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市场调查报告书
商品编码
1776739

2032 年病理学 AI 诊断自动化市场预测：按组件、部署模式、技术、应用、最终用户和地区进行的全球分析

AI in Pathology - Diagnostic Automation Market Forecasts to 2032 - Global Analysis by Component (Software, Hardware and Services), Deployment Mode (On-premise, Cloud-based and Hybrid), Technology, Application, End User and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球病理学 AI 诊断自动化市场预计在 2025 年将达到 8,697 亿美元，到 2032 年将达到 3,2,648 亿美元，预测期内的复合年增长率为 20.8%。

病理学中的人工智慧 - 诊断自动化利用人工智慧分析病理影像，从而简化工作流程并支援诊断决策。它可自动执行诸如切片筛检和影像量化等重复性任务，从而提高准确性和效率。将机器学习与数位病理学工具结合，有助于病理学家更快、更准确地检测疾病，最终改善患者预后，并在现代医疗保健中实现更具可扩展性、数据驱动的诊断。

根据《卫报》报道，剑桥大学的一种人工智慧演算法分析了 4,000 多张十二指肠切片检查影像，几乎立即诊断出乳糜泻，而人类病理学家则需要 5-10 分钟才能诊断出来。

扩大数位病理学的应用

医疗保健机构正在增加对全切片成像扫描仪和数位基础设施的投资，以提高诊断准确性和工作流程效率。这种转变将使病理学家能够远端分析组织样本，从而促进跨地域的远距病理咨询和第二意见。此外，数位病理学为人工智慧演算法的部署奠定了重要基础，因为机器学习模型需要数位化病理组织影像进行训练和检验。数位病理学平台与人工智慧的结合将显着缩短诊断审查时间，同时提高病理评估的一致性。

缺乏标准化数据

缺乏标准化的数据通讯协定对人工智慧在诊断病理学中的应用构成了重大挑战。不同实验室的组织准备、染色程序和成像参数各不相同，导致数据不一致，从而影响了人工智慧模型的性能。此外，缺乏统一的病理图像註释标准，阻碍了精确人工智慧演算法所需的强大训练资料集的开发。此外，高品质註释资料集的匮乏也限制了深度学习模型的有效性及其对不同患者群体和疾病类型的适用性。

与多体学资料和精准医疗的整合

人工智慧病理学与多体学数据的融合为个人化医疗提供了前所未有的机会。透过将组织病理学图像分析与基因组学、蛋白质组学和代谢组学资讯相结合，人工智慧系统可以提供全面的疾病表征和治疗方法建议。这种整合能够识别新的生物标记和治疗标靶，这在精准医疗方法日益普及的肿瘤学应用中尤其重要。此外，对个人化医疗的日益重视为能够无缝整合各种数据以支援临床决策流程的人工智慧解决方案创造了巨大的市场机会。

数据偏见和普遍性问题

数据偏差对人工智慧在病理诊断领域的广泛应用构成了重大威胁，因为基于不具代表性的数据集训练的演算法可能会在不同患者群体中产生不可靠的结果。疾病概况在地理、人口和机构方面的差异可能导致人工智慧模型在某些环境中表现良好，但在部署到不同的临床环境中时却会失败。此外，训练资料集缺乏多样性可能会加剧现有的医疗保健差距，并限制人工智慧解决方案的全球适用性。此外，许多人工智慧演算法的「黑箱」特性引发了人们对透明度和可解释性的担忧，使病理学家难以理解和信任人工智慧产生的建议。这种普遍性挑战可能会削弱人们对人工智慧系统的信任，并减缓其在临床实践中的应用。

COVID-19的影响：

新冠疫情加速了数位病理学和人工智慧技术的采用，因为医疗保健系统力求在维持诊断服务的同时最大限度地减少身体接触。远距办公的需求促使远距病理学解决方案的引入，使病理学家能够在家中审查病例并与同事进行虚拟协作。此外，疫情凸显了病理学家的严重短缺以及对自动化诊断工具的需求，以有效应对日益增长的工作量。这场危机也刺激了对云端基础的病理学平台和人工智慧诊断系统的投资，以确保即使在封锁和保持社交距离措施期间也能持续提供医疗服务。

