세계의 AI 기반 디지털 병리학 시장 : 신경 회로망 유형별, 어세이 유형별, 최종사용자 유형별, 응용 영역별, 적응증 유형별, 주요 지역별(2022-2035년)

AI-based Digital Pathology / AI Pathology Market Distribution by Type of Neural Network, Type of Assay, Type of End-user, Area of Application, Type of Target Disease Indication and Key Geographies, 2022-2035

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한글목차

세계의 AI 기반 디지털 병리학(AI-based Digital Pathology) 시장에 대해 조사했으며, 시장 개요, 신경 회로망 유형별/어세이 유형별/최종사용자 유형별/응용 영역별/적응증 유형별/지역별 동향, 시장 진출기업 개요 등의 정보를 제공합니다.

목차

제1장 서문

제2장 주요 요약

제3장 서론

• 챕터 개요
• 디지털 병리학에서의 인공지능(AI)
• AI 기반 디지털 병리학 워크플로우
• AI 기반 디지털 병리학 솔루션의 응용
• AI 기반 디지털 병리학에 초점을 맞춘 규제 요건
• 디지털 병리학에서의 AI의 사용에 따른 과제
• 향후 전망

제4장 AI 기반 디지털 병리학 : 시장 상황

• 챕터 개요
• AI 기반 디지털 병리학 프로바이더 : 전체적 시장 구도 전망
• AI 기반 디지털 병리학 프로바이더 : 개발자 상황

제5장 AI 기반 디지털 병리학 시장 : 중요 인사이트

제6장 기업 개요

• 챕터 개요
• PathAI
• Paige
• Akoya Biosciences
• PROSCIA
• Visiopharm
• Roche Tissue Diagnostics
• Aiforia Technologies
• Indica Labs
• Ibex Medical Analytics

제7장 기업의 경쟁력 분석

• 챕터 개요
• 가정과 주요 파라미터
• 조사 방법
• 포트폴리오 강도 벤치마킹
• 자금조달력 벤치마킹
• 기업의 경쟁력 분석 : 소기업
• 기업의 경쟁력 분석 : 중기업
• 기업의 경쟁력 분석 : 대기업

제8장 자금조달과 투자

제9장 수요 분석

• 챕터 개요
• 범위와 조사 방법
• AI 기반 디지털 병리학에 대한 세계 수요, 2022-2035년
• AI 기반 디지털 병리학 수요 : 지역별 분석
• AI 기반 디지털 병리학 수요 : 최종사용자 유형별 분석
• 결론

제10장 시장 규모와 기회 분석

• 챕터 개요
• 예측 조사 방법과 주요 가정
• 세계의 AI 기반 디지털 병리학 시장, 2022-2035년
• AI 기반 디지털 병리학 시장 : 신경 회로망 유형별 분석, 2022년 및 2035년
• AI 기반 디지털 병리학 시장 : 어세이 유형별 분석, 2022년 및 2035년
• AI 기반 디지털 병리학 시장 : 최종사용자 유형별 분석, 2022년 및 2035년
• AI 기반 디지털 병리학 시장 : 응용 영역별 분석, 2022년 및 2035년
• AI 기반 디지털 병리학 시장 : 적응증 유형별, 2022년 및 2035년
• AI 기반 디지털 병리학 시장 : 주요 지역별 분석, 2022년 및 2035년

제11장 결론

제12장 주요 인사이트

제13장 부록 1 : 표 형식 데이터

제14장 부록 II : 회사 및 조직 리스트

LSH

영문 목차

영문목차

INTRODUCTION

Pathology is a subfield of medical science that primarily focuses on the nature, genesis and cause of a disease. Further, pathology forms an essential component of diagnostic pathways established for multiple disease indications, especially cancer detection. In fact, 70-80% of the total healthcare decisions involved in either diagnosis or treatment of ailments require a pathological assessment. Further, according to the International Agency for Research on Cancer (IARC), by 2040, 27 million new cancer cases are expected to be reported annually. , This rise in cancer cases, coupled to the rapidly ageing global population, is expected to lead to a substantial increase in the pathology workload. However, as the demand for professional pathologists continues to increase, the number of active pathologists in the field is diminishing over time. As per a recent study, a 30% decline in the number of active pathologists is expected to be observed by 2030, as compared to the number of such professionals in 2010. Moreover, 63.2% of the currently active pathologists are anticipated to retire in the next 10 years. Furthermore, it is projected that a substantial disparity (close to 30%) between the expected demand for pathology services and supply of pathologists is likely to be witnessed by the year 2030.

