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體外診斷人工智能診斷用人工智能

市場調查報告書

商品編碼

2126510

人工智慧（AI）在醫療診斷領域的市場：策略性洞察與預測（2026-2035）

Artificial Intelligence (AI) in Diagnostics Market - Strategic Insights and Forecasts (2026-2035)

出版日期: 2026年08月13日 | 出版商:

Knowledge Sourcing Intelligence | 英文 176 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

預計到 2026 年，用於醫療診斷的人工智慧市場規模將達到 31 億美元，到 2035 年將達到 179.1 億美元，複合年成長率為 21.5%。

在全球醫療診斷領域，人工智慧市場正經歷快速轉型，其驅動力包括診斷數量的增加、人才短缺以及臨床數據日益複雜化。人工智慧透過識別醫學影像、病理標本、實驗室結果、基因組資料集和非結構化臨床文件中具有臨床意義的模式，輔助診斷決策。機器學習、深度學習、電腦視覺和自然語言處理等技術透過提高檢測準確率、減少解讀差異以及優先處理高風險病例，為臨床醫生提供支援。人口老化、慢性病盛行率上升、精準醫療的追求以及診斷影像技術的日益普及，正在產生大量的臨床數據，而傳統工作流程無法高效處理這些數據，導致醫療系統中的診斷數量持續成長。這種不平衡促使人們越來越依賴人工智慧驅動的診斷成像技術來提高營運效率，同時又不影響診斷品質。由於自適應演算法需要在其整個生命週期內進行持續監控，監管機構正在製定針對人工智慧驅動的醫療設備的專門指南。監管機構日益重視透明度、臨床有效性、檢驗、上市後監測和風險管理，敦促開發人員在進行大規模商業化之前加強實證實踐。人工智慧在醫療診斷領域的戰略重要性遠不止於自動化，醫療機構需要能夠連接影像系統、實驗室資訊系統、病理工作流程、基因組分析平台和電子健康記錄的整合平台。

市場促進因素

診斷影像檢查檢查數量增加

由於人口老化和慢性病盛行率上升，需要放射學評估的患者人數不斷增加，對診斷影像檢查的需求也隨之持續成長。隨著醫院報告檢測結果延遲的情況日益普遍，醫療機構正在採用人工智慧驅動的影像分析來簡化工作流程。這將在不取代醫師監督的前提下，提高診斷的一致性，使臨床醫師能夠優先處理重要的觀察。

擴大人工智慧醫療設備的法規結構

由於基於軟體的醫療設備需要在其整個生命週期內接受監管，其監管週期遠超傳統醫療設備，因此醫療監管機構正在不斷制定專門針對人工智慧的監管框架。隨著主要市場監管要求的不斷演變，開發商正在加大對前瞻性檢驗研究的投入。這種轉變不僅增強了商業化機遇，也提升了醫療服務提供者的信心。

精準醫療的廣泛應用

精準醫療依賴對影像、病理、分子診斷和基因組資訊的準確解讀。由於傳統分析方法無法有效率地處理日益複雜的多模態資料集，醫療機構正在採用人工智慧平台。這一轉變將有助於實現個人化診斷，同時改善腫瘤及其他專科領域的生物標記識別和治療方案選擇。

整個醫療保健系統的數位轉型

可互通的數位平台正在推動醫院診斷基礎設施的現代化，以改善醫療協調和營運效率。隨著醫療機構優先考慮自動化，人工智慧解決方案正被整合到企業影像系統、實驗室工作流程和電子健康記錄。這種結構性轉變正在強化對企業級人工智慧部署而非孤立點解決方案的長期需求。

市場限制因素

醫療機構在將人工智慧融入日常診斷流程之前，需要可靠的前瞻性檢驗證據，這使得臨床有效性驗證的要求仍然多種多樣。監管要求在不同司法管轄區不斷變化，延長了旨在進入多區域市場的開發商的產品上市時間。此外，醫療機構必須遵守嚴格的病患資料保護要求，這限制了人工智慧的普及應用，因為資料隱私、網路安全和互通性方面存在著許多挑戰。

市場定義和範圍
行業概覽
產業變化
主要市場趨勢
市場規模表現分析
市場預測
診斷工作流程分析
按診斷專業領域分類的人工智慧採用情況
對所進行的診斷測試數量進行分析
與臨床決策支援系統的整合
醫療服務提供者對實施情形的分析

第4章市場動態

市場促進因素
市場限制因素
市場機遇
市場挑戰

第5章行業情勢

產業價值鏈分析
定價分析
還款狀態

第6章：創新趨勢

診斷領域新興的人工智慧技術
產品創新分析
臨床檢驗和性能評估分析
人工智慧演算法開發趨勢
管道分析
多模態人工智慧與基礎模型的整合
雲端和邊緣人工智慧應用趨勢
與數位健康融合

