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 / AI 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 pathology technique 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 / AI 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 / AI 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.
Frequently Asked Questions
- Who are the leading players engaged in offering AI-based digital pathology / AI 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 / AI pathology market?
- How is the current and future opportunity likely to be distributed across key market segment?
Please note: This report can be updated on request. Please contact our Customer Experience team using the Ask a Question widget on our website.
Table of Contents
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- 3D HISTECH
- 50 Partners
- 83North
- 8VC
- ACME Investments
- Act Venture Capital
- Adage
- aetherAI
- Agilent
- Agilent Technologies
- Aiforia
- Akoya Biosciences
- Aktia Nordic Micro Cap
- Alpha Intelligence Capital
- Alverno Laboratories
- aMoon
- AnaPath
- Angels Santé
- APEX Ventures
- Ascend Capital Partners
- ATP
- Augmentiqs
- Augsburg University Hospital
- Aventior
- Axon Diagnostics
- BankInvest
- Bayern Kapital
- BGV
- Biospring Partners
- BioView
- Blue Venture Fund
- Breyer Capital
- Bristol Myers Squibb
- C.L. Davids Fond
- CADESS
- Casdin Capital
- Catalio Capital Management
- CellCarta
- Charles River Laboratories
- Cleveland Clinic
- Corista
- Crosscope
- Clinitech Laboratory
- D1 Capital Partners
- Danhua Capital
- Databricks
- Datavant
- Deciphex
- Dedalus
- Deep Bio
- DeePathology.ai
- Dell
- DHVC
- Diagnexia
- Digital Pathology Lab
- Dr. Lal PathLabs
- Emerald Development Managers
- Enterprise Ireland
- Entrepreneurs Investis
- EOSIN Microscopic Intelligence
- Epredia
- Fairhaven Capital
- FFEI
- Flagship Biosciences
- Fluidigm
- Flybridge Capital Partners
- Fonds Ambition Amorçage Angels
- Fusion Fund
- General Atlantic
- General Catalyst
- Genuv
- Gestalt Diagnostics
- GI Partners
- GlaxoSmithKline
- Glencoe Software
- GNI Group
- Godman Sachs Merchant Banking division
- Grey Slide Imaging
- Halo Business Angel Network
- Hamamatsu Photonics
- HBAN MedTech Syndicate HealthCare VenturesHealthQuest Capital
- Highline Capital Management
- High-Tech Gründerfonds
- HistoIndex
- Hitachi Ventures
- Hologic
- Hospitex International
- Huron Digital Pathology
- Ibex Medical Analytics
- ImaBiotech
- Imsight
- Indica Labs
- iCAIRD
- INFINITT
- Inform Diagnostics
- Innovationsstarter Fonds HamburgInnovatus Capital Partners
- Inovata Personalised Health Accelerator
- Inspirata
- Intel
- Invicro
- Irrus Investments
- Janssen
- JiNan Danjier Electronics
- JLK
- Johnson & Johnson Innovation
- Kaiser Permanente
- Kamet Ventures
- KdT Ventures
- Kenan Turnacioglu
- KFBIO
- KKR
- LabCorp
- Leica Biosystems
- Lumea
- Lunaphore
- Mayo Clinic
- Mechanomind
- Merck
- microDimensions
- Mindpeak
- Motic Digital PathologyMotu Ventures
- NantOmics
- National Cancer Institute
- National Science Foundation
- NextStep Ventures NGD-Lab
- Nina Capital
- NorthCap UniversityNovartis
- Octopus Ventures
- OneCell
- Ontario Centres of Excellence
- Opta-Tech
- OptraScan
- OracleBio
- Orange Digital Ventures
- Oxford University Innovation
- Paige
- Parapixel
- Parkwalk Advisors
- Partech
- PathAI
- Pathan
- Pathcore
- PereDoc
- Perspectum
- PHC
- Philips
- Pictor Labs
- Pillar VC
- Piper Jaffray Merchant Banking
- Planven Entrepreneur Ventures
- Plug and PlayPolaris Partners
- Poplar Healthcare
- PP Capital
- Pramana
- Primaa
- PROSCIA
- Puhua Capital
- Quest Diagnostics
- Razor’s Edge Ventures
- Refactor Capital
- Reveal Biosciences
- Robin Hood Ventures ROBO Global
- Roche Tissue Diagnostics
- RT Capital Management
- Sana Kliniken Berlin-Brandenburg
- Scale Venture Partners
- Sectra
- Sentient Med
- Siemens Healthineers
- SigTuple
- Smart In MediaSonora Quest Laboratories
- STO-Rahoitus
- S V Market
- Targos
- TechcyteTelegraph Hill Partners
- Tiger Global Management
- Triangle Peak Partners
- Tribun Health
- U.S. Department of Energy
- Ultivue
- Unilabs
- UNIM
- University of Edinburgh
- UofL HealthUniversity of Oxford
- US Department of Energy
- Vækstfonden
- ViQi
- Visiopharm
- Western Digital Capital
- WSK Medical
- XIFIN
- ZayaAI
- Zegami
Methodology
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