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1776739

2032 年病理學 AI 診斷自動化市場預測：按組件、部署模式、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球病理學 AI 診斷自動化市場預計在 2025 年將達到 8,697 億美元，到 2032 年將達到 3,2,648 億美元，預測期內的複合年成長率為 20.8%。

病理學中的人工智慧 - 診斷自動化利用人工智慧分析病理影像，從而簡化工作流程並支援診斷決策。它可自動執行諸如切片篩檢和影像量化等重複性任務，從而提高準確性和效率。將機器學習與數位病理學工具結合，有助於病理學家更快、更準確地檢測疾病，最終改善患者預後，並在現代醫療保健中實現更具可擴展性、數據驅動的診斷。

根據《衛報》報道，劍橋大學的一種人工智慧演算法分析了 4,000 多張十二指腸切片檢查影像，幾乎立即診斷出乳糜瀉，而人類病理學家則需要 5-10 分鐘才能診斷出來。

擴大數位病理學的應用

醫療保健機構正在增加對全切片成像掃描儀和數位基礎設施的投資，以提高診斷準確性和工作流程效率。這種轉變將使病理學家能夠遠端分析組織樣本，從而促進跨地域的遠距病理諮詢和第二意見。此外，數位病理學為人工智慧演算法的部署奠定了重要基礎，因為機器學習模型需要數位化病理組織影像進行訓練和檢驗。數位病理學平台與人工智慧的結合將顯著縮短診斷審查時間，同時提高病理評估的一致性。

缺乏標準化數據

缺乏標準化的數據通訊協定對人工智慧在診斷病理學中的應用構成了重大挑戰。不同實驗室的組織準備、染色程序和成像參數各不相同，導致數據不一致，從而影響了人工智慧模型的性能。此外，缺乏統一的病理圖像註釋標準，阻礙了精確人工智慧演算法所需的強大訓練資料集的開發。此外，高品質註釋資料集的匱乏也限制了深度學習模型的有效性及其對不同患者群體和疾病類型的適用性。

與多體學資料和精準醫療的整合

人工智慧病理學與多體學數據的融合為個人化醫療提供了前所未有的機會。透過將組織病理學圖像分析與基因組學、蛋白質組學和代謝組學資訊相結合，人工智慧系統可以提供全面的疾病表徵和治療方法建議。這種整合能夠識別新的生物標記和治療標靶，這在精準醫療方法日益普及的腫瘤學應用中尤其重要。此外，對個人化醫療的日益重視為能夠無縫整合各種數據以支援臨床決策流程的人工智慧解決方案創造了巨大的市場機會。

數據偏見和普遍性問題

數據偏差對人工智慧在病理診斷領域的廣泛應用構成了重大威脅，因為基於不具代表性的資料集訓練的演算法可能會在不同患者群體中產生不可靠的結果。疾病概況在地理、人口和機構方面的差異可能導致人工智慧模型在某些環境中表現良好，但在部署到不同的臨床環境中時卻會失敗。此外，訓練資料集缺乏多樣性可能會加劇現有的醫療保健差距，並限制人工智慧解決方案的全球適用性。此外，許多人工智慧演算法的「黑箱」特性引發了人們對透明度和可解釋性的擔憂，使病理學家難以理解和信任人工智慧產生的建議。這種普遍性挑戰可能會削弱人們對人工智慧系統的信任，並減緩其在臨床實踐中的應用。

COVID-19的影響：

新冠疫情加速了數位病理學和人工智慧技術的採用，因為醫療保健系統力求在維持診斷服務的同時最大限度地減少身體接觸。遠距辦公的需求促使遠距病理學解決方案的引入，使病理學家能夠在家中審查病例並與同事進行虛擬協作。此外，疫情凸顯了病理學家的嚴重短缺以及對自動化診斷工具的需求，以有效應對日益成長的工作量。這場危機也刺激了對雲端基礎的病理學平台和人工智慧診斷系統的投資，以確保即使在封鎖和保持社交距離措施期間也能持續提供醫療服務。

預計在預測期內軟體部分將成為最大的部分。

人工智慧演算法和分析平台在病理診斷自動化中發揮重要作用，預計軟體領域將在預測期內佔據最大的市場佔有率。軟體解決方案包括影像分析演算法、機器學習模型和診斷決策支援系統，這些構成了人工智慧病理工作流程的核心。對自動化影像解讀、模式識別和診斷輔助的需求日益成長，推動了軟體開發的大量投資。此外，持續的演算法改進和針對各種病理狀況的專用應用程式的開發，也鞏固了該領域的市場主導地位。

