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智慧體外診斷市場：人工智慧在體外診斷中的應用—按應用、技術、產品和使用者分類—高階主管和顧問指南（2026-2030 年）
市場調查報告書
商品編碼
2025437

智慧體外診斷市場：人工智慧在體外診斷中的應用—按應用、技術、產品和使用者分類—高階主管和顧問指南（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個工作天內

價格

報告摘要：

未來20年，人工智慧（AI）將推動體外診斷（IVD）市場的成長。隨著醫生利用一切可用資訊對抗疾病，該市場正在迅速擴張。同時，製藥公司看到了開發幾乎任何治療方法的潛力。探索這種全新的診斷方法將如何徹底改變醫療保健產業。

人工智慧 (AI) 正在迅速改變體外診斷 (IVD) 產業，它能夠提高診斷準確性、加快數據解讀速度並實現更個人化的醫療決策。包括機器學習、深度學習和高級數據分析在內的人工智慧技術正日益融入臨床化學、分子診斷、病理學、基因組學、免疫檢測、微生物學和就地檢驗(POCT) 等各個診斷流程。人工智慧與先進診斷技術的融合為提高疾病檢測準確性、簡化工作流程和最佳化醫療成本創造了新的機會。

隨著醫療服務提供者、診斷公司和生命科學機構利用數據驅動的洞察來提升診斷效能，全球人工智慧驅動的體外診斷市場正在迅速擴張。這一成長的促進因素包括診斷數據日益複雜化、精準醫療的擴展、數位病理學的應用以及基因組和分子檢測技術的日益普及。人工智慧能夠更有效地利用診斷訊息，提升臨床檢測的臨床價值，並支持疾病的早期發現。

這份市場調查報告全面分析了人工智慧技術如何變革體外診斷（IVD）在多個診斷領域的格局。報告檢驗了技術趨勢、市場促進因素、競爭格局、監管考慮以及開發人工智慧診斷解決方案的企業所面臨的策略機會。

人工智慧在診斷創新中的作用

診斷技術會產生龐大而複雜的資料集，需要精細的解讀才能獲得具有臨床意義的見解。人工智慧能夠自動分析高維度診斷數據，從而識別出傳統分析方法難以發現的模式。

人工智慧演算法正日益廣泛地應用於輔助解讀醫學影像、基因組序列、生物標記組合以及多種診斷結果。機器學習模型整合了包括檢測結果、臨床資訊、影像資料和基因組圖譜在內的多種資料類型，從而有助於提高疾病分類和風險評估的準確性。

人工智慧輔助診斷可望助力疾病早期發現、提升患者分層水準並最佳化治療方法選擇。這些能力在腫瘤學、感染疾病管理、心血管疾病風險評估以及罕見疾病診斷等領域尤其重要。

在醫療保健系統中，人工智慧工具的採用正在迅速增加，以提高診斷效率並減少臨床解釋的差異。

此外，人工智慧技術能夠從大規模資料集中不斷學習，從而不斷提高診斷效能。

主要應用領域

人工智慧正在體外診斷市場的多個細分領域中得到應用。

  • 數位病理學是人工智慧應用方面最先進的領域之一。機器學習演算法可以分析組織病理學影像，以識別癌症生物標記並對組織形態進行分類。
  • 在分子診斷和基因組學領域，正在利用人工智慧分析複雜的基因資料集，並識別具有臨床意義的突變。
  • 在微生物檢查室中，人工智慧工具被用於識別病原體和檢測抗生素抗藥性模式。
  • 在臨床化學應用領域，預測分析用於識別患者檢測數據中的趨勢。
  • 在免疫檢測檢測中，正在利用人工智慧輔助分析多個生物標記組合。
  • 流式細胞技術和細胞分析技術利用人工智慧對細胞群體進行分類，並識別罕見的細胞表現型。
  • 照護現場利用人工智慧驅動的分析工具，支援分散式醫療服務。
  • 此外，人工智慧技術還有潛力最佳化檢查室工作流程並支援品管流程。

市場促進因素

多種因素正在推動人工智慧驅動的體外診斷（IVD）市場成長。

  • 隨著診斷資料量和複雜性的增加，對先進分析工具的需求也在成長。
  • 擴大精準醫療計畫需要整合基因組數據和生物標記數據。
  • 某些診斷領域專業臨床知識的匱乏，促使人們對自動化決策支援工具越來越感興趣。
  • 醫療機構正在尋求能夠提高效率並減少診斷差異的技術。
  • 雲端運算和資料儲存技術的進步使得管理大規模診斷資料整合為可能。
  • 數位醫療技術的發展正在推動人工智慧診斷工具的整合。
  • 監管機構正在不斷改善相關框架，以支援人工智慧在醫療領域的應用。
  • 對醫療數據分析的投資正在推動診斷技術的創新。
  • 電子健康記錄的日益普及使得將人工智慧工具整合到臨床工作流程中成為可能。

