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

NEW YORK, Dec. 15, 2022 /PRNewswire/ --

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.



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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.

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