第7章監理情勢

法律規範
核准流程
合規要求

第8章：全球人工智慧醫療診斷市場分析

分析：按技術
分析：按成分
分析：透過臨床應用
分析：依部署模式
分析：按最終用戶

第9章：全球人工智慧診斷市場細分分析

透過技術
- 機器學習
- 深度學習
- 自然語言處理
- 電腦視覺
- 其他
按組件
- 軟體
- 硬體
- 服務
臨床應用
- 腫瘤學
- 循環系統
- 神經病學
- 呼吸內科
- 胃腸病學
- 感染疾病
- 其他臨床用途
按部署模式
- 基於雲端的
- 現場
最終用戶
- 醫院
- 診斷檢查室
- 製藥和生物技術公司
- 其他

第10章：全球人工智慧醫療診斷市場區域分析

北美洲
歐洲
亞太地區
南美洲
中東和非洲

第11章：全球人工智慧在醫療診斷領域的市場：國別分析

美國
加拿大
德國
英國
法國
義大利
西班牙
荷蘭
中國
日本
韓國
印度
澳洲
巴西
墨西哥
沙烏地阿拉伯
阿拉伯聯合大公國
南非

第12章競爭格局

市佔率分析
策略趨勢
併購、合作與聯盟
新產品發布

第13章：公司簡介

Siemens Healthineers AG
GE HealthCare Technologies Inc.
Koninklijke Philips NV
Johnson & Johnson
Medtronic plc
Boston Scientific Corporation
Tempus AI, Inc.
Aidoc Medical Ltd.
Viz.ai, Inc.
PathAI, Inc.

第14章：全球人工智慧醫療診斷市場商業性預測分析

第15章投資與資金籌措分析

創業投資趨勢
政府資金
研發投資

第16章：未來展望

主要成長機遇
未來產業趨勢

簡介目錄

Product Code: KSI061615738

The AI in Diagnostics Market is forecast to grow at a CAGR of 21.5%, reaching USD 17.91 billion in 2035 from USD 3.10 billion in 2026.

The global AI in diagnostics market is undergoing rapid transformation, driven by the convergence of rising diagnostic volumes, workforce shortages, and the increasing complexity of clinical data. Artificial intelligence enhances diagnostic decision-making by identifying clinically meaningful patterns within medical images, pathology slides, laboratory results, genomic datasets, and unstructured clinical documentation. Machine learning, deep learning, computer vision, and natural language processing support clinicians by improving detection accuracy, reducing interpretation variability, and prioritizing high-risk cases. Healthcare systems continue to experience increasing diagnostic volumes because aging populations, chronic disease prevalence, precision medicine initiatives, and expanding imaging utilization generate larger quantities of clinical data than conventional workflows can efficiently process. This imbalance increases dependence on AI-assisted interpretation to improve operational efficiency without compromising diagnostic quality. Regulatory authorities are establishing dedicated guidance for artificial intelligence-enabled medical devices because adaptive algorithms require continuous oversight throughout their lifecycle. Regulatory expectations increasingly emphasize transparency, clinical validation, cybersecurity, post-market surveillance, and risk management, encouraging developers to strengthen evidence generation before large-scale commercialization. The strategic importance of AI in diagnostics extends beyond automation because healthcare organizations are seeking integrated platforms that connect imaging systems, laboratory information systems, pathology workflows, genomics platforms, and electronic health records.

Market Drivers

Increasing Diagnostic Imaging Volumes

Diagnostic imaging demand continues to expand because aging populations and chronic disease prevalence increase the number of patients requiring radiological assessment. Healthcare providers are implementing AI-assisted image analysis to improve workflow efficiency as reporting backlogs are becoming more common across hospitals. This adoption strengthens diagnostic consistency while enabling clinicians to prioritize critical findings without replacing physician oversight.

Expansion of Regulatory Frameworks for AI-Based Medical Devices

Healthcare regulators continue developing dedicated frameworks for artificial intelligence because software-based medical devices require lifecycle oversight beyond traditional medical equipment. Developers are increasing investments in prospective validation studies as regulatory expectations continue evolving across major markets. This transition supports greater confidence among healthcare providers while improving commercialization opportunities.

Growing Adoption of Precision Medicine

Precision medicine depends on accurate interpretation of imaging, pathology, molecular diagnostics, and genomic information. Healthcare organizations are integrating AI platforms because conventional analytical approaches cannot efficiently process increasingly complex multimodal datasets. This shift supports personalized diagnosis while improving biomarker identification and treatment selection across oncology and other specialty areas.