Amidst the ever-growing demand for pathology services, the simultaneous use of technological advances to automate and digitize healthcare procedures is growing. These developments have accelerated research and clinical diagnosis, as well as enhanced patient outcomes, in the recent years. Specifically, AI-powered digital imaging is one such technology, which has revolutionized the pathology industry by enabling high-throughput scanning of patient samples. To provide more context, AI-based digital pathology involves collection, management, analyzing and sharing of data (via digital slides) in a digital setting. Through this process, digital slides are created by scanning conventional glass slides with a scanning device, which may be seen on a computer screen or a mobile device and offer a high-resolution digital image. Further, AI in digital pathology presents a viable solution to managing the growing pathology workload, while also ensuring more rapid and consistent diagnostic services and research activities. Moreover, AI-powered digital pathology solutions (digital pathology scanners and digital pathology software) allow pathologists to examine more cases and offer a precise diagnosis. It is worth highlighting that digitized workflows can speed up processing times, lower administrative errors, enable remote collaboration and boost productivity, thereby, allowing significant cost savings. Experts believe that there has been a significant rise in the revenue generation potential within this domain. This is further supported by the significant investments being made in this industry. Since 2016, funding received by digital pathology firms have surpassed USD 1.6 billion, with majority of amount being raised in the year 2021. Considering the rising popularity and demand for such solutions in the healthcare and research industry, and the ongoing efforts of AI-powered digital pathology solution providers to further improve / expand their respective portfolios, we believe that the AI-based digital pathology market is likely to evolve at a steady pace, till 2035.

SCOPE OF THE REPORT

The "AI-based Digital Pathology Market by Type of Neural Network (Artificial Neural Network, Convolutional Neural Network, Fully Convolutional Network, Recurrent Neural Network and Other Neural Networks), Type of Assay (ER Assay, HER2 Assay, Ki67 Assay, PD-L1 Assay, PR Assay and Other Type of Assays), Type of End-user (Academic Institutions, Hospitals / Healthcare Institutions, Laboratories / Diagnostic Institutions, Research Institutes and Other End-users), Area of Application (Diagnostics, Research and Other Areas of Application), Target Disease Indication (Breast Cancer, Colorectal Cancer, Cervical Cancer, Gastrointestinal Cancer, Lung Cancer, Prostate Cancer and Other Indications) and Key Geographies (North America, Europe, Asia, Latin America, Middle East and North Africa and Rest of the World): Industry Trends and Global Forecasts, 2022-2035" report features an extensive study of the current market landscape and future potential of the AI-based digital pathology market. The study features an in-depth analysis, highlighting the capabilities of various stakeholders engaged in providing AI-based digital pathology. Amongst other elements, the report features:

