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市場調查報告書
商品編碼
1957200
人工智慧在乳房攝影影像領域的市場-全球產業規模、佔有率、趨勢、機會和預測:按組件、顯像模式、應用、最終用途、地區和競爭格局分類,2021-2031年AI In Breast Imaging Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By Imaging Modality, By Application, By End Use, By Region & Competition, 2021-2031F |
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全球乳癌影像人工智慧(AI)市場預計將從 2025 年的 3.2032 億美元成長到 2031 年的 4.4092 億美元,複合年成長率為 5.47%。
在這一領域,機器學習和深度學習演算法正被應用於輔助放射科醫生,以提高其在分析乳房X光片、超音波和磁振造影等醫學影像時檢測異常的能力。推動這項發展的主要因素是:全球乳癌發生率的上升以及迫切需要加強篩檢項目,從而減輕放射科醫生繁重的工作負擔。這些工具透過自動化日常任務和優先處理可疑病例來簡化臨床工作流程,從而解決影像數量快速成長與專家數量有限之間的不平衡問題。
| 市場概覽 | |
|---|---|
| 預測期 | 2027-2031 |
| 市場規模:2025年 | 3.2032億美元 |
| 市場規模:2031年 | 4.4092億美元 |
| 複合年成長率:2026-2031年 | 5.47% |
| 成長最快的細分市場 | 篩檢 |
| 最大的市場 | 北美洲 |
醫療專業人員對這些技術的日益普及表明,他們對這類營運支援的需求日益成長。歐洲放射學會 (ESR) 2024 年的報告顯示,48% 的受訪會員目前正在使用人工智慧,預計這項技術將產生最大的影響,尤其是在乳房和腫瘤成像領域。然而,阻礙市場擴張的主要挑戰在於實施和整合到現有系統中的高成本。複雜的監管要求往往會加劇這些經濟障礙,使得資源有限的醫療機構難以採用這些診斷解決方案,從而限制了市場滲透率。
放射科醫師日益短缺,加上診斷工作量不斷增加,是推動人工智慧在乳房攝影應用的首要驅動力。全球醫療系統正面臨嚴重的供需失衡,影像檢查數量遠超過可用人員,導致醫護人員疲憊不堪,診斷延誤。因此,人工智慧解決方案正被引入檢查分診和自動報告等環節,以提高這些不堪重負的科室的效率。英國皇家放射學院在2024年6月發布的《2023年臨床放射學調查報告》中指出,英國醫療系統面臨30%的臨床放射科醫生缺口,預計到2028年這一數字將惡化至40%。這種短缺正在加速人工智慧的商業化進程。根據 Axis Imaging News 2024 年 5 月的一份報告,美國食品藥物管理局(FDA) 在核准名單中新增了 191 種人工智慧驅動的醫療設備,其中 128 種專注於放射學領域,凸顯了該行業對人才短缺的積極應對。
同時,全球乳癌發生率的上升使得更完善的篩檢通訊協定和更有效率的技術需求日益迫切。隨著早期疾病篩檢計畫的擴展,需要解讀的乳房X光片數量激增,給診斷基礎設施維持準確性和處理能力帶來了巨大壓力。根據美國癌症協會於2024年1月發布的《2024-2025年乳癌統計資料》,預計2024年美國將新增約310,720例侵襲性乳癌病例。為了應對不斷上升的發病率,能夠檢測高風險異常的人工智慧演算法正在被應用,以確保病例增加不會導致漏診或延誤治療。
高昂的實施和整合成本是全球乳癌影像人工智慧市場擴張的主要障礙。實施這些先進的診斷工具需要大量的資金投入,包括購買複雜的軟體、必要的硬體升級以及整合複雜的IT基礎設施。除了初始成本外,醫療機構還面臨持續的支出,例如系統維護、定期軟體更新和專業人員培訓。對於許多機構,特別是小規模獨立診所和資源有限的機構而言,這些財務負擔可能構成障礙,尤其是在目前缺乏能夠保證明確投資回報的全面報銷模式的情況下。
經濟負擔是整個產業對人工智慧應用猶豫不決的主要原因。根據歐洲放射學會 (ESR) 2024 年的一項調查,49.5% 的受訪者認為「成本」或「預算不足」是臨床環境中採用人工智慧的主要障礙。因此,市場成長仍主要集中在資金雄厚的學術機構,而一般醫療機構的採用則停滯不前。這種經濟差距有效地限制了市場發展的範圍,並減緩了整個產業全球擴張的步伐。
數位乳房斷層合成成像人工智慧解決方案的普及,正在解決分析體積影像資料的複雜性問題。由於3D乳房X光乳房X光攝影產生的資料集比傳統的2D方法大規模,因此對能夠增強病灶可見性並減少假陰性結果的人工智慧演算法的需求日益成長,尤其是在緻密乳房組織中。近期的大規模臨床數據也證實了其有效性。在2025年11月發表的新聞稿《突破性自然健康研究證實DeepHealth創新人工智慧輔助乳癌檢測工作流程的有效性》中,RadNet報告了超過57.9萬名女性的評估結果,顯示其人工智慧輔助篩檢通訊協定與標準3D乳房X光乳房X光攝影相比,癌症檢出率提高了21.6%。
同時,人工智慧供應商與影像設備製造商之間的策略合作正透過將分析功能直接整合到放射學判讀環境中,加速市場滲透。這些合作使醫療機構能夠在現有基礎設施內利用先進的診斷工具,而無需投資於分散的獨立軟體解決方案。一個顯著的例子是,領先的醫療機構正在全面採用這些技術。 2025年4月,《放射學商業》(Radiology Business)在報導題為「領先的醫療機構利用人工智慧改進乳房攝影% 。這凸顯了這些商業性協議所帶來的營運價值。
The Global AI In Breast Imaging Market is projected to expand from USD 320.32 Million in 2025 to USD 440.92 Million by 2031, registering a CAGR of 5.47%. This sector involves the application of machine learning and deep learning algorithms to aid radiologists in analyzing medical imagery, including mammograms, ultrasound, and MRI scans, for enhanced anomaly detection. Growth is primarily driven by the increasing global prevalence of breast cancer, which necessitates robust screening programs, and the critical need to alleviate the workload of overburdened radiologists. By automating routine tasks and prioritizing suspicious cases, these tools address the disparity between surging image volumes and the limited availability of specialists, thereby improving clinical workflow efficiency.