預計在預測期內，雲端基礎的部分將以最高的複合年成長率成長。

預計在預測期內，雲端基礎的細分市場將實現最高成長率，這得益於對可擴展、可存取且經濟高效的人工智慧病理學解決方案的需求。雲端平台使醫療機構無需大量的領先基礎設施投資即可存取複雜的人工智慧演算法，即使是規模較小的實驗室和資源受限的環境也能使用先進的診斷工具。此外，雲端基礎的系統促進了病理學家之間的無縫協作，實現了遠距會診，並支援共用訓練人工智慧模型所需的大型組織病理學資料集。此外，雲端平台支援持續的演算法更新和改進，使用戶無需手動安裝軟體即可享受最新的人工智慧功能。

比最大的地區

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的醫療基礎設施、強勁的研發投入以及對人工智慧醫療設備的良好法規環境。該地區受益於強力的政府舉措，例如ARPA-H等組織的資助計劃，這些計劃推動了人工智慧在臨床診斷中的應用。此外，領先科技公司的出現以及醫療保健提供者與人工智慧開發商之間建立的夥伴關係正在加速市場發展。數位病理系統的高採用率和熟練專業人員的存在進一步鞏固了北美市場的地位。

複合年成長率最高的地區：

預計亞太地區在預測期內的複合年成長率最高。這得益於醫療保健支出的增加、數位基礎設施的擴張以及人們對人工智慧在醫療診斷領域應用的日益關注。中國、日本和印度等國家正在大力投資醫療保健現代化計劃，包括人工智慧病理學解決方案，以應對日益加重的疾病負擔和病理學家短缺的問題。此外，該地區龐大的患者群體為訓練和檢驗人工智慧模型提供了豐富的資料集，為本地化演算法開發創造了機會。政府對數位健康舉措的支持以及對人工智慧在醫療保健領域應用的優惠政策將推動市場擴張。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球病理學人工智慧診斷自動化市場（按組件）

  • 軟體
    • 人工智慧影像分析和量化軟體
    • 診斷決策支援與報告軟體
    • 數位病理學工作流程管理軟體
  • 硬體
    • 全切片掃描儀
    • 先進的數位顯微鏡（支援人工智慧）
    • 高效能運算和資料儲存基礎設備基礎設施
  • 服務
    • 實施和整合服務
    • 維護和支援服務
    • 諮詢和培訓服務
    • 資料註釋和管理服務

6. 全球病理學人工智慧診斷自動化市場（按部署模式）

  • 本地
  • 雲端基礎
  • 混合

7. 全球病理學人工智慧診斷自動化市場（按技術）

  • 機器學習 (ML) 與深度學習 (DL)
    • 卷積類神經網路（CNN）
    • 生成對抗網路（GAN）
    • 循環神經網路（RNN）
    • 其他深度學習架構
  • 自然語言處理（NLP）
  • 電腦視覺
  • 其他人工智慧技術

8. 全球病理學人工智慧診斷自動化市場（按應用）

  • 疾病診斷與預後
    • 癌症診斷
    • 感染疾病診斷
    • 神經病理學
    • 腎臟病理學
    • 胃腸道病理學
    • 其他疾病的診斷
    • 預後預測與復發風險評估
  • 藥物研發
    • 目標識別與檢驗
    • 化合物篩檢與療效評估
    • 生物標記的發現與量化
    • 毒理學和安全病理學研究
    • 臨床試驗中的患者分層
  • 最佳化臨床工作流程
    • 自動幻燈片分類和優先排序
    • 自動化品管和保證
    • 自動報告產生和註釋幫助
    • 案件管理和歸檔效率
  • 研究和學術用途
    • 基礎與轉化研究
    • 病理學教育和培訓工具

9. 全球病理學人工智慧診斷自動化市場（按最終用戶）

  • 醫院和醫療機構
  • 診斷實驗室
  • 製藥和生物技術公司
  • 學術研究機構
  • 合約研究組織（CRO）

第 10 章。按地區分類的全球病理學 AI 診斷自動化市場

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章：企業概況

  • 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.