• An executive summary of the insights captured during our research. It offers a high-level view on the current state of AI-based digital pathology market and its likely evolution in the mid-long term.
• A general introduction to AI-based digital pathology, featuring information on artificial intelligence in digital pathology, workflow of AI-based digital pathology, applications of AI-based digital pathology solutions in the healthcare domain. Additionally, the chapter includes details on the various regulatory requirements related to AI-based digital pathology. The chapter concludes with a discussion on the challenges, key growth drivers and future perspectives associated with the use of AI in digital pathology.
• A detailed assessment of the overall market landscape of AI-based digital pathology providers, based on several relevant parameters, such as geographical reach, year of establishment, company size (in terms of number of employees), location of headquarters (country-wise and continent-wise), type of product (hardware and software), type of service (automated image analysis, image management, vendor agnostic, cloud-based solution, whole slide imaging, laboratory information system, hospital information system and picture archiving and communication system), type of feature (prognostic algorithms, predictive algorithms and multi-modal fusion algorithms), additional features (customizability, scalability and deployment options), area of application (diagnosis and research use), target disease indication, type of assay, type of end-user (research institutes, academic institutions, hospitals / healthcare institutions, laboratories / diagnostic institutions, others) and information on number of available software.
• An in-depth analysis, highlighting the contemporary market trends, including [A] distribution based on type of service and area of application, [B] distribution based on type of feature and area of application, [C] distribution based on type of product and area of application, [D] type of product and location of headquarters, as well as [E] an insightful hybrid representation of AI-based digital pathology providers based on company size and location of headquarters.
• Elaborate profiles of various prominent players that are engaged in offering services related to AI-based digital pathology. Each profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and management team) and details related to recent developments and an informed future outlook.
• A company competitive analysis of various players engaged in this domain. It highlights the capabilities of industry players (in terms of their expertise across various services related to AI-based digital pathology). The analysis allows companies to compare their existing capabilities within and beyond their peer groups and identify opportunities to gain a competitive edge in the industry. The chapter also includes benchmarking of industry players engaged in this domain based on their portfolio strength (type of product, type of service, type of feature, additional features, area of application and type of end-user) and funding activity (number of funding instances and funding amount).
• An analysis of the funding and investments made within this domain, during the period 2016-2022, based on several relevant parameters, such as number of instances, amount invested, type of funding, area of application, geography and information on most active players engaged in the AI-based digital pathology domain.
• An elaborate analysis in order to estimate the current and future demand for AI-based digital pathology, based on several relevant parameters, such as geography (North America, Europe, Asia, Latin America, MENA and Rest of the World) and end-users (hospitals, research and other end-users).
• A detailed market forecast analysis, highlighting the likely evolution of the AI-based digital pathology market in the short to mid-term and long term, over the period 2022-2035. Further, the year-wise projections of the current and future opportunity have been segmented based on several relevant parameters, such as type of neural network (artificial neural network, convolutional neural network, fully convolutional network, recurrent neural network and other neural networks), type of assay (ER assay, HER2 assay, Ki67 assay, PD-L1 assay, PR assay and other type of assays), type of end-user (academic institutions, hospitals / healthcare institutions, laboratories / diagnostic institutions, research institutes and other end-users), area of application (diagnostics, research and other areas of application), target disease indication (breast cancer, colorectal cancer, cervical cancer, gastrointestinal cancer, lung cancer, prostate cancer and other indications) and key geographies (North America, Europe, Asia, Latin America, Middle East and North Africa and Rest of the World). In order to account for future uncertainties and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, which represent different tracks of the industry's growth.

All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.

RESEARCH METHODOLOGY

The data presented in this report has been gathered via primary and secondary research. Wherever possible, the available data has been checked for accuracy from multiple sources of information.

The secondary sources of information include:

• Annual reports
• Investor presentations
• SEC filings
• Industry databases
• News releases from company websites
• Government policy documents
• Industry analysts' views

While the focus has been on forecasting the market till 2035, the report also provides our independent view on various non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary sources of information.

KEY QUESTIONS ANSWERED:

• Who are the leading players engaged in offering AI-based digital pathology in the healthcare domain?
• Which geographies emerged as key hubs for AI-based digital pathology providers?
• Which type of end-users are primarily employing AI in digital pathology in their regular workflow?
• What type of funding initiatives are most commonly being reported by stakeholders in this domain?
• What are the key strategies that can be implemented by emerging players to enter the AI-based digital pathology market?
• What are the key market trends and driving factors that are likely to impact the growth of the AI-based digital pathology market?
• How is the current and future opportunity likely to be distributed across key market segment?

CHAPTER OUTLINES

• Chapter 2: is an executive summary of the insights captured in our research. It offers a high-level view on the current state of AI-based digital pathology market and its likely evolution in the mid-long term.

• Chapter 3: provides a general introduction to AI-based digital pathology, featuring information on artificial intelligence in digital pathology, workflow of AI-based digital pathology, applications of AI-based digital pathology solutions. Additionally, the chapter includes details on the various regulatory requirements related to AI-based digital pathology. The chapter concludes with a discussion on the challenges, key growth drivers and future perspectives associated with the use of AI in digital pathology.

• Chapter 4: provides a detailed review of the overall market landscape of AI-based digital pathology providers, based on several relevant parameters, such as geographical reach, year of establishment, company size (in terms of number of employees), location of headquarters (country-wise and continent-wise), type of product (hardware and software), type of service (automated image analysis, image management, vendor agnostic, cloud-based solution, whole slide imaging, laboratory information system, hospital information system and picture archiving and communication system), type of feature (prognostic algorithms, predictive algorithms and multi-modal fusion algorithms), additional features (customizability, scalability and deployment options), area of application (diagnosis and research use), target disease indication, type of assay, type of end-user (research institutes, academic institutions, hospitals / healthcare institutions, laboratories / diagnostic institutions, others) and information on number of available software.

• Chapter 5: provides an in-depth analysis, highlighting the contemporary market trends, using five schematic representations, including [A] distribution based on type of service and area of application, [B] distribution based on type of feature and area of application, [C] distribution based on type of product and area of application, [D] type of product and location of headquarters, [E] an insightful hybrid representation of AI-based digital pathology providers based on company size and location of headquarters.

• Chapter 6: includes detailed profiles of various prominent players that are engaged in offering services related to AI-based digital pathology. Each profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and management team) and details related to recent developments and an informed future outlook.

• Chapter 7: presents a company competitive analysis of various players engaged in this domain. It highlights the capabilities of industry players in terms of their expertise across various services related to AI-based digital pathology. The analysis allows companies to compare their existing capabilities within and beyond their peer groups and identify opportunities to gain a competitive edge in the industry. The chapter also includes benchmarking of industry players based on their portfolio strength and funding activity.

• Chapter 8: features an analysis of the funding and investments made within the domain, during the period 2016-2022, based on several relevant parameters, such as number of instances, amount invested, type of funding, area of application, geography and information on most active players engaged in this field.

• Chapter 9: presents an elaborate analysis in order to estimate the current and future demand for AI-based digital pathology, based on several relevant parameters, such as geography (North America, Europe, Asia, Latin America, MENA and Rest of the World) and end-users (hospitals, research and other end-users).

• Chapter 10: presents a detailed market forecast analysis, highlighting the likely evolution of the AI-based digital pathology market in the short to mid-term and long term, over the period 2022-2035. Further, the year-wise projections of the current and future opportunity have been segmented based on relevant parameters, such as type of neural network (artificial neural network, convolutional neural network, fully convolutional network, recurrent neural network and other neural networks), type of assay (ER assay, HER2 assay, Ki67 assay, PD-L1 assay, PR assay and other type of assays), type of end-user (academic institutions, hospitals / healthcare institutions, laboratories / diagnostic institutions, research institutes and other end-users), area of application (diagnostics, research and other areas of application), type of target disease indication (breast cancer, colorectal cancer, cervical cancer, gastrointestinal cancer, lung cancer, prostate cancer and other indications) and key geographies (North America, Europe, Asia, Latin America, Middle East and North Africa and Rest of the World). In order to account for future uncertainties and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, which represent different tracks of the industry's growth.

• Chapter 11: presents the summary of the overall report. Additionally, in this chapter, we have provided a list of key takeaways from the report, and expressed our independent opinion related to the research and analysis described in the previous chapters.

• Chapter 12: is a collection of executive insights of the discussions held with various key stakeholders in this market. The chapter provides a brief overview of the companies and details of interviews held with Joe Yeh (Chief Executive Officer and Chairman, aetherAI), Suraj Bramhane (Laboratory Director and Chief Pathologist, Clinitech Laboratory), Savvas Damaskinos (Vice President, Research and Technology, Huron Digital Pathology), Anil Berger (Vice President, Sales and Marketing, Mindpeak) and Scott Wallace (Vice President, Business Development and Strategic Partnerships, Pramana)

• Chapter 13: is an appendix, which contains the list of companies and organizations mentioned in the report.

• Chapter 14: is an appendix, which provides tabulated data and numbers for all the figures provided in the report.

