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市場調查報告書
商品編碼
2127903
乳房攝影診斷AI市場規模、佔有率、成長、全球產業分析、區域洞察,2026-2034年預測AI in Breast Imaging Market Size, Share, Growth and Global Industry Analysis, Regional Insights and Forecast to 2026-2034 |
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由於人工智慧在乳癌篩檢中的應用日益廣泛、對早期疾病檢測的需求不斷成長,以及放射科醫生面臨日益繁重的影像工作量,全球乳房攝影影像人工智慧市場正經歷快速成長。報告顯示,2025年全球乳房攝影人工智慧市場規模為8.425億美元。預計該市場將從2026年的10.906億美元成長至2034年的86億美元,預測期內CAGR為29.45%。北美地區在2025年佔據市場主導地位,市場佔有率達41.99%,這主要得益於數位乳房X光攝影的普及、人工智慧驅動的診斷工作流程、有利的法規核准以及對醫療技術的大力投資。
乳房攝影影像人工智慧技術涵蓋先進的軟體解決方案及相關服務,透過提升病灶檢出率、乳房密度評估、工作流程優先排序和報告準確性,為乳房X光攝影、數位乳房斷層合成(DBT)、乳房超音波和磁振造影(MRI)提供支援。乳癌篩檢率的提高以及對更快、更準確診斷的需求不斷成長,持續推動市場擴張。
市場定義和範圍
人工智慧在乳房攝影影像領域的應用,將機器學習、電腦視覺和深度學習演算法整合到乳房攝影影像工作流程中,以提高診斷準確性和臨床效率。這些人工智慧解決方案能夠幫助放射科醫師檢測可疑病灶、評估緻密乳房組織、優先處理高風險病例並減少假陰性結果。
該市場涵蓋人工智慧軟體、實施和支援服務、雲端、本地部署和混合部署模式,以及電腦視覺、機器學習和自然語言處理等技術。其應用實例包括癌症檢測和分流、乳房密度評估、工作流程優先排序、報告生成支援以及醫院、影像中心、乳癌篩檢機構和研究機構的風險分層。
市場動態
促進因素
乳癌早期檢測日益成長的需求是市場成長的主要驅動力。乳癌率的上升、全國篩檢計畫的擴展以及對更早發現可治療癌症的需求,正促使醫院和影像中心採用人工智慧驅動的診斷工具。人工智慧透過識別細微異常、減少漏診並提高篩檢的整體效率,進而提昇放射科醫師的工作效率。
趨勢
一個關鍵的市場趨勢是,人工智慧驅動的診斷技術正日益普及,並被整合到乳房X光攝影、數位乳房斷層合成(DBT)、超音波和磁振造影(MRI)的工作流程中。人工智慧解決方案的功能已不再局限於病灶檢測,而是擴展到乳房密度評估、工作流程最佳化和個人化風險預測等領域。越來越多的法規核准和臨床實踐中成功的案例研究進一步加速這些技術在醫療保健系統中的應用。
阻礙因素
高昂的實施成本仍是限制市場發展的主要因素。實施人工智慧需要投資於與PACS/RIS系統的整合、雲端基礎設施、網路安全、工作流程重新設計、軟體維護以及放射科醫生培訓。有限的保險償付政策和高昂的實施成本繼續阻礙小規模醫療機構採用人工智慧技術。
機會
人工智慧驅動的3D乳房斷層合成(DBT)技術的廣泛應用帶來了巨大的成長機會。隨著醫療機構擴大利用DBT來改善乳房組織的可視化效果,對能夠分析大量3D影像資料的人工智慧工具的需求也迅速成長。人工智慧驅動的DBT技術不僅提高了診斷準確率,還能同時減少放射科醫師解讀影像所需的時間。
課題
高品質標註影像資料集的匱乏仍然是業界面臨的最大挑戰之一。開發精準的人工智慧演算法需要大量經過臨床驗證、專家檢驗的乳房X光攝影、超音波、MRI和DBT資料集,這使得模型開發極為耗時耗力。
依組件分類,到2025年,軟體領域成為市場成長的主要驅動力,這主要得益於PACS、雲端系統以及與乳房X光攝影工作站整合的AI診斷平台的廣泛應用。隨著實施和諮詢需求的成長,服務領域預計也將保持穩定成長。
就部署方式而言,基於雲端的部署方式憑藉擴充性、減少基礎設施投資、集中式軟體更新以及易於在企業內部署等優勢,引領了市場。
從技術角度來看,電腦視覺主導了市場。這是因為它在分析乳房X光攝影、數位乳房斷層合成成像(DBT)、超音波和磁振造影(MRI)影像以進行癌症檢測和病灶識別方面發揮著非常重要的作用。
依影像方式分類,乳房X光攝影和數位乳房斷層合成(DBT)佔據了最大的市場佔有率,因為它們廣泛用於常規乳癌篩檢計劃中。
就應用而言,癌症檢測和分診仍然是主要領域,因為越來越多的臨床證據表明,人工智慧可以提高癌症檢測率,同時減輕放射科醫生的工作。
依最終用戶分類,醫院和醫療保健系統佔據市場主導。這主要歸功於這些機構進行的大量診斷成像工作、其先進的數位基礎設施以及與人工智慧解決方案供應商不斷擴大的合作關係。
預計北美將引領全球乳房攝影市場,到2025年市場規模將達到3.538億美元,主要得益於人工智慧乳房X光乳房X光攝影和數位乳房斷層合成技術的廣泛應用、先進的醫療基礎設施以及美國食品藥物管理局(FDA)的積極核准。歐洲預計將實現強勁成長,這主要得益於全國範圍內系統性篩檢計畫的實施以及人工智慧診斷技術法規核准範圍的擴大。亞太地區主導將實現快速成長,這主要受中國、印度和日本乳癌防治意識的提高、診斷基礎設施的改善以及對人工智慧醫學影像技術投資的增加所推動。拉丁美洲以及中東和非洲地區預計將保持穩定成長,因為各國政府將加強癌症篩檢計畫並推動醫療現代化。
本報告對全球乳癌影像人工智慧市場進行了全面分析,涵蓋了2025年、2026年和2034年的市場規模預測,以及市場動態、關鍵促進因素、限制因素、機會、挑戰,並依組件、部署形式、技術、模式、應用和最終用戶進行了細分。報告還包括區域分析、競爭格局、公司概況、監管趨勢、併購、產品發布以及影響產業發展的最新技術進展。
