Stratistics MRC에 따르면 세계의 병리학용 AI 진단 자동화 시장은 2025년 8,697억 달러를 차지하고, 예측 기간 동안 CAGR 20.8%로 성장해, 2032년까지 3조 2,648억 달러에 이를 것으로 예측됩니다.
병리학용 AI 진단 자동화는 인공지능을 사용하여 병리 영상을 분석하고, 워크플로우를 합리화하고, 진단 판단을 지원합니다. 머신러닝과 디지털 병리학 도구를 통합하면 병리학자가 더 빠르고 정확하게 질병을 감지할 수 있어 궁극적으로 환자 결과를 개선하고 현대 건강 관리에서 확장성이 뛰어나고 데이터 중심 진단을 가능하게 합니다.
The Guardian에 따르면, 케임브리지 대학의 AI 알고리즘은 4,000장 이상의 십이지장 생검 이미지를 분석하고, 인간의 병리의가 1건당 5-10분 걸리는 것에 비해 거의 순식간에 세리악병을 진단했습니다.
디지털 병리 진단의 도입 확대
의료기관은 진단 정확도와 워크플로우 효율을 높이기 위해 홀 슬라이드 이미징 스캐너와 디지털 인프라에 대한 투자를 늘리고 있습니다. 또한 디지털 병리학은 머신러닝 모델이 교육 및 검증에 디지털화된 병리 조직 이미지를 필요로 하기 때문에 AI 알고리즘의 전개에 필수적인 기반을 구축합니다.
표준화된 데이터 부족
표준화된 데이터 프로토콜의 부족은 병리학용 AI의 도입에 큰 과제를 가져옵니다. 이 때문에 정확한 AI 알고리즘에 필요한 강인한 교육 데이터세트의 개발이 방해되고 있습니다.
멀티오믹스 데이터 및 정밀의료와의 통합
AI 병리학과 멀티오믹스 데이터의 융합은 개별화 헬스케어의 제공에 전례가 없는 기회를 가져옵니다. 특히 정밀의료 접근법이 점차 채택되고 있는 종양학 응용 분야에서 가치가 높습니다.
데이터 편향 및 일반화 가능성 문제
데이터의 편향은 병리학용 AI의 보급에 심각한 위협이 됩니다. 인구통계학적 및 의료기관에 의해 질병상에 변동이 있으면 AI 모델은 특정 환경에서는 뛰어난 성능을 발휘하지만, 다양한 임상 환경에서 전개하면 실패할 가능성이 있습니다. 다양성이 없는 것은 기존의 헬스케어 격차를 영속시켜 AI 솔루션의 세계적인 적용성을 제한할 가능성이 있습니다.
COVID-19의 유행은 헬스케어 시스템이 물리적인 접촉을 최소한으로 억제하면서 진단 서비스를 유지하려고 했기 때문에 디지털 병리학과 AI 기술의 채용을 가속시켰습니다. 게다가 팬데믹은 병리의 위기적인 부족을 부각시키고, 증대하는 작업량을 효율적으로 처리하기 위한 자동 진단 툴의 필요성을 부각했습니다.
예측 기간 동안 소프트웨어 부문이 최대가 될 전망
병리 진단 자동화에서 AI 알고리즘과 분석 플랫폼의 기본적인 역할을 통해 소프트웨어 부문이 예측 기간 동안 최대 시장 점유율을 차지할 것으로 예측됩니다. 자동화된 이미지 해석, 패턴 인식, 진단 지원에 대한 수요 증가가 소프트웨어 개발에의 다액의 투자를 뒷받침하고 있습니다. 또한, 계속적인 알고리즘의 개량과, 다양한 병리학적 상태에 특화한 용도의 개발이, 이 부문의 압도적인 시장 포지션에 공헌하고 있습니다.
예측 기간 동안 클라우드 기반 부문이 가장 높은 CAGR이 예상됩니다.
예측 기간 동안 확장 가능하고 접근성이 뛰어나고 비용 효율적인 AI 병리학 솔루션에 대한 요구는 클라우드 기반 부문이 가장 높은 성장률을 보일 것으로 예측됩니다. 또한 클라우드 기반 시스템은 병리학 의사 간의 원활한 협업을 촉진하고 원격 컨설팅을 가능하게하고 AI 모델 교육에 필요한 대규모 병리 조직 학적 데이터 세트 공유를 지원합니다.
예측 기간 동안 북미는 첨단 건강 관리 인프라, 충실한 R&D 투자, AI 의료기기에 대한 양호한 규제 환경으로 인해 최대 시장 점유율을 차지할 것으로 예측됩니다. 이 지역은 임상진단용 AI 도입을 촉진하는 ARPA-H와 같은 조직에 의한 자금 제공 프로그램 등, 정부의 강력한 이니셔티브의 혜택을 받고 있으며, 주요 기술 기업의 존재와 건강 관리 제공업체와 AI 개발자 사이에 확립된 파트너십이 시장 개척을 가속화하고 있습니다.
예측 기간 동안 아시아태평양이 가장 높은 CAGR을 나타낼 것으로 예측됩니다. 이는 건강 관리 지출 증가, 디지털 인프라 확대, 의료 진단에서 AI 용도의 인지도 향상으로 인한 것입니다. 중국, 일본, 인도 등 국가는 증대하는 질병 부담과 병리학 부족에 대처하기 위해 AI 병리 솔루션을 포함한 건강 관리 엄청난 투자를 하고 있으며, 이 지역의 대규모 환자 집단은 AI 모델의 훈련과 검증을 위한 광범위한 데이터 세트를 제공하고, 지역에 뿌리를 둔 알고리즘 개발의 기회를 낳고 있습니다.
According to Stratistics MRC, the Global AI in Pathology - Diagnostic Automation Market is accounted for $869.7 billion in 2025 and is expected to reach $3264.8 billion by 2032 growing at a CAGR of 20.8% during the forecast period. AI in Pathology-Diagnostic Automation uses artificial intelligence to analyze pathology images, streamline workflows, and support diagnostic decisions. It automates repetitive tasks like slide screening and image quantification, improving accuracy and efficiency. By integrating machine learning with digital pathology tools, it helps pathologists detect diseases faster and with greater precision, ultimately enhancing patient outcomes and enabling more scalable, data-driven diagnostics in modern healthcare.
According to The Guardian, a University of Cambridge AI algorithm analysed 4,000+ duodenal biopsy images and diagnosed coeliac disease almost instantly, compared to the 5-10 minutes a human pathologist takes per case.
Increasing adoption of digital pathology
Healthcare institutions are increasingly investing in whole slide imaging scanners and digital infrastructure to enhance diagnostic accuracy and workflow efficiency. This transformation enables pathologists to analyze tissue samples remotely, facilitating telepathology consultations and second opinions across geographical boundaries. Furthermore, digital pathology creates the essential foundation for AI algorithm deployment, as machine learning models require digitized histopathological images for training and validation. The integration of AI with digital pathology platforms significantly reduces diagnostic review time while improving consistency in pathological assessments.
Lack of standardized data
The absence of standardized data protocols poses a significant challenge to AI implementation in pathology diagnostics. Variability in tissue preparation, staining procedures, and imaging parameters across different laboratories creates inconsistencies that can compromise AI model performance. Additionally, the lack of uniform annotation standards for pathological images hinders the development of robust training datasets required for accurate AI algorithms. Moreover, the scarcity of high-quality, annotated datasets limits the effectiveness of deep learning models and their applicability across diverse patient populations and disease types.
Integration with multi-omics data and precision medicine
The convergence of AI pathology with multi-omics data presents unprecedented opportunities for personalized healthcare delivery. By combining histopathological image analysis with genomic, proteomic, and metabolomic information, AI systems can provide comprehensive disease characterization and treatment recommendations. This integration enables the identification of novel biomarkers and therapeutic targets, particularly valuable in oncology applications where precision medicine approaches are increasingly adopted. Furthermore, the growing emphasis on personalized medicine creates substantial market opportunities for AI solutions that can seamlessly integrate diverse data types to support clinical decision-making processes.
Data bias and generalizability issues
Data bias represents a critical threat to the widespread adoption of AI in pathology diagnostics, as algorithms trained on non-representative datasets may produce unreliable results across different patient populations. Geographic, demographic, and institutional variations in disease presentation can lead to AI models that perform well in specific settings but fail when deployed in diverse clinical environments. Additionally, the lack of diversity in training datasets can perpetuate existing healthcare disparities and limit the global applicability of AI solutions. Moreover, the "black box" nature of many AI algorithms raises concerns about transparency and explainability, making it difficult for pathologists to understand and trust AI-generated recommendations. These generalizability challenges can undermine confidence in AI systems and slow their clinical adoption.
