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Global AI-based Digital Pathology/AI Pathology Market Research Report 2022-2035 Featuring Profiles of PathAI, Paige, Akoya Biosciences, PROSCIA, Visiopharm, Roche, Aiforia, Indica Labs, & Ibex

Global AI-based Digital Pathology/AI Pathology Market Research Report 2022-2035 Featuring Profiles of PathAI, Paige, Akoya Biosciences, PROSCIA, Visiopharm, Roche, Aiforia, Indica Labs, & Ibex

全球基於人工智能的數字病理學/人工智能病理學市場研究報告 2022-2035 報告提供泰語,佩奇,阿科亞生物科學,PROSCIA,視覺藥物,羅氏,艾福利亞,印度實驗室和山羊的概況
PR Newswire ·  2023/01/21 15:20

DUBLIN, Jan. 21, 2023 /PRNewswire/ -- The "AI-based Digital Pathology/AI Pathology Market Distribution by Type of Neural Network, Type of Assay, Type of End-user, Area of Application, Type of Target Disease Indication and Key Geographies, 2022-2035" report has been added to  ResearchAndMarkets.com's offering.

都柏林, 2023 年一月二十一日 /PRNewswire/-「基於人工智能的數字病理學/人工智能病理學市場分布(按神經網絡類型,測定類型,最終用戶類型,應用領域,目標疾病指示類型和關鍵地理位置劃分),2022-2035 年度報告已添加到 市場研究 提供。

The "AI-based Digital Pathology / AI Pathology Market" report features an extensive study of the current market landscape and future potential of the AI-based digital pathology market. The study features an in-depth analysis, highlighting the capabilities of various stakeholders engaged in providing AI-based digital pathology.

「基於 AI 的數字病理學/人工智能病理學市場」報告對基於 AI 的數字病理學市場的當前市場格局和未來潛力進行了廣泛研究。該研究具有深入的分析,突出了參與提供基於 AI 的數字病理學的各種利益相關者的能力。

Amidst the ever-growing demand for pathology services, the simultaneous use of technological advances to automate and digitize healthcare procedures is growing. These developments have accelerated research and clinical diagnosis, as well as enhanced patient outcomes, in the recent years.

在對病理服務不斷增長的需求中,同時使用技術進步來自動化和數字化醫療保健程序的同時正在增長。近年來,這些發展加速了研究和臨床診斷,並增強了患者的治療效果。

Specifically, AI-powered digital imaging is one such technology, which has revolutionized the pathology industry by enabling high-throughput scanning of patient samples. To provide more context, AI-based digital pathology / AI pathology involves collection, management, analyzing and sharing of data (via digital slides) in a digital setting.

具體來說,人工智能驅動的數字成像是這樣的技術之一,它通過對患者樣本進行高通量掃描徹底改變了病理學行業。為了提供更多情境,基於人工智能的數字病理學/AI 病理學涉及數字環境中的數據(通過數字幻燈片)的收集,管理,分析和共享。

Through this process, digital slides are created by scanning conventional glass slides with a scanning device, which may be seen on a computer screen or a mobile device and offer a high-resolution digital image. Further, AI pathology technique presents a viable solution to managing the growing pathology workload, while also ensuring more rapid and consistent diagnostic services and research activities.

通過此過程,數字幻燈片是通過使用掃描裝置掃描傳統玻璃幻燈片來創建的,該設備可以在計算機屏幕或移動設備上看到並提供高分辨率的數字圖像。此外,人工智能病理學技術為管理日益增長的病理學工作量提供了一個可行的解決方案,同時還確保了更快速,一致的診斷服務和研究活動。

Moreover, AI-powered digital pathology solutions (digital pathology scanners and digital pathology software) allow pathologists to examine more cases and offer a precise diagnosis. It is worth highlighting that digitized workflows can speed up processing times, lower administrative errors, enable remote collaboration and boost productivity, thereby, allowing significant cost savings.

此外,人工智能驅動的數字病理學解決方案(數字病理掃描儀和數字病理學軟件)使病理學家能夠檢查更多病例並提供精確診斷。值得強調的是,數字化的工作流程可以加快處理時間,減少管理錯誤,實現遠程協作並提高生產力,從而大大節省成本。

Considering the rising popularity and demand for such solutions in the healthcare and research industry, and the ongoing efforts of AI-powered digital pathology solution providers / AI pathology solution providers to further improve / expand their respective portfolios, we believe that the AI-based digital pathology market is likely to evolve at a steady pace, till 2035.