预计在预测期内软体部分将成为最大的部分。

人工智慧演算法和分析平台在病理诊断自动化中发挥重要作用，预计软体领域将在预测期内占据最大的市场占有率。软体解决方案包括影像分析演算法、机器学习模型和诊断决策支援系统，这些构成了人工智慧病理工作流程的核心。对自动化影像解读、模式识别和诊断辅助的需求日益增长，推动了软体开发的大量投资。此外，持续的演算法改进和针对各种病理状况的专用应用程式的开发，也巩固了该领域的市场主导地位。

预计在预测期内，云端基础的部分将以最高的复合年增长率成长。

预计在预测期内，云端基础的细分市场将实现最高成长率，这得益于对可扩展、可存取且经济高效的人工智慧病理学解决方案的需求。云端平台使医疗机构无需大量的领先基础设施投资即可存取复杂的人工智慧演算法，即使是规模较小的实验室和资源受限的环境也能使用先进的诊断工具。此外，云端基础的系统促进了病理学家之间的无缝协作，实现了远距会诊，并支援共用训练人工智慧模型所需的大型组织病理学资料集。此外，云端平台支援持续的演算法更新和改进，使用户无需手动安装软体即可享受最新的人工智慧功能。

比最大的地区

在预测期内，北美预计将占据最大的市场占有率，这得益于其先进的医疗基础设施、强劲的研发投入以及对人工智慧医疗设备的良好法规环境。该地区受益于强有力的政府倡议，例如ARPA-H等组织的资助计划，这些计划推动了人工智慧在临床诊断中的应用。此外，领先科技公司的出现以及医疗保健提供者与人工智慧开发商之间建立的伙伴关係正在加速市场发展。数位病理系统的高采用率和熟练专业人员的存在进一步巩固了北美市场的地位。

复合年增长率最高的地区：

预计亚太地区在预测期内的复合年增长率最高。这得益于医疗保健支出的增加、数位基础设施的扩张以及人们对人工智慧在医疗诊断领域应用的日益关注。中国、日本和印度等国家正在大力投资医疗保健现代化计划，包括人工智慧病理学解决方案，以应对日益加重的疾病负担和病理学家短缺的问题。此外，该地区庞大的患者群体为训练和检验人工智慧模型提供了丰富的数据集，为本地化演算法开发创造了机会。政府对数位健康倡议的支持以及对人工智慧在医疗保健领域应用的优惠政策将推动市场扩张。

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目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球病理学人工智慧诊断自动化市场（按组件）

  • 软体
    • 人工智慧影像分析和量化软体
    • 诊断决策支援与报告软体
    • 数位病理学工作流程管理软体
  • 硬体
    • 全切片扫描仪
    • 先进的数位显微镜（支援人工智慧）
    • 高效能运算和资料储存基础设备基础设施
  • 服务
    • 实施和整合服务
    • 维护和支援服务
    • 咨询和培训服务
    • 资料註释和管理服务

6. 全球病理学人工智慧诊断自动化市场（按部署模式）

  • 本地
  • 云端基础
  • 杂交种

7. 全球病理学人工智慧诊断自动化市场（按技术）

  • 机器学习 (ML) 与深度学习 (DL)
    • 卷积类神经网路（CNN）
    • 生成对抗网路（GAN）
    • 循环神经网路（RNN）
    • 其他深度学习架构
  • 自然语言处理（NLP）
  • 电脑视觉
  • 其他人工智慧技术

8. 全球病理学人工智慧诊断自动化市场（按应用）

  • 疾病诊断与预后
    • 癌症诊断
    • 感染疾病诊断
    • 神经病理学
    • 肾臟病理学
    • 胃肠道病理学
    • 其他疾病的诊断
    • 预后预测与復发风险评估
  • 药物研发
    • 目标识别与检验
    • 化合物筛检与疗效评估
    • 生物标誌物的发现与量化
    • 毒理学和安全病理学研究
    • 临床试验中的患者分层
  • 优化临床工作流程
    • 自动幻灯片分类和优先排序
    • 自动化品管和保证
    • 自动报告产生和註释帮助
    • 案件管理和归檔效率
  • 研究和学术用途
    • 基础与转化研究
    • 病理学教育和培训工具