| Market Overview | |
|---|---|
| Forecast Period | 2027-2031 |
| Market Size 2025 | USD 320.32 MIllion |
| Market Size 2031 | USD 440.92 MIllion |
| CAGR 2026-2031 | 5.47% |
| Fastest Growing Segment | Screening |
| Largest Market | North America |
This demand for operational support is evident in the rising utilization of these technologies among practitioners. In 2024, the European Society of Radiology reported that 48% of surveyed members were currently using AI, with the technology expected to have the most significant impact on breast and oncologic imaging. However, a major challenge hindering market expansion is the high cost associated with implementation and integration into existing systems. These financial barriers, often compounded by complex regulatory requirements, prevent resource-constrained healthcare facilities from adopting these diagnostic solutions, effectively restricting broader market penetration.
Market Driver
The growing shortage of radiologists combined with an increasing diagnostic workload serves as the most urgent catalyst for AI adoption in the breast imaging sector. Healthcare systems globally face a critical imbalance where the volume of imaging studies exceeds the available workforce, resulting in burnout and diagnostic delays. AI solutions are consequently being integrated to triage scans and automate reporting, acting as a force multiplier for strained departments. The Royal College of Radiologists noted in their 'Clinical Radiology Census 2023 Report' in June 2024 that the UK healthcare system faces a 30% shortfall of clinical radiologists, which is projected to worsen to 40% by 2028. This scarcity has accelerated commercialization efforts; Axis Imaging News reported in May 2024 that the U.S. FDA added 191 new AI-enabled medical devices to its approved list, with 128 focused on radiology, highlighting the industry's aggressive response to workforce limitations.
Concurrently, the increasing global incidence of breast cancer necessitates more robust screening protocols, further amplifying the need for efficient technologies. As screening programs expand to catch disease earlier, the number of mammograms requiring interpretation is surging, placing immense pressure on diagnostic infrastructure to maintain accuracy and throughput. According to the American Cancer Society's 'Breast Cancer Facts & Figures 2024-2025' released in January 2024, an estimated 310,720 new invasive breast cancer cases are projected to be diagnosed in women in the US during 2024. This escalating prevalence drives the deployment of AI algorithms capable of flagging high-risk anomalies, ensuring that rising case volumes do not result in missed diagnoses or delayed treatments.
Market Challenge
The high cost of implementation and integration constitutes a substantial impediment to the expansion of the global AI in breast imaging market. Deploying these advanced diagnostic tools requires significant capital investment, covering the acquisition of sophisticated software, necessary hardware upgrades, and complex IT infrastructure integration. Beyond the initial outlay, healthcare facilities face ongoing expenses for system maintenance, regular software updates, and specialized staff training. For many organizations, particularly smaller independent practices and resource-constrained clinics, these financial demands are prohibitive, especially given the current lack of comprehensive reimbursement models to ensure a clear return on investment.
This financial strain is a primary reason for the hesitation observed across the industry. According to the European Society of Radiology in 2024, 49.5% of surveyed members identified costs or lack of budget as the main potential barrier to AI implementation in clinical practice. Consequently, market growth remains skewed toward well-funded academic centers, while broader adoption across the general healthcare landscape is stalled. This economic disparity effectively limits the market's reach and decelerates the overall trajectory of global industry expansion.
Market Trends
The proliferation of AI solutions for Digital Breast Tomosynthesis is addressing the complexities of analyzing volumetric imaging data. As 3D mammography generates larger datasets than traditional 2D modalities, AI algorithms are increasingly deployed to enhance lesion conspicuity and reduce false negatives, particularly in dense breast tissue. This efficacy was substantiated by recent large-scale clinical data; RadNet Inc. reported in a November 2025 press release regarding the 'Landmark Nature Health Study Demonstrates the Effectiveness of DeepHealth's Novel AI-Powered Breast Cancer Detection Workflow' that an evaluation involving over 579,000 women revealed their AI-supported screening protocol achieved a 21.6% increase in the cancer detection rate compared to standard 3D mammography.
Simultaneously, strategic alliances between AI vendors and imaging OEMs are accelerating market penetration by embedding analytics directly into radiology reading environments. These collaborations allow healthcare providers to access advanced diagnostic tools within their existing infrastructure rather than investing in fragmented, standalone software solutions. A notable instance involves major institutions securing comprehensive access to such technologies; Radiology Business reported in April 2025 in the article 'Big-name healthcare orgs tap AI to improve breast imaging workflows' that Therapixel's partnership to integrate its MammoScreen software at Mayo Clinic increased radiologist interpretation speeds by approximately 35%, underscoring the operational value driving these commercial agreements.
Report Scope
In this report, the Global AI In Breast Imaging Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies present in the Global AI In Breast Imaging Market.
Global AI In Breast Imaging Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: