AI in Pathology Market is expected to grow to $49 million by 2028, at a CAGR of 15.6%. This study provides detailed insights into industry trends, pricing strategies, patent reviews, and stakeholder perspectives from conferences. AI algorithms help in analyzing digital pathology images, detecting and classifying abnormalities like tumours and cancerous cells, resulting in more accurate diagnoses. The benefits include improved efficiency, patient care, and diagnostic accuracy. Market growth is driven by increasing demand for advanced technologies, rising concerns about misdiagnoses, and a focus on cost control, though operational costs and interoperability pose challenges.

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Software segment accounted for the largest share of the AI in pathology market, By component.

Based on component, the software segment accounted for the largest share of the global AI in pathology market in 2022. The large share of this segment can be attributed to pathologists’ high adoption of AI-based software due to factors such as high adaptability and interoperability and automation of various tasks in pathology, such as image analysis, data extraction, and report generation. These driving factors are shaping the adoption and development of AI-based software for pathology, offering significant potential for advancements in disease detection, diagnosis, and treatment planning. For instance, in December 2021, F. Hoffmann-La Roche Ltd. (Switzerland) launched its artificial intelligence (AI)-based digital pathology software to help pathologists evaluate breast cancer markers such as Ki-67, ER, and PR.

The drug discovery segment is expected to witness the highest growth rate in the AI in pathology market by application.

Based on application, the AI in pathology market is segmented into drug discovery, disease diagnosis and prognosis, clinical workflow, and training & education. The drug discovery segment is estimated to grow at the highest CAGR, during the forecast period. The growth in high throughput screening and imaging, increasing use of AI that is benefitting toxicology testing for illicit drugs, rising pharmaceutical & biotechnology R&D expenditure, and the ability of AI in pathology to accelerate the development of new therapeutics, improve diagnostic accuracy, and enhance personalized medicine approaches are the major factors responsible for the large share and high growth rate of the drug discovery application segment.

North America dominated the AI in pathology market in 2022.

The global market has been segmented based on region: North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. North America held the largest share of the AI in pathology market in 2022, followed by Europe and Asia Pacific. This market is also projected to grow at the highest CAGR. The large share and high growth rate of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US, this region has always been at the forefront of implementing advanced technologies and integrated AI systems within the pathology labs, factors such as increasing need to enhance efficiency of labs, growing cases of misdiagnoses, rise in use of telepathology with AI advancements have also supported the growth of this market in North America.

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AI in Pathology Market Dynamics:

Drivers:

1. Technology advancements in deep learning have enabled a synergy with artificial intelligence (AI) in pathology space

Restraints:

1. High cost of digital pathology systems

Opportunities:

1. Shortage of skilled pathologists

Challenges:

1. Lack of sufficient data to train the AI algorithms

Key Market Players:

The key players functioning in the AI in pathology market include Koninklijke Philips N.V. (Netherlands), F. Hoffmann-La Roche Ltd (Switzerland), Hologic, Inc. (US), Akoya Biosciences, Inc. (US), Aiforia Technologies Plc (Finland), Indica Labs Inc. (US), OptraScan (US), Ibex Medical Analytics Ltd. (Israel), Mindpeak GmbH (Germany), Tribun Health (France), Techcyte, Inc. (US), Deep Bio Inc. (Korea), Lumea Inc. (US), Visiopharm (Denmark), aetherAI (Taiwan), Aiosyn (Netherlands), Paige AI, Inc. (US), Proscia Inc. (US), PathAI, Inc. (US), Tempus Labs, Inc. (US), Konfoong Biotech International Co., Ltd. (China), DoMore Diagnostics AS (Norway), Verily Life Sciences, LLC (US), deepPath (US), and 4D Path Inc (US).

Recent Developments:

• In April 2023, Indica Labs Inc. (US) signed an agreement with Lunit Inc. (South Korea). The agreement helped to provide a fully interoperable solution between Indica Labs’ HALO AP image management software platform and Lunit’s suite of AI pathology products.
• In March 2022, Ibex Medical Analytics Ltd. (Isarel) had partnered with Dedalus Group (Italy). Through this partnership, the company aimed to bring the power of artificial intelligence to digital pathology.
• In January 2022, Aiforia Technologies Plc (Finland) collaborated with Mayo Clinic (US). Under this collaboration, AI-powered pathology research support architecture was established at the Mayo Clinic to enable faster results and scalable studies in translational research.
• In December 2021, F. Hoffmann-La Roche Ltd. (Switzerland) launched its artificial intelligence (AI)-based digital pathology algorithms to help pathologists evaluate breast cancer markers such as Ki-67, ER, and PR.

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AI in Pathology Market Advantages:

• Improved Accuracy: AI algorithms can analyze large volumes of medical data with high precision and accuracy. They can detect subtle patterns, anomalies, and features in pathology images that might be difficult for human pathologists to identify. This leads to improved diagnostic accuracy and reduces the chances of errors.
• Enhanced Efficiency: AI algorithms can process pathology images at a much faster rate than humans. They can analyze and interpret large volumes of data in a fraction of the time it would take a human pathologist. This increased efficiency allows for quicker turnaround times in diagnoses, enabling faster patient treatment and management.
• Standardization of Diagnoses: Pathology diagnoses can sometimes be subjective, as different pathologists may interpret images differently. AI algorithms can help standardize diagnoses by providing consistent and objective assessments. This can lead to more uniform and reliable diagnoses, reducing inter-observer variability.
• Augmented Decision Support: AI algorithms can act as decision support tools for pathologists. They can provide relevant information, suggestions, and recommendations based on their analysis of pathology images. This assists pathologists in making more informed decisions and can help improve patient outcomes.
• Predictive Analytics: AI algorithms can analyze pathology data over time, identify patterns, and make predictions about disease progression, treatment response, and patient outcomes. This predictive analytics capability can aid in personalized medicine, allowing for tailored treatment plans and interventions.
• Education and Training: AI algorithms can be used as educational tools for pathologists in training. They can provide case examples, highlight important features, and offer guidance during the learning process. This can help improve the knowledge and skills of pathologists, especially in challenging or rare cases.
• Cost Reduction: By increasing efficiency and accuracy, AI in pathology has the potential to reduce healthcare costs. Faster diagnoses and optimized treatment plans can lead to better resource utilization and more effective allocation of healthcare resources.

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