TABLE OF CONTENTS

1. PREFACE

• 1.1. Chapter Overview
• 1.2. Market Segmentations
• 1.3. Research Methodology
• 1.4. Key Questions Answered
• 1.5. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

• 3.1. Chapter Overview
• 3.2. Artificial Intelligence in Digital Pathology
• 3.3. Workflow of AI-based Digital Pathology
• 3.4. Applications of AI-based Digital Pathology Solutions
• 3.5. Regulatory Requirements Focused on AI-based Digital Pathology:
• 3.6. Challenges Associated with the Use of AI in Digital Pathology
• 3.7. Future Perspectives

4. AI-BASED DIGITAL PATHOLOGY: MARKET LANDSCAPE

• 4.1. Chapter Overview
• 4.2. AI-based Digital Pathology Providers: Overall Market Landscape
  • 4.2.1. Analysis by Type of Product
  • 4.2.2. Analysis by Type of Service Offered
  • 4.2.3. Analysis by Type of Feature
  • 4.2.4. Analysis by Additional Features
  • 4.2.5. Analysis by Target Disease Indication
  • 4.2.6. Analysis by Type of Assay
  • 4.2.7. Analysis by Area of Application
  • 4.2.8. Analysis by Type of End-user
  • 4.2.9. Analysis by Number of Available Software
• 4.3. AI-based Digital Pathology Providers: Developer Landscape
  • 4.3.1. Analysis by Geographical Reach
  • 4.3.2. Analysis by Year of Establishment
  • 4.3.3. Analysis by Company Size
  • 4.3.4. Analysis by Location of Headquarters (Country-wise)
  • 4.3.5. Analysis by Location of Headquarters (Continent-wise)

5. AI-BASED DIGITAL PATHOLOGY MARKET: KEY INSIGHTS

• 5.1. Chapter Overview
  • 5.1.1. Analysis by Type of Service and Area of Application
  • 5.1.2. Analysis by Type of Feature and Area of Application
  • 5.1.3. Analysis by Type of Product and Area of Application
  • 5.1.4. Analysis by Type of Product and Location of Headquarters
  • 5.1.5. Analysis by Company Size and Location of Headquarters

6. COMPANY PROFILES

• 6.1. Chapter Overview
• 6.2. PathAI
  • 6.2.1. Company Overview
  • 6.2.2. Recent Developments and Future Outlook
• 6.3. Paige
  • 6.3.1. Company Overview
  • 6.3.2. Recent Developments and Future Outlook
• 6.4. Akoya Biosciences
  • 6.4.1. Company Overview
  • 6.4.2. Recent Developments and Future Outlook
• 6.5. PROSCIA
  • 6.5.1. Company Overview
  • 6.5.2. Recent Developments and Future Outlook
• 6.6. Visiopharm
  • 6.6.1. Company Overview
  • 6.6.2. Recent Developments and Future Outlook
• 6.7. Roche Tissue Diagnostics
  • 6.7.1. Company Overview
  • 6.7.2. Recent Developments and Future Outlook
• 6.8. Aiforia Technologies
  • 6.8.1. Company Overview
  • 6.8.2. Recent Developments and Future Outlook
• 6.9. Indica Labs
  • 6.9.1. Company Overview
  • 6.9.2. Recent Developments and Future Outlook
• 6.10. Ibex Medical Analytics
  • 6.10.1. Company Overview
  • 6.10.2. Recent Developments and Future Outlook

7. COMPANY COMPETITIVENESS ANALYSIS

• 7.1. Chapter Overview
• 7.2. Assumptions and Key Parameters
• 7.3. Methodology
• 7.4. Benchmarking of Portfolio Strength
• 7.5. Benchmarking of Funding Strength
• 7.6. Company Competitiveness Analysis: Small Players
• 7.7. Company Competitiveness Analysis: Mid-sized Players
• 7.8. Company Competitiveness Analysis: Large Players

8. FUNDING AND INVESTMENTS

• 8.1. Chapter Overview
• 8.2. Types of Funding
• 8.3. AI-based Digital Pathology: List of Funding and Investments
  • 8.3.1. Cumulative Year-wise Trend by Number of Instances
  • 8.3.2. Cumulative Year-wise Trend by Amount Invested
  • 8.3.3. Analysis by Type of Funding
  • 8.3.4. Analysis by Type of Funding and Amount Invested
  • 8.3.5. Analysis by Area of Application
  • 8.3.6. Analysis by Geography
  • 8.3.7. Most Active Players: Analysis by Number of Funding Instances
  • 8.3.8. Most Active Players: Analysis by Amount Raised
• 8.4. Concluding Remarks

