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體外診斷癌症癌症診斷

市場調查報告書

商品編碼

1410108

癌症診斷領域人工智慧的全球市場：預測（2023-2028）

AI in Cancer Diagnostics Market - Forecasts from 2023 to 2028

出版日期: 2023年12月14日 | 出版商:

Knowledge Sourcing Intelligence | 英文 147 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2021年全球癌症診斷人工智慧市場規模達807,795,000美元。

隨著人工智慧技術徹底改變癌症診斷，癌症診斷的人工智慧市場正在迅速擴大。人工智慧演算法分析大量患者資料，包括醫學照片、遺傳資料和臨床記錄，以幫助識別、分類和預測癌症。利用機器學習和深度學習方法，人工智慧系統可以發現醫學照片中的微妙模式和異常，從而早期發現癌症並改善患者預後。人工智慧在癌症診斷中的整合可以提高準確性、消除診斷錯誤並提供個體化的治療方法建議。隨著癌症盛行率的不斷上升，以及對高效、準確診斷解決方案的需求不斷成長，人工智慧在癌症診斷市場中將在徹底改變癌症治療和推動精準醫療進步方面發揮巨大作用，前景廣闊。

人工智慧技術的進步將加強人工智慧在癌症診斷市場的成長。

人工智慧的發展有可能透過提高準確性、效率和早期檢測率來徹底改變癌症診斷，最終改善患者的治療結果和個體化治療方法。

多模態資料整合，用於癌症診斷市場人工智慧的綜合分析。

整合多模式資料進行徹底分析已成為癌症診斷的重大發展。整合多模式資料可以更詳細地了解疾病，使醫生能夠做出明智的決策並為癌症患者制定個體化的治療策略。

改善患者治療效果和治療計劃將推動人工智慧在癌症診斷市場的規模。

改善患者治療效果和治療計畫是將人工智慧涵蓋癌症診斷的兩大好處。根據發表在 JAMA Network Open 上的一項研究，與人類病理學家相比，人工智慧演算法提高了肺癌檢測的準確性，從而改善了患者的治療結果。這項研究發現人工智慧輔助診斷提高了敏感性和特異性。此外，根據《自然醫學》發表的一項研究，基於人工智慧的乳癌治療計劃模型顯著減少了不必要的手術，從而改善了患者的治療結果和生活品質。這些發現表明人工智慧 (AI) 可以指導治療決策、最佳化藥物選擇並減少治療浪費，從而改善患者治療效果並創造更個體化和有效的癌症治療方法。我們強調我們提供的可能性。

北美是癌症診斷人工智慧市場的市場領導者。

北美已成為癌症診斷市場人工智慧的行業領導者。該地區的主導地位有幾個原因。北美擁有先進的醫療基礎設施、許多主要的人工智慧企業，並且高度重視癌症研究和開發。此外，該地區受益於醫療機構、研究中心和技術公司之間的緊密聯繫，這促進了創新並推動了基於人工智慧的癌症診斷的發展。此外，北美擁有完善的法律規範，可以輕鬆在醫療保健領域引入和使用人工智慧技術。此外，該地區龐大的患者群體、高昂的醫療成本以及優惠的報銷規則都促進了該地區人工智慧在癌症診斷市場的成長。然而，隨著歐洲和亞太地區等其他地區在癌症診斷市場的人工智慧方面繼續取得重大進展，密切關注不斷變化的環境非常重要。

人工智慧在癌症診斷市場中擴大採用數位病理學和放射學。

在人工智慧癌症診斷領域，數位病理學和放射學的使用顯著增加。數位病理學測試將病理切片數位化，從而可以輕鬆存取、共用和分析高解析度影像。好處包括遠端協作、更好的影像處理以及與人工智慧演算法的無縫整合。同樣，數位放射線可以實現醫學影像的數位化，從而實現更快、更有效率的影像儲存、搜尋和分析。數位病理學和數位放射線的廣泛應用將為人工智慧技術在癌症診斷中的應用奠定堅實的基礎。實現更準確、更有效率的診斷、分類和治療計劃，從而實現更精準、個體化的癌症治療。數位病理學和放射學與人工智慧的融合具有徹底改變癌症診斷的巨大潛力。

主要進展：

2023年7月，人工智慧和精準醫療領域的先驅Tempus與TScan Therapeutics合作，TScan Therapeutics是一家臨床階段的生物製藥公司，專注於為癌症患者開發TCR工程化T細胞療法（TCR-T）。伴同性診斷(CDx) 測試。該合作夥伴關係將支持 TScan 的 1 期固體癌臨床試驗篩檢程序，使患者能夠接受基於腫瘤抗原陽性和完整 HLA 表達的客製化 TCR-T 組合。 2023 年 6 月，著名的病理診斷人工智慧 (AI) 供應商 Mindpeak 和領先的數位病理學和計算病理學解決方案提供商 Proscia 宣布，他們將為癌症患者提供先進的診斷服務。改善透過此次合作，兩家公司正在為緊密整合的人工智慧驅動流程奠定基礎，以幫助病理學家做出更有效率、更明智和可重複的臨床選擇。