9. 全球病理学人工智慧诊断自动化市场（按最终用户）

  • 医院和医疗机构
  • 诊断实验室
  • 製药和生物技术公司
  • 学术研究机构
  • 合约研究组织（CRO）

第 10 章。按地区分類的全球病理学 AI 诊断自动化市场

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章：企业概况

  • PathAI, Inc.
  • Paige.AI, Inc.
  • Aiforia Technologies Plc
  • Akoya Biosciences, Inc.
  • Deep Bio, Inc.
  • Ibex Medical Analytics Ltd.
  • Proscia Inc.
  • Indica Labs, Inc.
  • Inspirata, Inc.
  • Mindpeak GmbH
  • Tribun Health
  • OptraSCAN, Inc.
  • aetherAI Co., Ltd.
  • DoMore Diagnostics AS
  • Hologic, Inc.
  • Roche Tissue Diagnostics
  • Google（Alphabet Inc.）
  • Microsoft
Product Code: SMRC30070

According to Stratistics MRC, the Global AI in Pathology - Diagnostic Automation Market is accounted for $869.7 billion in 2025 and is expected to reach $3264.8 billion by 2032 growing at a CAGR of 20.8% during the forecast period. AI in Pathology-Diagnostic Automation uses artificial intelligence to analyze pathology images, streamline workflows, and support diagnostic decisions. It automates repetitive tasks like slide screening and image quantification, improving accuracy and efficiency. By integrating machine learning with digital pathology tools, it helps pathologists detect diseases faster and with greater precision, ultimately enhancing patient outcomes and enabling more scalable, data-driven diagnostics in modern healthcare.

According to The Guardian, a University of Cambridge AI algorithm analysed 4,000+ duodenal biopsy images and diagnosed coeliac disease almost instantly, compared to the 5-10 minutes a human pathologist takes per case.

Market Dynamics:

Driver:

Increasing adoption of digital pathology

Healthcare institutions are increasingly investing in whole slide imaging scanners and digital infrastructure to enhance diagnostic accuracy and workflow efficiency. This transformation enables pathologists to analyze tissue samples remotely, facilitating telepathology consultations and second opinions across geographical boundaries. Furthermore, digital pathology creates the essential foundation for AI algorithm deployment, as machine learning models require digitized histopathological images for training and validation. The integration of AI with digital pathology platforms significantly reduces diagnostic review time while improving consistency in pathological assessments.

Restraint:

Lack of standardized data

The absence of standardized data protocols poses a significant challenge to AI implementation in pathology diagnostics. Variability in tissue preparation, staining procedures, and imaging parameters across different laboratories creates inconsistencies that can compromise AI model performance. Additionally, the lack of uniform annotation standards for pathological images hinders the development of robust training datasets required for accurate AI algorithms. Moreover, the scarcity of high-quality, annotated datasets limits the effectiveness of deep learning models and their applicability across diverse patient populations and disease types.

Opportunity:

Integration with multi-omics data and precision medicine

The convergence of AI pathology with multi-omics data presents unprecedented opportunities for personalized healthcare delivery. By combining histopathological image analysis with genomic, proteomic, and metabolomic information, AI systems can provide comprehensive disease characterization and treatment recommendations. This integration enables the identification of novel biomarkers and therapeutic targets, particularly valuable in oncology applications where precision medicine approaches are increasingly adopted. Furthermore, the growing emphasis on personalized medicine creates substantial market opportunities for AI solutions that can seamlessly integrate diverse data types to support clinical decision-making processes.

Threat:

Data bias and generalizability issues

Data bias represents a critical threat to the widespread adoption of AI in pathology diagnostics, as algorithms trained on non-representative datasets may produce unreliable results across different patient populations. Geographic, demographic, and institutional variations in disease presentation can lead to AI models that perform well in specific settings but fail when deployed in diverse clinical environments. Additionally, the lack of diversity in training datasets can perpetuate existing healthcare disparities and limit the global applicability of AI solutions. Moreover, the "black box" nature of many AI algorithms raises concerns about transparency and explainability, making it difficult for pathologists to understand and trust AI-generated recommendations. These generalizability challenges can undermine confidence in AI systems and slow their clinical adoption.

