AI in Pathology Market Report Scope & overview:

The AI in Pathology Market size was valued at USD 1050.18 million in 2023 and is expected to grow to USD 1915.21 million by 2031 and grow at a CAGR Of 7.8% over the forecast period of 2024-2031.

With the help of the combination of artificial intelligence pathology, pathologists may now adopt image analytics by analyzing more slides in a shorter amount of time. This can assist pathologists in narrowing their focus and enhancing the effectiveness of their results. With devices and applications having access to electronic health records, radiology pictures, and other data, the use of artificial intelligence in pathology will change the whole patient experience and encourage greater patient engagement.

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MARKET DYNAMICS

KEY DRIVERS:

• Enhancements in computer Power and Cloud Infrastructure

• Deep learning algorithms

Deep learning algorithms and computer vision techniques have significantly improved in recent years, making it possible for AI models to analyze and interpret medical images with ever-increasing accuracy. There are now more applications for AI in pathology because of the creation of complex AI algorithms that can spot patterns and anomalies in pathology images.

RESTRAIN:

• Data quality and standardization

• Cost factors

The creation or procurement of AI software, hardware infrastructure, and continuous maintenance can all be quite expensive when using AI systems in pathology. To justify their adoption, AI systems must carefully consider their return on investment and cost-effectiveness.

OPPORTUNITY:

•  Workflow Automation

• Data mining and research

AI systems can examine massive databases, including genomic data and electronic medical records, to find patterns, biomarkers, and new relationships. This promotes pathology research efforts and aids in the creation of innovative diagnostic and therapy strategies.

CHALLENGES:

• Transparency and comprehensibility

• Limited data availability for rare diseases

For AI algorithms to perform at their best, they normally need a lot of labeled data. Nevertheless, it can be difficult to get enough labeled data for rare diseases or conditions. Collaborations across healthcare organizations, data-sharing programs, and the creation of transfer learning methodologies to draw on information from related disease fields are all part of the solution to this problem.

IMPACT OF COVID-19

The rapid adoption of AI-based techniques in pathology has been spurred by the need to effectively diagnose and manage COVID-19 cases. To help with the detection and categorization of COVID-19-related pathology findings, such as lung abnormalities on chest radiographs or CT scans, AI algorithms have been created and put to use. The creation of AI models has been employed extensively in COVID-19 research to analyze a sizable amount of histopathology and medical imaging data. Understanding the pathology of the disease and prospective treatments has been made possible thanks to AI algorithms' assistance in recognizing patterns and features in pathology images linked to COVID-19 infection. The pandemic has interfered with research and development in the market for AI in pathology. several clinical trials

IMPACT OF RUSSIA-UKRAINE WAR

The war and its aftermath may cause the international supply chains that support the AI in pathology sector to be disrupted. The development of software and the manufacturing of electrical components both heavily involve Ukraine. Any regional disturbances could cause delays or a lack of essential parts needed for AI pathology systems. Political unrest and hostilities frequently result in economic turbulence, which has an impact on stock market behavior and investment choices. In such a setting, businesses could be reluctant to spend money on AI pathology solutions, which would hinder market expansion or cause it to migrate to more secure geographical areas. The war's effects on the AI pathology market.

IMPACT OF ONGOING RECESSION

During a downturn, money for research and development including AI in pathology may be in short supply. Spending priorities may cause businesses and organizations to cut back on investments in developing technology. This might slow down the creation and uptake of AI applications in pathology. Budget reductions in many industries, including healthcare, are common during recessions. Financial limitations might affect the willingness and capacity of pathology departments to invest in AI technologies. The market for AI in pathology may grow more slowly due to decreased demand.

KEY MARKET SEGMENTATION

By Neural Network Type

• Convolutional Neural Networks

• Recurrent Neural Networks

• Generative Adversarial Networks

• MVPNet

• Reinforced Auto Zoom Net

By Product Type

• Scanners

• Software

• Communication Systems

• Storage Systems

By Type

• Human Pathology

• Veterinary Pathology

By End User

• Pharmaceutical and Biotechnology Companies

• Hospitals and Reference Laboratories

• Academic and Research Institutes

By Application

• Teleconsultation

• Disease Diagnosis

• Drug Discovery

• Training and Education

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REGIONAL COVERAGE:

North America

• USA

• Canada

• Mexico

Europe

• Germany

• UK

• France

• Italy

• Spain

• The Netherlands

• Rest of Europe

Asia-Pacific

• Japan

• South Korea

• China

• India

• Australia

• Rest of Asia-Pacific

The Middle East & Africa

• Israel

• UAE

• South Africa

• Rest of the Middle East & Africa

Latin America

• Brazil

• Argentina

• Rest of Latin American

Key Players:

The Major Players are Roche, Leica Biosystems, Hamamatsu Photonics, Koninklijke Philips, 3D Hsitech, Apollo Enterprises Imaging, Xifin,  Huron Digital Pathology, Visionpharm, Corista, Indica Labs, Objective Pathology Services, and other players listed in the final report.

Hamamatsu Photonics-Company Financial Analysis