企業產品

Watson for Oncology：這個人工智慧驅動的系統使用自然語言處理和機器學習技術來幫助腫瘤學家做出明智的治療決策。我們分析病歷、研究論文和臨床指引等患者資料，以提案適合每位患者需求的實證治療方法。
NVIDIA DGX 系統：研究人員和醫療保健相關人員可以使用這個先進的 AI 計算平台來訓練和部署用於癌症診斷的複雜 AI 模型。 DGX 設備提供更快的運算能力，使建立用於癌症診斷的 AI 演算法和應用程式變得更加容易。
Tempus 臨床平台：此平台收集並分析來自資料來源的大量臨床和分子資料，包括電子健康記錄、病理報告、基因組定序和放射影像。 Tempus 的人工智慧演算法從這些資料中提取相關見解，以幫助癌症診斷、治療計劃和預後預測。
Concentriq Dx：此數位病理平台使用人工智慧演算法來改進病理影像的處理和解釋。實現自動影像處理，例如腫瘤檢測、惡性確定和定量。該工具透過實現數位載玻片的快速檢查和註釋來促進病理學家的協作。

The AI in the cancer diagnostics market is expanding rapidly as artificial intelligence technologies revolutionise cancer diagnostics. AI algorithms analyse massive volumes of patient data, such as medical pictures, genetic data, and clinical records, to help in the identification, classification, and prognosis of cancer. AI systems can discover subtle patterns and anomalies in medical pictures using machine learning and deep learning approaches, resulting in early cancer identification and improved patient outcomes. AI integration in cancer diagnostics can improve precision, eliminate diagnostic mistakes, and provide personalised therapy recommendations. With the rising prevalence of cancer and the increasing demand for efficient and accurate diagnostic solutions, AI in the cancer diagnostics market offers enormous promise for revolutionising cancer care and propelling advances in precision medicine.

Advancements in Artificial Intelligence Technologies Enhance the AI in Cancer Diagnostics Market Growth.

AI developments have the potential to revolutionise cancer diagnostics by increasing accuracy, efficiency, and early detection rates, ultimately leading to better patient outcomes and personalised treatment methods.

Integration of Multi-modal Data for Comprehensive Analysis in AI in Cancer Diagnostics Market.

Integrating multimodal data for thorough analysis has emerged as a key development in cancer diagnoses. The integration of multimodal data enables a more thorough picture of the disease, allowing doctors to make educated decisions and design personalised treatment strategies for cancer patients.

Improved Patient Outcomes and Treatment Planning boosts the AI in Cancer Diagnostics Market Size.

Improved patient outcomes and treatment planning are two major benefits of incorporating AI in cancer diagnoses. Research published in JAMA Network Open found that AI algorithms enhanced lung cancer detection accuracy when compared to human pathologists alone, resulting in better patient outcomes. The study found that AI-assisted diagnosis improved in terms of both sensitivity and specificity. Furthermore, according to a study published in Nature Medicine, AI-based models for breast cancer treatment planning resulted in a considerable reduction in needless procedures, resulting in improved patient outcomes and quality of life. These findings emphasise the potential of artificial intelligence (AI) in guiding treatment decisions, optimising drug selection, and reducing needless treatments, thereby improving patient outcomes and providing more personalised and effective cancer care.

North America is the Market Leader in the AI in Cancer Diagnostics Market.

North America has established itself as the industry leader in AI in the cancer diagnostics market. Several reasons contribute to the region's prominence. North America has sophisticated healthcare infrastructure, a large presence of major AI businesses, and a strong emphasis on cancer research and development. Furthermore, the region benefits from substantial connections among healthcare institutions, research centres, and technology businesses, which fosters innovation and propels developments in AI-based cancer diagnoses. North America also has a favourable regulatory framework, making it easier to adopt and use AI technology in healthcare. Furthermore, the region's huge patient population, high healthcare spending, and favourable reimbursement rules all contribute to the region's AI in cancer diagnostics market growth. However, as other areas, such as Europe and Asia-Pacific, continue to make substantial advancements in the AI in cancer diagnostics market, it is critical to watch the developing environment.

Increasing Adoption of Digital Pathology and Radiology in AI in Cancer Diagnostics Market.

In the field of AI in cancer diagnoses, the use of digital pathology and radiology has seen a substantial increase. Pathology slides are digitised in digital pathology, providing for simple access, sharing, and analysis of high-resolution pictures. Remote collaboration, better picture processing, and seamless integration with AI algorithms are some of the advantages. Similarly, digital radiology allows for the digitalization of medical imaging, allowing for faster and more efficient picture storage, retrieval, and analysis. The growing use of digital pathology and radiology lays a solid platform for the use of AI technology in cancer diagnoses. It enables more precise and personalised cancer care by allowing for more accurate and efficient diagnosis, classification, and therapy planning. The merging of digital pathology and radiology with AI has enormous promise to revolutionise cancer diagnoses.

