AI in Pathology Market, valued at US$71.7 million in 2023, is forecasted to grow at a robust CAGR of 15.4%, reaching US$82.8 million in 2024 and an impressive US$169.8 million by 2029. Al algorithms can now be applied to scanning digital pathology images to identify and characterize different anomalies such as tumors, cancerous cells, and structures in tissue that could help pathologists identify and quantify the critical features that could aid in better diagnosis and the decision to start treatment. AI in pathology provides several advantages, such as accurate results, efficiency, and better patient care. This system can help pathologists to detect and characterize abnormalities, make diagnoses, and predict the outcomes of the disease through large datasets and algorithms that are trained by annotated pathology images.
Integration of AI in multiplex imaging, adoption of cloud-based AI platforms in pathology, increasing use of automated AI-assisted QC systems, and rising FDA approvals for AI/ML systems are some of the major factors accelerating market growth. Some of the other growth drivers for the market include a growing demand for higher technology solutions, rising levels of misdiagnoses, increased funding programs to improve the standard of care towards patients, and a growing emphasis on cost containment along with hospital operation efficiency.
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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).
DRIVER: Increasing cases of misdiagnosis lead to a growing need for AI in pathology
Misdiagnosis can occur when a disease or condition is either missed, misclassified, or delayed in its diagnosis. This has been such a big problem in healthcare since it leads to the provision of suboptimal and late treatment and also harm to patients, as well as increased healthcare costs. Prostate cancer, breast cancer, and thyroid cancer have proven to be among the most common cancers that are being misdiagnosed. According to a journal published in 2023 by BMJ-Quality & Safety, it is estimated that an estimated total of 800,000 Americans have wrong diagnoses yearly causing the death of 371,000 patients and permanent disabilities to 424,000.
The most common reasons for the misdiagnoses included the interpretation of scans by general pathologists instead of subspecialists, non-ordering of the necessary follow-up test by physicians, and errors made by doctors in interpreting the test results. Artificial intelligence, although still in its infancy in clinical practice, could help with the rising workloads of pathologists while simultaneously providing consistent and timely diagnostic care.
RESTRAINT: High cost of digital pathology systems
One of the reasons for low adoption of Al is a high upfront investment in establishing digital pathology systems: Al often depends on such systems for data acquisition and analysis. A typical digital pathology system will include a slide scanner, an image server, and the relevant software. These can often cost between USD 500,000 and USD 1,500,000. A full-featured scanner itself costs around USD 250,000. The average price of a digital pathology scanner in the Asia Pacific is around USD 110,000 to USD 130,000. Although large hospitals with good capital budgets can afford these systems, not very many pathologists or institutes have limited budgets or even IT support. Healthcare providers are financially constrained much more to invest in expensive technologies like digital pathology, which is most prominently found in emerging countries like India, Brazil, and Mexico. Furthermore, skilled manpower is required for the smooth management and servicing of these digital pathology systems. The prices of the digital pathology systems are supplemented by the lack of scarcity of skilled manpower to operate the digital pathology systems which is most likely to limit the adoption of the systems. Without the adoption of these systems, Al will not be able to get incorporated into pathology.
There are only a few labs in the world that are totally digital now. Some nations, such as the Netherlands or Scandinavian countries, are ahead of the curve in this area. However, in Germany or the US, the transition to digital pathology in the clinical setting is moving at a glacial pace, and so is the adoption of AI in pathology.
OPPORTUNITY: Growing demand for personalized medicine
Personalized medicine offers tailored treatments and prevention procedures to each individual's condition for maximum results. The adoption of this kind of medicine is on the rise due to an increase in the population aged at 65 years and above besides an increase incidence of diseases like cancer. For all these disease conditions, personalized medicine offers the opportunity for targeted therapies aimed to destroy tumor cells with minimal impact on healthy cells. According to this report, out of the 48 novel therapeutic molecular entities approved by the US FDA in 2021, 17, alongside two new cell-based therapies, were personalized medicine. Around 47% of these newly accepted personalized therapies were indicated for the treatment of cancers. A few of the important developments concerning the application of AI in pathology for personalized medicine are listed below:
- In September 2024, Paige Al, Inc. launched Paige Alba, an Al tool developed as a clinical-grade multimodal co-pilot to advance personalized medicine and precision oncology. It provides real-time Al-driven insights for patients.
- In March 2022, Ibex Medical Analytics Ltd. partnered with Dedalus Group. Through this partnership, the company can bring the power of artificial intelligence to digital pathology.
In personalized medicine, pathological data is required to prescribe the right therapy at the right time. AI in pathology systems offers companion algorithms for specimen slide diagnosis, which helps detect cancer cells and enable early diagnosis. Thus, the high demand for personalized medicine offers significant growth opportunities for AI in the pathology market.
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.
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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