Covid-19 Impact:

The COVID-19 pandemic accelerated the adoption of digital pathology and AI technologies as healthcare systems sought to maintain diagnostic services while minimizing physical contact. Remote work requirements necessitated the implementation of telepathology solutions, enabling pathologists to review cases from home and collaborate virtually with colleagues. Furthermore, the pandemic highlighted the critical shortage of pathologists and the need for automated diagnostic tools to handle increased workloads efficiently. The crisis also drove investments in cloud-based pathology platforms and AI-powered diagnostic systems to ensure continuity of care during lockdowns and social distancing measures.

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

The software segment is expected to account for the largest market share during the forecast period due to the fundamental role of AI algorithms and analytical platforms in pathology automation. Software solutions encompass image analysis algorithms, machine learning models, and diagnostic decision support systems that form the core of AI-powered pathology workflows. The increasing demand for automated image interpretation, pattern recognition, and diagnostic assistance drives substantial investment in software development. Additionally, continuous algorithm improvements and the development of specialized applications for various pathological conditions contribute to the segment's dominant market position.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by the need for scalable, accessible, and cost-effective AI pathology solutions. Cloud platforms enable healthcare institutions to access sophisticated AI algorithms without substantial upfront infrastructure investments, making advanced diagnostic tools available to smaller laboratories and resource-constrained settings. Furthermore, cloud-based systems facilitate seamless collaboration between pathologists, enable remote consultations, and support the sharing of large histopathological datasets required for AI model training. Additionally, cloud platforms support continuous algorithm updates and improvements, ensuring that users have access to the latest AI capabilities without manual software installations.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to the region's advanced healthcare infrastructure, substantial research and development investments, and favorable regulatory environment for AI medical devices. The region benefits from strong government initiatives, including funding programs from organizations like ARPA-H that promote AI implementation in clinical diagnostics. Additionally, the presence of leading technology companies and established partnerships between healthcare providers and AI developers accelerate market growth. The high adoption rate of digital pathology systems and the availability of skilled professionals further strengthen North America's market position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing healthcare expenditure, expanding digital infrastructure, and rising awareness of AI applications in medical diagnostics. Countries like China, Japan, and India are investing heavily in healthcare modernization initiatives that include AI pathology solutions to address growing disease burdens and pathologist shortages. Furthermore, the region's large patient population provides extensive datasets for AI model training and validation, creating opportunities for localized algorithm development. Government support for digital health initiatives and favorable policies for AI adoption in healthcare accelerate market expansion.

Key players in the market

Some of the key players in AI in Pathology - Diagnostic Automation Market include PathAI, Inc., Paige.AI, Inc., Aiforia Technologies Plc, Akoya Biosciences, Inc., Deep Bio, Inc., Ibex Medical Analytics Ltd., Proscia Inc., Indica Labs, Inc., Inspirata, Inc., Mindpeak GmbH, Tribun Health, OptraSCAN, Inc., aetherAI Co., Ltd., DoMore Diagnostics AS, Hologic, Inc., Roche Tissue Diagnostics, Google (Alphabet Inc.) and Microsoft.

Key Developments:

In June 2025, PathAI, a global leader in artificial intelligence (AI) and digital pathology solutions announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for AISight(R) Dx*-its digital pathology image management system-for use in primary diagnosis in clinical settings. Building on the initial 510(k) clearance for AISight Dx(Novo) in 2022, this latest milestone underscores the platform's continuous innovation and PathAI's commitment to delivering enhanced capabilities as the product evolves.

In March 2025, Aiforia Technologies, a pioneer in AI-driven diagnostics in pathology, has announced a new partnership with PathPresenter. This collaboration aims to broaden the reach and adoption of Aiforia's AI-powered image analysis solutions by utilizing PathPresenter's comprehensive pathology workflow platform. By combining their distinct expertise in digital pathology, the companies aim to provide pathologists with enhanced diagnostic capabilities and streamlined end-to-end workflow management solutions.