9. DEMAND ANALYSIS

• 9.1. Chapter Overview
• 9.2. Scope and Methodology
• 9.3. Global Demand for AI-based Digital Pathology, 2022-2035
• 9.4. Demand for AI-based Digital Pathology: Analysis by Geography
  • 9.4.1. Demand for AI-based Digital Pathology in North America
    • 9.4.1.1 Demand for AI-based Digital Pathology in the US
    • 9.4.1.2 Demand for AI-based Digital Pathology in Canada
  • 9.4.2. Demand for AI-based Digital Pathology in Europe
    • 9.4.2.1. Demand for AI-based Digital Pathology in UK
    • 9.4.2.2. Demand for AI-based Digital Pathology in Germany
    • 9.4.2.3. Demand for AI-based Digital Pathology in Spain
    • 9.4.2.4. Demand for AI-based Digital Pathology in Italy
    • 9.4.2.5. Demand for AI-based Digital Pathology in France
  • 9.4.3. Demand for AI-based Digital Pathology in Asia
    • 9.4.3.1. Demand for AI-based Digital Pathology in China
    • 9.4.3.2. Demand for AI-based Digital Pathology in Japan
    • 9.4.3.3. Demand for AI-based Digital Pathology in South Korea
  • 9.4.4. Demand for AI-based Digital Pathology in Latin America
    • 9.4.4.1. Demand for AI-based Digital Pathology in Brazil
  • 9.4.5. Demand for AI-based Digital Pathology in MENA
    • 9.4.5.1. Demand for AI-based Digital Pathology in Saudi Arabia
  • 9.4.6. Demand for AI-based Digital Pathology in Rest of the World
    • 9.4.6.1. Demand for AI-based Digital Pathology in Australia
• 9.5. Demand for AI-based Digital Pathology: Analysis by Type of End-user
  • 9.5.1 Demand for AI-based Digital Pathology in Hospitals
  • 9.5.2. Demand for AI-based Digital Pathology in Research Institutes
  • 9.5.3. Demand for AI-based Digital Pathology in Other End-users
• 9.6. Concluding Remarks

