 Market Players by Type
  • Table 2 Factors Driving Growth
  • Table 3 Four Factors Limiting Growth
  • Table 4 Seven Key Diagnostic Laboratory Technology Trends
  • Table 5 - Global Market by Region
  • Table 6 Global Market by Application
  • Table 7 Global Market by Technology
  • Table 8 Global Market by Place
  • Table 9 Global Market by Product
  • Table 10 Cancer Testing by Country
  • Table 11 Infectious Disease Testing by Country
  • Table 12 Metabolic Testing by Country
  • Table 13 Cardiac Testing by Country
  • Table 14 Diabetes Testing by Country
  • Table 15 Other Disease Testing by Country
  • Table 16 NGS Technology by Country
  • Table 17 PCR Technology by Country
  • Table 18 Chemistry/IA Technology by Country
  • Table 19 Pathology Technology by Country
  • Table 20 Other Technology by Country
  • Table 21 Research by Country
  • Table 22 Pharmaceutical Research by Country
  • Table 23 Clinical by Country
  • Table 24 Other Place by Country
  • Table 25 Instruments by Country
  • Table 26 Assay by Country
  • Table 27 Software by Country
  • Table 28 Services by Country
  • Table 29 Other Product by Country
  • Table 30 Laboratory Fee Schedule
  • Table 31 The Most Common Assays
  • Table 32 Largest Revenue Assays

Table of Figures

  • Figure 1 Global Healthcare Spending
  • Figure 2 The Lab Test Pie
  • Figure 3 The Road to Diagnostics
  • Figure 4 AI and Learning Methods
  • Figure 5 The Changing Age of The World’s Population
  • Figure 6 Health Care Consumption by Age
  • Figure 7 Cancer Incidence - Age at Diagnosis
  • Figure 8 Centralized vs. Decentralized Laboratory Service
  • Figure 9 A Highly Multiplexed Syndromic Testing Unit
  • Figure 10 The Real Cost to Sequence the Human Genome
  • Figure 11 The Codevelopment Process
  • Figure 12 Comparing MDx Diagnostic and Traditional Testing
  • Figure 13 Base Year Country Global Share
  • Figure 14 Global Market by Application - Base vs. Final Year
  • Figure 15 Market by Application Base Year
  • Figure 16 Market by Application Final Year
  • Figure 17 Application Share by Year
  • Figure 18 Application Segment Growth
  • Figure 19 Global Market by Technology - Base vs. Final Year
  • Figure 20 Market by Technology Base Year
  • Figure 21 Market by Technology Final Year
  • Figure 22 Market by Technology Share by Year
  • Figure 23 Market by Technology Segment Growth
  • Figure 24 Market by Place - Base vs. Final Year
  • Figure 25 Market by Place Base Year
  • Figure 26 Market by Place Final Year
  • Figure 27 Market by Place Share by Year
  • Figure 28 Market by Place Segment Growth
  • Figure 29 Market by Product - Base vs. Final Year
  • Figure 30 Market by Product Base Year
  • Figure 31 Market by Product Final Year
  • Figure 32 Market by Product Share by Year
  • Figure 33 Market by Product Segment Growth
  • Figure 34 Cancer Testing Growth
  • Figure 35 Infectious Disease Testing Growth
  • Figure 36 Metabolic Testing Growth
  • Figure 37 Cardiac Testing Growth
  • Figure 38 Diabetes Testing Growth
  • Figure 39 Other Disease Testing Growth
  • Figure 40 NGS Technology Growth
  • Figure 41 PCR Technology Growth
  • Figure 42 Chemistry/IA Technology Growth
  • Figure 43 Pathology Technology Growth
  • Figure 44 Other Technology Growth
  • Figure 45 Research Growth
  • Figure 46 Pharmaceutical Research Growth
  • Figure 47 Clinical Growth
  • Figure 48 Other Place Growth
  • Figure 49 Instruments Growth
  • Figure 50 Assay Growth
  • Figure 51 Software Growth
  • Figure 52 Services Growth
  • Figure 53 Other Product Growth