In March 2025, Proscia(R), a software company leading pathology's transition to digital and AI, has secured $50M in funding, bringing its total raised to $130M. This investment follows Proscia's record-breaking growth in 2024. Proscia now counts 16 of the top 20 pharmaceutical companies among its users and is on track for 22,000+ patients to be diagnosed on its Concentriq(R) software platform each day.

Components Covered:

  • Software
  • Hardware
  • Services

Deployment Modes:

  • On-premise
  • Cloud-based
  • Hybrid

Technologies Covered:

  • Machine Learning (ML) & Deep Learning (DL)
  • Natural Language Processing (NLP)
  • Computer Vision
  • Other AI Technologies

Applications Covered:

  • Disease Diagnosis & Prognosis
  • Drug Discovery & Development
  • Clinical Workflow Optimization
  • Research & Academic Applications

End Users Covered:

  • Hospitals & Healthcare Institutions
  • Diagnostic Laboratories
  • Pharmaceutical & Biotechnology Companies
  • Academic & Research Institutes
  • Contract Research Organizations (CROs)

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

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

Free Customization Offerings:

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

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

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Pathology - Diagnostic Automation Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 AI-Powered Image Analysis & Quantification Software
    • 5.2.2 Diagnostic Decision Support & Reporting Software
    • 5.2.3 Digital Pathology Workflow Management Software
  • 5.3 Hardware
    • 5.3.1 Whole Slide Scanners
    • 5.3.2 Advanced Digital Microscopes (AI-enabled)
    • 5.3.3 High-Performance Computing & Data Storage Infrastructure
  • 5.4 Services
    • 5.4.1 Implementation & Integration Services
    • 5.4.2 Maintenance & Support Services
    • 5.4.3 Consulting & Training Services
    • 5.4.4 Data Annotation & Curation Services

6 Global AI in Pathology - Diagnostic Automation Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-premise
  • 6.3 Cloud-based
  • 6.4 Hybrid

7 Global AI in Pathology - Diagnostic Automation Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning (ML) & Deep Learning (DL)
    • 7.2.1 Convolutional Neural Networks (CNNs)
    • 7.2.2 Generative Adversarial Networks (GANs)
    • 7.2.3 Recurrent Neural Networks (RNNs)
    • 7.2.4 Other Deep Learning Architectures
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Computer Vision
  • 7.5 Other AI Technologies

8 Global AI in Pathology - Diagnostic Automation Market, By Application

  • 8.1 Introduction
  • 8.2 Disease Diagnosis & Prognosis
    • 8.2.1 Cancer Diagnosis
    • 8.2.2 Infectious Disease Diagnosis
    • 8.2.3 Neuropathology
    • 8.2.4 Renal Pathology
    • 8.2.5 Gastrointestinal Pathology
    • 8.2.6 Other Disease Diagnosis
    • 8.2.7 Prognosis Prediction & Recurrence Risk Assessment
  • 8.3 Drug Discovery & Development
    • 8.3.1 Target Identification & Validation
    • 8.3.2 Compound Screening & Efficacy Assessment
    • 8.3.3 Biomarker Discovery & Quantification
    • 8.3.4 Toxicology & Safety Pathology Studies
    • 8.3.5 Clinical Trial Patient Stratification
  • 8.4 Clinical Workflow Optimization
    • 8.4.1 Automated Slide Triage & Prioritization
    • 8.4.2 Automated Quality Control & Assurance
    • 8.4.3 Automated Reporting & Annotation Assistance
    • 8.4.4 Case Management & Archiving Efficiency
  • 8.5 Research & Academic Applications
    • 8.5.1 Basic & Translational Research
    • 8.5.2 Pathology Education & Training Tools

9 Global AI in Pathology - Diagnostic Automation Market, By End User

  • 9.1 Introduction
  • 9.2 Hospitals & Healthcare Institutions
  • 9.3 Diagnostic Laboratories
  • 9.4 Pharmaceutical & Biotechnology Companies
  • 9.5 Academic & Research Institutes
  • 9.6 Contract Research Organizations (CROs)