Digital Transformation Across Healthcare Systems

Hospitals continue modernizing diagnostic infrastructure because interoperable digital platforms improve care coordination and operational efficiency. Artificial intelligence solutions are becoming integrated into enterprise imaging systems, laboratory workflows, and electronic health records as healthcare organizations prioritize automation. This structural transition strengthens long-term demand for enterprise-scale AI deployment rather than isolated point solutions.

Market Restraints

Clinical validation requirements remain extensive because healthcare providers require robust prospective evidence before integrating AI into routine diagnostic workflows. Regulatory requirements continue evolving across jurisdictions, creating longer commercialization timelines for developers seeking multi-region market access. Data privacy, cybersecurity, and interoperability challenges limit implementation because healthcare organizations must comply with stringent patient data protection requirements.

Technology and Segment Insights

By Technology

Deep learning represents the largest technology platform because complex neural network architectures improve image recognition, pathology interpretation, and predictive analytics across multiple diagnostic applications. Healthcare providers are expanding investments in deep learning solutions as imaging datasets continue increasing in size and complexity. Machine learning, natural language processing, and computer vision represent additional critical technologies.

By Component

Software constitutes the primary revenue-generating component because artificial intelligence capabilities are delivered through clinical decision support platforms, workflow management systems, image analysis applications, and cloud-based diagnostic solutions. Healthcare organizations are deploying enterprise software platforms as interoperability with hospital information systems becomes increasingly important. Hardware and services represent additional segments.

By Clinical Application

Oncology represents the leading clinical application because cancer diagnosis increasingly depends on imaging, pathology, molecular profiling, and genomic interpretation. Healthcare institutions are integrating AI across multidisciplinary oncology workflows as precision medicine continues expanding globally. Cardiology, neurology, pulmonology, gastroenterology, and infectious diseases represent significant and growing application areas.

By End User

Hospitals represent the largest end-user segment because they manage high-volume diagnostic services across multiple specialties. Diagnostic laboratories are adopting AI for workflow optimization and result interpretation. Pharmaceutical and biotechnology companies utilize AI for biomarker discovery and clinical trials.

Competitive and Strategic Outlook

The competitive landscape features major medical technology companies and specialized AI diagnostic firms. Siemens Healthineers AG distinguishes itself through one of the broadest AI-enabled diagnostic ecosystems spanning radiology, molecular imaging, laboratory diagnostics, and digital health. GE HealthCare Technologies Inc. differentiates itself by combining advanced imaging equipment with AI-enabled workflow orchestration through the Edison Digital Health Platform. Koninklijke Philips N.V. maintains a strong competitive position by integrating AI across diagnostic imaging, image-guided therapy, patient monitoring, and healthcare informatics. Tempus AI, Inc. differentiates itself through its focus on precision medicine by combining AI with clinical, molecular, imaging, and genomic data. Aidoc Medical Ltd. specializes in AI solutions for radiology workflow optimization and emergency care triage. PathAI, Inc. has established a strong competitive position through AI applications for digital pathology, biomarker discovery, and pharmaceutical research.
Strategic developments include increasing integration of multimodal diagnostics, expansion of digital pathology, and growth of AI adoption in emerging healthcare markets. Companies are forming partnerships with healthcare providers, pharmaceutical companies, and technology vendors to strengthen clinical validation and commercialization. Product launches focus on enterprise software platforms, workflow automation, and precision medicine applications. Companies that successfully combine strong clinical evidence, regulatory expertise, interoperable software platforms, and scalable implementation capabilities are expected to lead the market.

Conclusion

The AI in diagnostics market is poised for exceptional growth, driven by rising diagnostic volumes, regulatory maturation, precision medicine, and healthcare digitalization. The evolution from pilot implementations toward enterprise-wide deployment is reshaping diagnostic practice. Companies that successfully deliver clinically validated, interoperable, and scalable AI solutions are expected to lead the market. Ongoing technological innovation, regulatory support, and expanding digital health infrastructure will further accelerate adoption throughout the forecast period.

Key Benefits of this Report

Insightful Analysis: Detailed market insights across regions, customer segments, policies, socio-economic factors, consumer preferences, and industry verticals.
Competitive Landscape: Understand strategic moves by key players to identify optimal market entry approaches.
Market Drivers and Future Trends: Assess major growth forces and emerging developments shaping the market.
Actionable Recommendations: Support strategic decisions to unlock new revenue streams.
Caters to a Wide Audience: Suitable for startups, research institutions, consultants, SMEs, and large enterprises.

What Businesses Use Our Reports For

Industry and market insights, opportunity assessment, product demand forecasting, market entry strategy, geographical expansion, capital investment decisions, regulatory analysis, new product development, and competitive intelligence.