考慮到醫療保健和研究行業對此類解決方案的日益普及和需求,以及 AI 驅動的數字病理解決方案提供商/AI 病理學解決方案提供商的持續努力,以進一步改善/擴大其各自的產品組合,我們認為,基於 AI 的數字病理學市場可能會以穩定的速度發展,直到 2035 年。

Scope of the Report

報告範圍

  • An executive summary of the insights captured during our research. It offers a high-level view on the current state of AI-based digital pathology market and its likely evolution in the mid-long term.
  • A general introduction to AI-based digital pathology, featuring information on artificial intelligence in digital pathology, workflow of AI-based digital pathology, applications of AI-based digital pathology solutions in the healthcare domain.
  • A detailed assessment of the overall market landscape of AI-based digital pathology providers, based on several relevant parameters.
  • An in-depth analysis, highlighting the contemporary market trends.
  • Elaborate profiles of various prominent players that are engaged in offering services related to AI-based digital pathology. Each profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and management team) and details related to recent developments and an informed future outlook.
  • A company competitive analysis of various players engaged in this domain. It highlights the capabilities of industry players (in terms of their expertise across various services related to AI-based digital pathology).
  • An analysis of the funding and investments made within this domain, during the period 2016-2022, based on several relevant parameters, such as number of instances, amount invested, type of funding, area of application, geography and information on most active players engaged in the AI-based digital pathology domain.
  • An elaborate analysis in order to estimate the current and future demand for AI-based digital pathology, based on several relevant parameters.
  • A detailed market forecast analysis, highlighting the likely evolution of the AI-based digital pathology market in the short to mid-term and long term, over the period 2022-2035. In order to account for future uncertainties and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, which represent different tracks of the industry's growth.
  • 我們研究過程中捕獲的見解的執行摘要。它提供了對基於 AI 的數字病理學市場的當前狀況及其在中期可能發展的高層次視圖。
  • 一般介紹基於 AI 的數字病理學,包括數字病理學中人工智能的信息,基於 AI 的數字病理學工作流程,基於 AI 的數字病理學解決方案在醫療保健領域的應用。
  • 根據幾個相關參數,對基於 AI 的數字病理提供商的整體市場格局進行詳細評估。
  • 深入分析,突出當代市場趨勢。
  • 詳細介紹從事提供與基於 AI 的數字病理學相關服務的各種著名球員的個人資料。每個簡介都提供了公司的簡要概述(包括成立年份,員工人數,總部位置和管理團隊的位置)以及與近期發展相關的詳細信息以及明智的未來展望。
  • 參與該領域的各種參與者的公司競爭分析。它突出了行業參與者的能力(就其在與基於 AI 的數字病理學相關的各種服務方面的專業知識而言)。
  • 在 2016-2022 年期間,對該領域內的資金和投資進行了分析,基於幾個相關參數,例如實例數量,投入金額,資金類型,申請區域,地理位置以及從事基於 AI 的數字病理領域的大多數活躍參與者的信息。
  • 根據幾個相關參數,進行詳盡的分析,以估計基於 AI 的數字病理學的當前和未來的需求。
  • 詳細的市場預測分析,突出了基於 AI 的數字病理市場在短期到中期和長期內的可能發展,在 2022-2035 年期間。為了解決未來的不確定性並增加我們的模型的穩健性,我們提供了三種市場預測方案,即保守、基礎和樂觀的情景,代表了行業增長的不同軌跡。