10 Global AI in Pathology - Diagnostic Automation Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 PathAI, Inc.
  • 12.2 Paige.AI, Inc.
  • 12.3 Aiforia Technologies Plc
  • 12.4 Akoya Biosciences, Inc.
  • 12.5 Deep Bio, Inc.
  • 12.6 Ibex Medical Analytics Ltd.
  • 12.7 Proscia Inc.
  • 12.8 Indica Labs, Inc.
  • 12.9 Inspirata, Inc.
  • 12.10 Mindpeak GmbH
  • 12.11 Tribun Health
  • 12.12 OptraSCAN, Inc.
  • 12.13 aetherAI Co., Ltd.
  • 12.14 DoMore Diagnostics AS
  • 12.15 Hologic, Inc.
  • 12.16 Roche Tissue Diagnostics
  • 12.17 Google (Alphabet Inc.)
  • 12.18 Microsoft

List of Tables

  • Table 1 Global AI in Pathology - Diagnostic Automation Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Pathology - Diagnostic Automation Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Pathology - Diagnostic Automation Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI in Pathology - Diagnostic Automation Market Outlook, By AI-Powered Image Analysis & Quantification Software (2024-2032) ($MN)
  • Table 5 Global AI in Pathology - Diagnostic Automation Market Outlook, By Diagnostic Decision Support & Reporting Software (2024-2032) ($MN)
  • Table 6 Global AI in Pathology - Diagnostic Automation Market Outlook, By Digital Pathology Workflow Management Software (2024-2032) ($MN)
  • Table 7 Global AI in Pathology - Diagnostic Automation Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 8 Global AI in Pathology - Diagnostic Automation Market Outlook, By Whole Slide Scanners (2024-2032) ($MN)
  • Table 9 Global AI in Pathology - Diagnostic Automation Market Outlook, By Advanced Digital Microscopes (AI-enabled) (2024-2032) ($MN)
  • Table 10 Global AI in Pathology - Diagnostic Automation Market Outlook, By High-Performance Computing & Data Storage Infrastructure (2024-2032) ($MN)
  • Table 11 Global AI in Pathology - Diagnostic Automation Market Outlook, By Services (2024-2032) ($MN)
  • Table 12 Global AI in Pathology - Diagnostic Automation Market Outlook, By Implementation & Integration Services (2024-2032) ($MN)
  • Table 13 Global AI in Pathology - Diagnostic Automation Market Outlook, By Maintenance & Support Services (2024-2032) ($MN)
  • Table 14 Global AI in Pathology - Diagnostic Automation Market Outlook, By Consulting & Training Services (2024-2032) ($MN)
  • Table 15 Global AI in Pathology - Diagnostic Automation Market Outlook, By Data Annotation & Curation Services (2024-2032) ($MN)
  • Table 16 Global AI in Pathology - Diagnostic Automation Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 17 Global AI in Pathology - Diagnostic Automation Market Outlook, By On-premise (2024-2032) ($MN)
  • Table 18 Global AI in Pathology - Diagnostic Automation Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 19 Global AI in Pathology - Diagnostic Automation Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 20 Global AI in Pathology - Diagnostic Automation Market Outlook, By Technology (2024-2032) ($MN)
  • Table 21 Global AI in Pathology - Diagnostic Automation Market Outlook, By Machine Learning (ML) & Deep Learning (DL) (2024-2032) ($MN)
  • Table 22 Global AI in Pathology - Diagnostic Automation Market Outlook, By Convolutional Neural Networks (CNNs) (2024-2032) ($MN)
  • Table 23 Global AI in Pathology - Diagnostic Automation Market Outlook, By Generative Adversarial Networks (GANs) (2024-2032) ($MN)
  • Table 24 Global AI in Pathology - Diagnostic Automation Market Outlook, By Recurrent Neural Networks (RNNs) (2024-2032) ($MN)
  • Table 25 Global AI in Pathology - Diagnostic Automation Market Outlook, By Other Deep Learning Architectures (2024-2032) ($MN)
  • Table 26 Global AI in Pathology - Diagnostic Automation Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 27 Global AI in Pathology - Diagnostic Automation Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 28 Global AI in Pathology - Diagnostic Automation Market Outlook, By Other AI Technologies (2024-2032) ($MN)