10. MARKET SIZING AND OPPORTUNITY ANALYSIS

• 10.1. Chapter Overview
• 10.2. Forecast Methodology and Key Assumptions
• 10.3. Global AI-based Digital Pathology Market, 2022-2035
• 10.4. AI-based Digital Pathology Market: Analysis by Type of Neural Network, 2022 and 2035
  • 10.4.1. AI-based Digital Pathology Market for Artificial Neural Network, 2022-2035
  • 10.4.2. AI-based Digital Pathology Market for Convolutional Neural Network, 2022-2035
  • 10.4.3. AI-based Digital Pathology Market for Fully Convolutional Network, 2022-2035
  • 10.4.4. AI-based Digital Pathology Market for Recurrent Neural Network, 2022 - 2035
  • 10.4.5. AI-based Digital Pathology Market for Other Neural Networks, 2022 - 2035
• 10.5. AI-based Digital Pathology Market: Analysis by Type of Assay, 2022 and 2035
  • 10.5.1. AI-based Digital Pathology Market for ER Assay, 2022-2035
  • 10.5.2. AI-based Digital Pathology Market for HER2 Assay, 2022-2035
  • 10.5.3. AI-based Digital Pathology Market for Ki67 Assay, 2022-2035
  • 10.5.4. AI-based Digital Pathology Market for PD-L1 Assay, 2022-2035
  • 10.5.5. AI-based Digital Pathology Market for PR Assay, 2022-2035
  • 10.5.6. AI-based Digital Pathology Market for Other Type of Assays, 2022-2035
• 10.6. AI-based Digital Pathology Market: Analysis by Type of End-user, 2022 and 2035
  • 10.6.1. AI-based Digital Pathology Market for Academic Institutions, 2022-2035
  • 10.6.2. AI-based Digital Pathology Market for Hospitals / Healthcare Institutions, 2022-2035
  • 10.6.3. AI-based Digital Pathology Market for Laboratories / Diagnostic Institutions, 2022-2035
  • 10.6.4. AI-based Digital Pathology Market for Research Institutes, 2022-2035
  • 10.6.5. AI-based Digital Pathology Market for Other End-users, 2022-2035
• 10.7. AI-based Digital Pathology Market: Analysis by Area of Application, 2022 and 2035
  • 10.7.1. AI-based Digital Pathology Market for Diagnostics, 2022-2035
  • 10.7.2. AI-based Digital Pathology Market for Research, 2022-2035
  • 10.7.3. AI-based Digital Pathology Market for Other Areas of Application, 2022-2035
• 10.8. AI-based Digital Pathology Market: Analysis by Target Disease Indication, 2022 and 2035
  • 10.8.1. AI-based Digital Pathology Market for Breast Cancer, 2022-2035
  • 10.8.2. AI-based Digital Pathology Market for Colorectal Cancer, 2022-2035
  • 10.8.3. AI-based Digital Pathology Market for Cervical Cancer, 2022-2035
  • 10.8.4. AI-based Digital Pathology Market for Gastrointestinal Cancer, 2022-2035
  • 10.8.5. AI-based Digital Pathology Market for Lung Cancer, 2022-2035
  • 10.8.6. AI-based Digital Pathology Market for Prostate Cancer, 2022-2035
  • 10.8.7. AI-based Digital Pathology Market for Other Indications, 2022-2035
• 10.9. AI-based Digital Pathology Market: Analysis by Key Geographies, 2022 and 2035
  • 10.9.1. AI-based Digital Pathology Market in North America, 2022-2035
    • 10.9.1.1. AI-based Digital Pathology Market in the US, 2022-2035
    • 10.9.1.2. AI-based Digital Pathology Market in Canada, 2022-2035
  • 10.9.2. AI-based Digital Pathology Market in Europe, 2022-2035
    • 10.9.2.1. AI-based Digital Pathology Market in UK, 2022-2035
    • 10.9.2.2. AI-based Digital Pathology Market in Germany, 2022-2035
    • 10.9.2.3. AI-based Digital Pathology Market in Spain, 2022-2035
    • 10.9.2.4. AI-based Digital Pathology Market in Italy, 2022-2035
    • 10.9.2.5. AI-based Digital Pathology Market in France, 2022-2035
  • 10.9.3. AI-based Digital Pathology Market in Asia, 2022-2035
    • 10.9.3.1. AI-based Digital Pathology Market in China, 2022-2035
    • 10.9.3.2. AI-based Digital Pathology Market in Japan, 2022-2035
    • 10.9.3.3. AI-based Digital Pathology Market in South Korea, 2022-2035
  • 10.9.4. AI-based Digital Pathology Market in Latin America, 2022-2035
    • 10.9.4.1. AI-based Digital Pathology Market in Brazil, 2022 - 2035
  • 10.9.5. AI-based Digital Pathology Market in MENA, 2022-2035
    • 10.9.5.1. AI-based Digital Pathology Market in Saudi Arabia, 2022-2035
  • 10.9.6. AI-based Digital Pathology Market in Rest of the World, 2022-2035
    • 10.9.6.1. AI-based Digital Pathology Market in Australia, 2022-2035

11. CONCLUDING REMARKS

12. EXECUTIVE INSIGHTS

• 12.1. Chapter Overview
• 12.2. aetherAI
  • 12.2.1. Company Snapshot
  • 12.2.2. Interview Transcript: Joe Yeh (Chief Executive Officer and Chairman)
• 12.3. CTL Clinitech Lab
  • 12.3.1. Company Snapshot
  • 12.3.2. Interview Transcript: Suraj Bramhane (Laboratory Director and Chief Pathologist)
• 12.4. Huron Digital Pathology
  • 12.4.1. Company Snapshot
  • 12.4.2. Interview Transcript: Savvas Damaskinos (Vice President, Research and Technology)
• 12.5. Mindpeak
  • 12.5.1. Company Snapshot
  • 12.5.2. Interview Transcript: Anil Berger (Vice President, Sales and Marketing)
• 12.6. Pramana
  • 12.6.1. Company Snapshot
  • 12.6.2. Interview Transcript: Scott Wallace (Vice President, Business Development and Strategic Partnerships)

13. APPENDIX 1: TABULATED DATA

14. APPENDIX II: LIST OF COMPANIES AND ORGANIZATION

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