Key Developments:

In July 2023, Tempus, a pioneer in artificial intelligence and precision medicine, announced a new collaboration to develop a companion diagnostic (CDx) test with TScan Therapeutics, a clinical-stage biopharmaceutical company focused on the development of TCR-engineered T cell therapies (TCR-T) for cancer patients. The partnership supports TScan's screening procedure for its Phase 1 solid tumour clinical trial, which is meant to allow patients to receive customized TCR-T mixes based on tumour antigen positivity and intact HLA expression.
In June 2023, Mindpeak, a prominent supplier of artificial intelligence (AI) for pathology, and Proscia, a leading provider of digital and computational pathology solutions, announced a collaboration to increase cancer patients' access to enhanced diagnostics. The cooperation lays the groundwork for the firms to create tightly integrated AI-powered processes that enable pathologists to make more efficient, informed, and repeatable clinical choices.

Company Products:

Watson for Oncology: This artificial intelligence-powered system uses natural language processing and machine learning techniques to help oncologists make educated treatment decisions. It analyses patient data, such as medical records, research articles, and clinical guidelines, to produce evidence-based therapy suggestions tailored to each patient's specific needs.
NVIDIA DGX Systems: Researchers and healthcare practitioners may use these sophisticated AI computing platforms to train and deploy complicated AI models for cancer diagnoses. The DGX devices offer faster computational capabilities, making it easier to construct AI algorithms and apps for cancer diagnostics.
Tempus Clinical Platform: This platform collects and analyses massive volumes of clinical and molecular data from a variety of sources, such as electronic health records, pathology reports, genomic sequencing, and radiology images. Tempus' AI algorithms extract relevant insights from this data to help with cancer diagnosis, treatment planning, and prognosis prediction.
Concentriq Dx: This digital pathology platform uses AI algorithms to improve pathology picture processing and interpretation. It enables automatic image processing for activities including tumour detection, grading, and quantification. The tool promotes pathologist collaboration by allowing for the rapid inspection and annotation of digital slides.

Segmentation:

By Cancer Type

Breast Cancer
Lung Cancer
Prostate Cancer
Colorectal Cancer
Skin Cancer
Others

By Application

Tumor Detection And Classification
Treatment Planning
Image Analysis
Genomic Analysis
Others

By End-User

Hospitals And Clinics
Diagnostic Centers
Research Institutes
Others

By Geography

North America
United States
Canada
Mexico
South America
Brazil
Argentina
Others
Europe
United Kingdom
Germany
France
Italy
Spain
Others
Middle East And Africa
Saudi Arabia
UAE
Others
Asia Pacific
Japan
China
India
South Korea
Indonesia
Taiwan
Others

1. INTRODUCTION

1.1. Market Overview
1.2. Market Definition
1.3. Scope of the Study
1.4. Market Segmentation
1.5. Currency
1.6. Assumptions
1.7. Base, and Forecast Years Timeline

2. RESEARCH METHODOLOGY

2.1. Research Data
2.2. Sources
2.3. Research Design

3. EXECUTIVE SUMMARY

3.1. Research Highlights

4. MARKET DYNAMICS

4.1. Market Drivers
4.2. Market Restraints
4.3. Porters Five Forces Analysis
- 4.3.1. Bargaining Power of Suppliers
- 4.3.2. Bargaining Power of Buyers
- 4.3.3. Threat of New Entrants
- 4.3.4. Threat of Substitutes
- 4.3.5. Competitive Rivalry in the Industry
4.4. Industry Value Chain Analysis

5. AI IN CANCER DIAGNOSTICS MARKET, BY CANCER TYPE

5.1. Introduction
5.2. BREAST CANCER
5.3. LUNG CANCER
5.4. PROSTATE CANCER
5.5. COLORECTAL CANCER
5.6. SKIN CANCER
5.7. OTHERS

6. AI IN CANCER DIAGNOSTICS MARKET, BY APPLICATION

6.1. Introduction
6.2. TUMOR DETECTION AND CLASSIFICATION
6.3. TREATMENT PLANNING
6.4. IMAGE ANALYSIS
6.5. GENOMIC ANALYSIS
6.6. OTHERS

7. AI IN CANCER DIAGNOSTICS MARKET, BY END-USER

7.1. Introduction
7.2. HOSPITALS AND CLINICS
7.3. DIAGNOSTIC CENTERS
7.4. RESEARCH INSTITUTES
7.5. OTHERS

8. AI IN CANCER DIAGNOSTICS MARKET, BY GEOGRAPHY

8.1. Introduction
8.2. North America
- 8.2.1. United States
- 8.2.2. Canada
- 8.2.3. Mexico
8.3. South America
- 8.3.1. Brazil
- 8.3.2. Argentina
- 8.3.3. Others
8.4. Europe
- 8.4.1. United Kingdom
- 8.4.2. Germany
- 8.4.3. France
- 8.4.4. Italy
- 8.4.5. Spain
- 8.4.6. Others
8.5. Middle East and Africa
- 8.5.1. Saudi Arabia
- 8.5.2. UAE
- 8.5.3. Others
8.6. Asia Pacific
- 8.6.1. Japan
- 8.6.2. China
- 8.6.3. India
- 8.6.4. South Korea
- 8.6.5. Indonesia
- 8.6.6. Taiwan
- 8.6.7. Others