  • Table 29 Global AI in Pathology - Diagnostic Automation Market Outlook, By Application (2024-2032) ($MN)
  • Table 30 Global AI in Pathology - Diagnostic Automation Market Outlook, By Disease Diagnosis & Prognosis (2024-2032) ($MN)
  • Table 31 Global AI in Pathology - Diagnostic Automation Market Outlook, By Cancer Diagnosis (2024-2032) ($MN)
  • Table 32 Global AI in Pathology - Diagnostic Automation Market Outlook, By Infectious Disease Diagnosis (2024-2032) ($MN)
  • Table 33 Global AI in Pathology - Diagnostic Automation Market Outlook, By Neuropathology (2024-2032) ($MN)
  • Table 34 Global AI in Pathology - Diagnostic Automation Market Outlook, By Renal Pathology (2024-2032) ($MN)
  • Table 35 Global AI in Pathology - Diagnostic Automation Market Outlook, By Gastrointestinal Pathology (2024-2032) ($MN)
  • Table 36 Global AI in Pathology - Diagnostic Automation Market Outlook, By Other Disease Diagnosis (2024-2032) ($MN)
  • Table 37 Global AI in Pathology - Diagnostic Automation Market Outlook, By Prognosis Prediction & Recurrence Risk Assessment (2024-2032) ($MN)
  • Table 38 Global AI in Pathology - Diagnostic Automation Market Outlook, By Drug Discovery & Development (2024-2032) ($MN)
  • Table 39 Global AI in Pathology - Diagnostic Automation Market Outlook, By Target Identification & Validation (2024-2032) ($MN)
  • Table 40 Global AI in Pathology - Diagnostic Automation Market Outlook, By Compound Screening & Efficacy Assessment (2024-2032) ($MN)
  • Table 41 Global AI in Pathology - Diagnostic Automation Market Outlook, By Biomarker Discovery & Quantification (2024-2032) ($MN)
  • Table 42 Global AI in Pathology - Diagnostic Automation Market Outlook, By Toxicology & Safety Pathology Studies (2024-2032) ($MN)
  • Table 43 Global AI in Pathology - Diagnostic Automation Market Outlook, By Clinical Trial Patient Stratification (2024-2032) ($MN)
  • Table 44 Global AI in Pathology - Diagnostic Automation Market Outlook, By Clinical Workflow Optimization (2024-2032) ($MN)
  • Table 45 Global AI in Pathology - Diagnostic Automation Market Outlook, By Automated Slide Triage & Prioritization (2024-2032) ($MN)
  • Table 46 Global AI in Pathology - Diagnostic Automation Market Outlook, By Automated Quality Control & Assurance (2024-2032) ($MN)
  • Table 47 Global AI in Pathology - Diagnostic Automation Market Outlook, By Automated Reporting & Annotation Assistance (2024-2032) ($MN)
  • Table 48 Global AI in Pathology - Diagnostic Automation Market Outlook, By Case Management & Archiving Efficiency (2024-2032) ($MN)
  • Table 49 Global AI in Pathology - Diagnostic Automation Market Outlook, By Research & Academic Applications (2024-2032) ($MN)
  • Table 50 Global AI in Pathology - Diagnostic Automation Market Outlook, By Basic & Translational Research (2024-2032) ($MN)
  • Table 51 Global AI in Pathology - Diagnostic Automation Market Outlook, By Pathology Education & Training Tools (2024-2032) ($MN)
  • Table 52 Global AI in Pathology - Diagnostic Automation Market Outlook, By End User (2024-2032) ($MN)
  • Table 53 Global AI in Pathology - Diagnostic Automation Market Outlook, By Hospitals & Healthcare Institutions (2024-2032) ($MN)
  • Table 54 Global AI in Pathology - Diagnostic Automation Market Outlook, By Diagnostic Laboratories (2024-2032) ($MN)
  • Table 55 Global AI in Pathology - Diagnostic Automation Market Outlook, By Pharmaceutical & Biotechnology Companies (2024-2032) ($MN)
  • Table 56 Global AI in Pathology - Diagnostic Automation Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
  • Table 57 Global AI in Pathology - Diagnostic Automation Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.