Frequently Asked Questions

常見問題

  • Who are the leading players engaged in offering AI-based digital pathology / AI pathology in the healthcare domain?
  • Which geographies emerged as key hubs for AI-based digital pathology providers?
  • Which type of end-users are primarily employing AI in digital pathology in their regular workflow?
  • What type of funding initiatives are most commonly being reported by stakeholders in this domain?
  • What are the key strategies that can be implemented by emerging players to enter the AI-based digital pathology market?
  • What are the key market trends and driving factors that are likely to impact the growth of the AI-based digital pathology / AI pathology market?
  • How is the current and future opportunity likely to be distributed across key market segment?
  • 在醫療保健領域提供基於 AI 的數字病理學/AI 病理學的主要參與者是誰?
  • 哪些地區成為基於 AI 的數字病理學提供商的關鍵樞紐?
  • 哪種類型的終端使用者主要在其常規工作流程中使用人工智慧進行數位病理學?
  • 在這個領域的利益相關者最常報告哪些類型的資助計劃?
  • 新興參與者進入基於 AI 的數字病理學市場可以實施哪些關鍵策略?
  • 哪些關鍵市場趨勢和驅動因素可能會影響基於 AI 的數字病理學/AI 病理學市場的增長?
  • 當前和未來的機會如何在關鍵細分市場中分配?

Key Topics Covered:

涵蓋的關鍵主題:

1. PREFACE
1.1. Chapter Overview
1.2. Market Segmentations
1.3. Research Methodology
1.4. Key Questions Answered
1.5. Chapter Outlines

1.序言
1.章節概述
1.2.市場細分
1.3.研究方法
1.4.回答的關鍵問題
1.5.章節大綱

2. EXECUTIVE SUMMARY

2.執行摘要

3. INTRODUCTION
3.1. Chapter Overview
3.2. Artificial Intelligence in Digital Pathology
3.3. Workflow of AI-based Digital Pathology
3.4. Applications of AI-based Digital Pathology Solutions
3.5. Regulatory Requirements Focused on AI-based Digital Pathology:
3.6. Challenges Associated with the Use of AI in Digital Pathology
3.7. Future Perspectives

3.介紹
3.1.章節概述
3.2.數位病理學中的人工智慧
3.3.基於 AI 的數字病理學工作流程
3.4.基於 AI 的數字病理解決方案的應用
3.5.關注基於 AI 的數字病理學的法規要求:
3.6.人工智慧在數位病理學中應用的相關挑戰
3.7.未來觀點

4. AI-BASED DIGITAL PATHOLOGY: MARKET LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Digital Pathology Providers: Overall Market Landscape
4.3. AI-based Digital Pathology Providers: Developer Landscape

4.基於 AI 的數字病理學:市場格局
4.1.章節概述
4.2.基於 AI 的數字病理學提供商:整體市場格局
4.3.基於 AI 的數字病理學提供商:開發人員格局

5. AI-BASED DIGITAL PATHOLOGY MARKET: KEY INSIGHTS
5.1. Chapter Overview
5.1.1. Analysis by Type of Service and Area of Application
5.1.2. Analysis by Type of Feature and Area of Application
5.1.3. Analysis by Type of Product and Area of Application
5.1.4. Analysis by Type of Product and Location of Headquarters
5.1.5. Analysis by Company Size and Location of Headquarters

5.基於 AI 的數字病理學市場:關鍵見解
5.1.章節概述
5.1.1.按服務類型及應用領域分析
按特徵類型和應用領域分析
5.1.3.按產品類型和應用領域分析
按產品類型及總部位置分析
五點一按公司規模及總部位置分析

6. COMPANY PROFILES
6.1. Chapter Overview
6.2. PathAI
6.2.1. Company Overview
6.2.2. Recent Developments and Future Outlook
6.3. Paige
6.4. Akoya Biosciences
6.5. PROSCIA
6.6. Visiopharm
6.7. Roche Tissue Diagnostics
6.8. Aiforia Technologies
6.9. Indica Labs
6.10. Ibex Medical Analytics

6.公司概況
6.1.章節概述
6.2.泰式炒飯
公司概況
6.2.2.近期發展及未來展望
6.3.佩奇
6.4.日本海水生物科學
6.5.箴言
6.6.視覺藥物
6.7.羅氏組織診斷
6.8.艾福利亞技術
6.9. 实验室
6.10.山羊醫學分析

7. COMPANY COMPETITIVENESS ANALYSIS
7.1. Chapter Overview
7.2. Assumptions and Key Parameters
7.3. Methodology
7.4. Benchmarking of Portfolio Strength
7.5. Benchmarking of Funding Strength
7.6. Company Competitiveness Analysis: Small Players
7.7. Company Competitiveness Analysis: Mid-sized Players
7.8. Company Competitiveness Analysis: Large Players