Report Coverage

Historical data from 2021 to 2024, Base year 2025, and Forecast years from 2026 to 2035
Growth opportunities, challenges, supply chain outlook, regulatory framework, and trend analysis
Competitive positioning, strategies, and market share evaluation, and trade analysis
Revenue growth and forecast assessment across segments and regions
Company profiling including strategies, products, financials, and key developments

1. Executive Summary

1.1 Market Snapshot
1.2 Key Findings
1.3 Analyst Insights
1.4 Strategic Recommendations

2. Research Methodology

2.1 Research Design
2.2 Data Collection Methodology
2.3 Market Size Estimation
2.4 Forecasting Model
2.5 Assumptions & Limitations

3. Global AI in Diagnostics Market Overview, Size & Forecast

3.1 Market Definition & Scope
3.2 Industry Overview
3.3 Industry Evolution
3.4 Key Market Trends
3.5 Historical Market Size Analysis (2021-2025)
3.6 Market Forecast (2026-2035)
3.7 Diagnostic Workflow Analysis
3.8 AI Adoption Across Diagnostic Specialties
3.9 Diagnostic Testing Volume Analysis
3.10 Clinical Decision Support Integration
3.11 Healthcare Provider Adoption Analysis

4. Market Dynamics

4.1 Market Drivers
4.2 Market Restraints
4.3 Market Opportunities
4.4 Market Challenges

5. Industry Landscape

5.1 Industry Value Chain Analysis
5.2 Pricing Analysis
5.3 Reimbursement Landscape

6. Innovation Landscape

6.1 Emerging AI Technologies in Diagnostics
6.2 Product Innovation Analysis
6.3 Clinical Validation and Performance Evaluation Analysis
6.4 AI Algorithm Development Trends
6.5 Pipeline Analysis
6.6 Multimodal AI and Foundation Model Integration
6.7 Cloud and Edge AI Deployment Trends
6.8 Digital Health Integration

7. Regulatory Landscape

7.1 Regulatory Framework
7.2 Approval Pathways
7.3 Compliance Requirements

8. Global AI in Diagnostics Market Landscape Analysis

8.1 Analysis by Technology
8.2 Analysis by Component
8.3 Analysis by Clinical Application
8.4 Analysis by Deployment Model
8.5 Analysis by End User

9. Global AI in Diagnostics Market Segment Analysis (2021-2035)

9.1 By Technology
- 9.1.1 Machine Learning
- 9.1.2 Deep Learning
- 9.1.3 Natural Language Processing
- 9.1.4 Computer Vision
- 9.1.5 Others
9.2 By Component
- 9.2.1 Software
- 9.2.2 Hardware
- 9.2.3 Services
9.3 By Clinical Application
- 9.3.1 Oncology
- 9.3.2 Cardiology
- 9.3.3 Neurology
- 9.3.4 Pulmonology
- 9.3.5 Gastroenterology
- 9.3.6 Infectious Diseases
- 9.3.7 Other Clinical Applications
9.4 By Deployment Model
- 9.4.1 Cloud-Based
- 9.4.2 On-Premise
9.5 By End User
- 9.5.1 Hospitals
- 9.5.2 Diagnostic Laboratories
- 9.5.3 Pharmaceutical & Biotechnology Companies
- 9.5.4 Others

10. Global AI in Diagnostics Market Geographical Analysis (2021-2035)

10.1 North America
10.2 Europe
10.3 Asia-Pacific
10.4 South America
10.5 Middle East & Africa

11. Global AI in Diagnostics Market Country Analysis (2021-2035)

11.1 United States
11.2 Canada
11.3 Germany
11.4 United Kingdom
11.5 France
11.6 Italy
11.7 Spain
11.8 Netherlands
11.9 China
11.10 Japan
11.11 South Korea
11.12 India
11.13 Australia
11.14 Brazil
11.15 Mexico
11.16 Saudi Arabia
11.17 United Arab Emirates
11.18 South Africa

12. Competitive Landscape

12.1 Market Share Analysis
12.2 Strategic Developments
12.3 Mergers & Acquisitions, Partnerships & Collaborations
12.4 Product Launches

13. Company Profiles

13.1 Siemens Healthineers AG
- 13.1.1 Company Overview
- 13.1.2 Financials
- 13.1.3 Product Portfolio
- 13.1.4 Recent Developments
13.2 GE HealthCare Technologies Inc.
13.3 Koninklijke Philips N.V.
13.4 Johnson & Johnson
13.5 Medtronic plc
13.6 Boston Scientific Corporation
13.7 Tempus AI, Inc.
13.8 Aidoc Medical Ltd.
13.9 Viz.ai, Inc.
13.10 PathAI, Inc.