The global AI in breast imaging market is witnessing rapid growth due to the increasing adoption of artificial intelligence in breast cancer screening, growing demand for early disease detection, and rising pressure on radiologists to manage expanding imaging workloads. According to the report, the global AI in breast imaging market size was valued at USD 842.5 million in 2025. The market is projected to grow from USD 1,090.6 million in 2026 to USD 8,600.0 million by 2034, exhibiting a CAGR of 29.45% during the forecast period. North America dominated the market with a 41.99% share in 2025, driven by widespread adoption of digital mammography, AI-enabled diagnostic workflows, favorable regulatory approvals, and strong investments in healthcare technology.
AI in breast imaging comprises advanced software solutions and related services that support mammography, digital breast tomosynthesis (DBT), breast ultrasound, and MRI by improving lesion detection, breast density assessment, workflow prioritization, and reporting accuracy. Increasing breast cancer screening rates and the growing need for faster, more consistent diagnosis continue to fuel market expansion.
Market Definition and Scope
AI in breast imaging integrates machine learning, computer vision, and deep learning algorithms into breast imaging workflows to improve diagnostic accuracy and clinical efficiency. These AI-powered solutions assist radiologists in detecting suspicious lesions, evaluating dense breast tissue, prioritizing high-risk cases, and reducing false-negative results.
The market covers AI software, implementation and support services, cloud-based, on-premise, and hybrid deployment models, along with technologies such as computer vision, machine learning, and natural language processing. Applications include cancer detection & triage, breast density assessment, workflow prioritization, reporting support, and risk stratification across hospitals, diagnostic imaging centers, breast screening facilities, and research institutes.
Market Dynamics
Drivers
The increasing demand for early breast cancer detection is the primary driver of market growth. Rising breast cancer incidence, expanding national screening programs, and the need to detect cancers at earlier and more treatable stages are encouraging hospitals and imaging centers to adopt AI-assisted diagnostic tools. AI enhances radiologists' productivity by identifying subtle abnormalities, reducing missed diagnoses, and improving overall screening efficiency.
Trends
A major market trend is the growing adoption of AI-powered diagnostic technologies integrated into mammography, DBT, ultrasound, and MRI workflows. AI solutions are evolving beyond lesion detection to include breast density assessment, workflow optimization, and personalized risk prediction. Increasing regulatory approvals and successful real-world clinical studies are further accelerating adoption across healthcare systems.
Restraints
High implementation costs remain a major restraint for the market. Deploying AI requires investment in PACS/RIS integration, cloud infrastructure, cybersecurity, workflow redesign, software maintenance, and radiologist training. Limited reimbursement policies and high deployment costs continue to discourage adoption among smaller healthcare providers.
Opportunities
The expansion of AI-assisted 3D breast tomosynthesis (DBT) presents significant growth opportunities. As healthcare providers increasingly adopt DBT for superior breast tissue visualization, demand for AI tools capable of analyzing large volumes of 3D imaging data is rising rapidly. AI-assisted DBT improves diagnostic accuracy while reducing radiologists' reading time.
Challenges
Limited availability of high-quality annotated imaging datasets remains one of the industry's biggest challenges. Developing accurate AI algorithms requires extensive clinically validated mammography, ultrasound, MRI, and DBT datasets with expert annotations, making model development time-consuming and expensive.
By component, the software segment dominated the market in 2025 owing to increasing adoption of AI-powered diagnostic platforms integrated with PACS, cloud systems, and mammography workstations. Services are expected to experience steady growth as implementation and consulting demand increases.
By deployment, the cloud-based segment led the market due to its scalability, lower infrastructure investment, centralized software updates, and easier enterprise-wide deployment.
By technology, computer vision dominated the market as it plays a critical role in analyzing mammography, DBT, ultrasound, and MRI images for cancer detection and lesion identification.
By modality, mammography and digital breast tomosynthesis (DBT) accounted for the largest market share owing to their widespread use in routine breast cancer screening programs.
By application, cancer detection & triage remained the leading segment due to increasing clinical evidence demonstrating AI's ability to improve cancer detection rates while reducing radiologists' workload.
By end user, hospitals & health systems dominated the market because of their high imaging volumes, advanced digital infrastructure, and growing partnerships with AI solution providers.
North America dominated the global AI in breast imaging market with a valuation of USD 353.8 million in 2025, supported by extensive adoption of AI-enabled mammography, digital breast tomosynthesis, advanced healthcare infrastructure, and favorable FDA approvals. Europe is expected to witness strong growth due to organized national screening programs and increasing regulatory acceptance of AI-enabled diagnostic technologies. Asia Pacific is projected to expand rapidly with growing breast cancer awareness, improving diagnostic infrastructure, and increasing investments in AI-based medical imaging across China, India, and Japan. Latin America and the Middle East & Africa are expected to experience steady growth as governments strengthen cancer screening programs and healthcare modernization initiatives.
Competitive Landscape
The global market is moderately fragmented, with companies focusing on regulatory approvals, AI innovation, workflow integration, and strategic partnerships. Major players include Hologic, Inc., GE HealthCare, Lunit Inc., RadNet (DeepHealth), ScreenPoint Medical B.V., CureMetrix, Clairity, Whiterabbit, Densitas Inc., and Therapixel. These companies continue expanding their AI portfolios through product innovation, cloud-based solutions, strategic collaborations, and clinical validation studies to strengthen their market positions.
Report Coverage
The report provides a comprehensive analysis of the global AI in breast imaging market, covering market size for 2025, 2026, and 2034, along with market dynamics, key drivers, restraints, opportunities, challenges, segmentation by component, deployment, technology, modality, application, and end user. It also includes regional analysis, competitive landscape, company profiles, regulatory developments, mergers & acquisitions, product launches, and recent technological advancements shaping the industry.
Conclusion
The global AI in breast imaging market is poised for exceptional growth, expanding from USD 842.5 million in 2025 to USD 1,090.6 million in 2026, and reaching USD 8,600.0 million by 2034. Rising breast cancer screening rates, increasing adoption of AI-powered diagnostic technologies, advancements in digital breast tomosynthesis, and growing demand for early disease detection will continue driving market expansion. Despite challenges related to implementation costs and data availability, continuous innovation, regulatory approvals, and broader clinical integration are expected to create substantial long-term opportunities for AI solution providers worldwide.
Segmentation By Component, Deployment, Technology, Modality, Application, End User, and Region
By Component * Software
By Deployment * Cloud-Based
By Technology * Computer Vision
By Modality * Mammography/DBT
By Application * Cancer Detection & Triage
By End User * Hospitals & Health Systems
By Region * North America (By Component, Deployment, Technology, Modality, Application, End User, and Country)