市場區隔

IVD 市場的人工智慧可以按診斷應用、技術類型、最終用戶和地區進行細分。

  • 按診斷應用分類，主要領域包括數位病理學、分子診斷、臨床化學、微生物學、免疫檢測和流式細胞技術。
  • 從技術類型來看，機器學習、深度學習、自然語言處理和電腦視覺技術是主要類別。
  • 最終用戶包括臨床檢查室、醫院、研究機構、製藥公司和診斷藥物開發公司。
  • 由於北美擁有強大的數位醫療基礎設施和對醫療技術創新的大量投資，它已成為一個主要市場。
  • 在促進數位醫療解決方案普及的監管措施的支持下，歐洲已成為一個重要的市場。
  • 由於對醫療保健技術的投資增加以及診斷測試數量的上升，亞太市場正在擴張。
  • 新興市場為人工智慧驅動的診斷解決方案提供了潛在機遇，這些解決方案可以改善人們獲得醫療專業知識的機會。

本報告包含18個國家和4個地區的詳細數據。購買本報告的客戶將獲得全球任何國家的詳細數據。

競爭格局

人工智慧驅動的體外診斷（IVD）市場包括診斷公司、軟體開發公司、數據分析公司和數位健康公司。

競爭環境取決於演算法效能、資料品質、監管核准情況以及與檢驗資訊系統的整合能力。

診斷公司與人工智慧開發商之間的策略合作十分常見。

各公司正投資開發整合診斷平台，將檢測設備與人工智慧數據分析軟體結合。

取得資料和訓練資料集是至關重要的競爭優勢。

與醫療資訊科技系統的整合將影響臨床檢查室的採用。

與機器學習演算法相關的智慧財產權會影響市場定位。

各公司正在投資監管合規策略，以支援人工智慧診斷工具的商業化。

未來展望

人工智慧有望在塑造體外診斷的未來中發揮越來越重要的作用。

  • 機器學習演算法的進步可以提高診斷準確率，並實現疾病的早期檢測。
  • 多體學資料集的整合有可能加速開發更個人化的診斷方法。
  • 檢查室工作流程自動化有可能提高營運效率。
  • 人工智慧驅動的決策支援工具能夠提高對複雜診斷結果的臨床解讀。
  • 數位病理學和基因組檢測的擴展預計將增加對人工智慧驅動分析的需求。
  • 加強診斷設備製造商和數位醫療開發公司之間的合作可以加速創新。

總體而言，人工智慧正在變革體外診斷業界。數據分析技術的持續進步和醫療保健的數位化有望支撐市場持續成長，並為診斷領域的創新創造新的機會。

目錄

第1章 市集指南

  • 戰略情勢分析
  • 企業主管、行銷負責人和業務拓展負責人。
  • 管理顧問和投資顧問指南

第2章：引言與市場定義

  • 什麼是智慧診斷？
  • 市場定義
  • 調查方法
  • 展望：醫療保健和體外診斷行業

第3章 市場概覽

  • 參與企業充滿活力的市場
    • 學術研究辦公室
    • 診斷測試開發人員
    • 測量儀器供應商
    • 化學品/試劑供應商
    • 病理檢測用品供應商
    • 獨立臨床實驗室
    • 國家/地區公共研究機構
    • 醫院檢查室
    • 醫師診所檢查室(POLS)
    • 審計機構
    • 認證機構
  • 理解人工智慧
    • 人工智慧
    • 機器學習
    • 深度學習
    • 卷積類神經網路
    • 生成對抗網路
    • 限制
  • 人工智慧在體外診斷的應用
    • 感染疾病
    • 腫瘤學
    • 解剖病理學
    • 心臟病學
    • 糖尿病
    • 普通內科

第4章 市場趨勢

  • 成長促進因素
  • 成長阻礙因素
  • 測量儀器、自動化和診斷技術的發展趨勢

第5章 近期趨勢

第6章：主要企業概況

  • 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7章：全球智慧診斷市場

  • 全球市場概覽（按國家/地區分類）
  • 全球市場概覽（按應用領域分類）
  • 全球市場概覽（依技術分類）
  • 區域全球市場概覽
  • 全球市場概覽（按產品類別分類）

第8章 世界市場（按應用領域分類）

  • 癌症
  • 傳染病檢查
  • 代謝測試
  • 心臟檢查
  • 糖尿病檢測
  • 其他

第9章 世界市場（依技術分類）

  • NGS技術
  • PCR技術
  • 化學/IA技術
  • 病理技術
  • 其他

第10章：全球市場（依地域分類）

  • 研究
  • 藥物研究
  • 臨床
  • 其他

第11章 世界市場（依產品分類）

  • 裝置
  • 分析
  • 軟體
  • 服務
  • 其他

第12章附錄

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