The COVID-19 pandemic accelerated the adoption of digital pathology and AI technologies as healthcare systems sought to maintain diagnostic services while minimizing physical contact. Remote work requirements necessitated the implementation of telepathology solutions, enabling pathologists to review cases from home and collaborate virtually with colleagues. Furthermore, the pandemic highlighted the critical shortage of pathologists and the need for automated diagnostic tools to handle increased workloads efficiently. The crisis also drove investments in cloud-based pathology platforms and AI-powered diagnostic systems to ensure continuity of care during lockdowns and social distancing measures.
The software segment is expected to be the largest during the forecast period
The software segment is expected to account for the largest market share during the forecast period due to the fundamental role of AI algorithms and analytical platforms in pathology automation. Software solutions encompass image analysis algorithms, machine learning models, and diagnostic decision support systems that form the core of AI-powered pathology workflows. The increasing demand for automated image interpretation, pattern recognition, and diagnostic assistance drives substantial investment in software development. Additionally, continuous algorithm improvements and the development of specialized applications for various pathological conditions contribute to the segment's dominant market position.
The cloud-based segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by the need for scalable, accessible, and cost-effective AI pathology solutions. Cloud platforms enable healthcare institutions to access sophisticated AI algorithms without substantial upfront infrastructure investments, making advanced diagnostic tools available to smaller laboratories and resource-constrained settings. Furthermore, cloud-based systems facilitate seamless collaboration between pathologists, enable remote consultations, and support the sharing of large histopathological datasets required for AI model training. Additionally, cloud platforms support continuous algorithm updates and improvements, ensuring that users have access to the latest AI capabilities without manual software installations.
During the forecast period, the North America region is expected to hold the largest market share owing to the region's advanced healthcare infrastructure, substantial research and development investments, and favorable regulatory environment for AI medical devices. The region benefits from strong government initiatives, including funding programs from organizations like ARPA-H that promote AI implementation in clinical diagnostics. Additionally, the presence of leading technology companies and established partnerships between healthcare providers and AI developers accelerate market growth. The high adoption rate of digital pathology systems and the availability of skilled professionals further strengthen North America's market position.
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing healthcare expenditure, expanding digital infrastructure, and rising awareness of AI applications in medical diagnostics. Countries like China, Japan, and India are investing heavily in healthcare modernization initiatives that include AI pathology solutions to address growing disease burdens and pathologist shortages. Furthermore, the region's large patient population provides extensive datasets for AI model training and validation, creating opportunities for localized algorithm development. Government support for digital health initiatives and favorable policies for AI adoption in healthcare accelerate market expansion.
Key players in the market
Some of the key players in AI in Pathology - Diagnostic Automation Market include PathAI, Inc., Paige.AI, Inc., Aiforia Technologies Plc, Akoya Biosciences, Inc., Deep Bio, Inc., Ibex Medical Analytics Ltd., Proscia Inc., Indica Labs, Inc., Inspirata, Inc., Mindpeak GmbH, Tribun Health, OptraSCAN, Inc., aetherAI Co., Ltd., DoMore Diagnostics AS, Hologic, Inc., Roche Tissue Diagnostics, Google (Alphabet Inc.) and Microsoft.
In June 2025, PathAI, a global leader in artificial intelligence (AI) and digital pathology solutions announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for AISight(R) Dx*-its digital pathology image management system-for use in primary diagnosis in clinical settings. Building on the initial 510(k) clearance for AISight Dx(Novo) in 2022, this latest milestone underscores the platform's continuous innovation and PathAI's commitment to delivering enhanced capabilities as the product evolves.
In March 2025, Aiforia Technologies, a pioneer in AI-driven diagnostics in pathology, has announced a new partnership with PathPresenter. This collaboration aims to broaden the reach and adoption of Aiforia's AI-powered image analysis solutions by utilizing PathPresenter's comprehensive pathology workflow platform. By combining their distinct expertise in digital pathology, the companies aim to provide pathologists with enhanced diagnostic capabilities and streamlined end-to-end workflow management solutions.
In March 2025, Proscia(R), a software company leading pathology's transition to digital and AI, has secured $50M in funding, bringing its total raised to $130M. This investment follows Proscia's record-breaking growth in 2024. Proscia now counts 16 of the top 20 pharmaceutical companies among its users and is on track for 22,000+ patients to be diagnosed on its Concentriq(R) software platform each day.