7.公司競爭力分析
7.1.章節概述
7.2.假設和關鍵參數
7.3.方法論
7.4.投資組合強度的基準
7.5.資金實力的基準
7.6.公司競爭力分析:小玩家
7.7.公司競爭力分析:中型企業
7.8.公司競爭力分析:大型企業

8. FUNDING AND INVESTMENTS
8.1. Chapter Overview
8.2. Types of Funding
8.3. AI-based Digital Pathology: List of Funding and Investments
8.4. Concluding Remarks

8.資金和投資
8.章節概述
8.2.資助類型
8.3.基於 AI 的數字病理學:資金和投資清單
總結備註

9. DEMAND ANALYSIS
9.1. Chapter Overview
9.2. Scope and Methodology
9.3. Global Demand for AI-based Digital Pathology, 2022-2035
9.4. Demand for AI-based Digital Pathology: Analysis by Geography
9.5. Demand for AI-based Digital Pathology: Analysis by Type of End-user
9.6. Concluding Remarks

9.需求分析
9.1.章節概述
9.2.範圍及方法
9.3.2022-2035 年全球對基於人工智能的數字病理學需求
9.4.基於 AI 的數字病理學的需求:按地理分析
9.人工智能數字病理學的需求:按最終用戶類型分析
9.6.總結備註

10. MARKET SIZING AND OPPORTUNITY ANALYSIS
10.1. Chapter Overview
10.2. Forecast Methodology and Key Assumptions
10.3. Global AI-based Digital Pathology Market, 2022-2035
10.4. AI-based Digital Pathology Market: Analysis by Type of Neural Network, 2022 and 2035
10.5. AI-based Digital Pathology Market: Analysis by Type of Assay, 2022 and 2035
10.6. AI-based Digital Pathology Market: Analysis by Type of End-user, 2022 and 2035
10.7. AI-based Digital Pathology Market: Analysis by Area of Application, 2022 and 2035
10.8. AI-based Digital Pathology Market: Analysis by Target Disease Indication, 2022 and 2035
10.9. AI-based Digital Pathology Market: Analysis by Key Geographies, 2022 and 2035

10.市場規模和機會分析
章節概述
10.2.預測方法及主要假設
全球基於人工智能的數字病理學市場
10.4.基於人工智能的數字病理學市場:按 2022 年和 2035 年的神經網絡類型分析
10.5.基於人工智能的數字病理學市場:按分析類型分析,2022 年和 2035 年
10.6.基於人工智能的數字病理學市場:按終端用戶類型分析,2022 年和 2035 年
10.7.基於人工智能的數字病理學市場:按應用領域分析,2022 年和 2035 年
10.8.基於人工智能的數字病理學市場:按目標疾病指示分析,2022 年和 2035 年
第一次基於人工智能的數字病理學市場:按主要地理位置分析,2022 年和 2035 年

11. CONCLUDING REMARKS

11.總結言論

12. EXECUTIVE INSIGHTS
12.1. Chapter Overview
12.2. aetherAIInterview Transcript: Joe Yeh (Chief Executive Officer and Chairman)
12.3. CTL Clinitech LabInterview Transcript: Suraj Bramhane (Laboratory Director and Chief Pathologist)
12.4. Huron Digital Pathology Interview Transcript: Savvas Damaskinos (Vice President, Research and Technology)
12.5. Mindpeak Interview Transcript: Anil Berger (Vice President, Sales and Marketing)
12.6. PramanaInterview Transcript: Scott Wallace (Vice President, Business Development and Strategic Partnerships)

12.行政洞察
12.1.章節概述
12.2. 乙太海面試成績單: 喬·葉 (行政總裁兼主席)
12.3.CTL Clinitech 實驗室面試成績單:布拉哈內(實驗室主任兼首席病理學家)
12.4.休倫數字病理學訪談成績單: 萨瓦斯·达马斯基诺斯 (研究及科技副總裁)
12.5.Mindpeak 面試成績單:阿尼爾·貝格(銷售和市場營銷副總裁)
12.6.面試成績單: 斯科特·华莱士 (業務發展及策略合作夥伴副總裁)

13. APPENDIX 1: TABULATED DATA

13.附錄 1:表格數據

14. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATION

14.附錄 2:公司和組織名單

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