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市場調查報告書

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1410142

放射學報告產生中人工智慧的全球市場：預測（2023-2028）

AI in Radiology Report Generation Market - Forecasts from 2023 to 2028

出版日期: 2023年12月22日 | 出版商:

Knowledge Sourcing Intelligence | 英文 140 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

全球放射學報告人工智慧市場規模預計在預測期內將以 33.98% 的複合年成長率成長。

人工智慧在醫療保健領域的變革力量正在改變放射學報告的人工智慧市場。人工智慧演算法與核醫影像設備無縫整合，以前所未有的精度和速度分析和解釋醫學影像。這項突破性技術可自動產生報告、提高工作效率並減少放射科醫生的工作量。人工智慧產生的報告高度準確，能夠及早發現異常情況並更好地治療患者。此外，人工智慧技術加快了放射科醫生的工作流程，使他們能夠專注於更困難的情況。隨著對快速、準確診斷的需求不斷成長，放射學報告市場中的人工智慧有望成為遊戲規則的改變者，提供更好的患者治療結果並簡化醫療保健業務，以實現更有效率、更有效的未來。事實證明確實如此。

醫學影像資料量的增加推動放射學報告產生的人工智慧市場成長

醫學影像資料量的不斷增加是放射學報告產生領域人工智慧市場的主要驅動力。隨著醫療機構和組織使用數位成像技術，創建的醫學影像數量呈指數級成長。這些海量資料由 X 光、MRI、電腦斷層掃描和其他診斷工具組成，形成了一個龐大的關鍵診斷資訊庫。手動分析如此大量的照片非常耗時，而且容易出現人為錯誤。深度學習演算法尤其擅長以驚人的速度和準確性處理和解釋此類資料。人工智慧演算法可以快速分析並從這些照片中提取關鍵訊息，幫助放射科醫生快速產生完整、準確的報告。人工智慧更好地處理大量資料的能力加速了其在放射學領域的接受度，並顯著改善了醫療結果。

放射學報告創建的人工智慧市場對自動報告創建的需求不斷成長

對更高效率、準確性和工作流程最佳化的需求正在推動放射學報告人工智慧市場對自動報告產生的需求不斷成長。傳統的手動報告產生過程非常耗時且容易出現人為錯誤，這可能會導致患者照護的延遲。採用人工智慧演算法的自動化簡化了報告生成流程，顯著縮短了周轉時間並提高了整體放射學效率。透過利用最新的自然語言處理 (NLP) 和影像識別演算法，人工智慧系統可以評估醫學影像、提取相關資訊並提供完整、標準化的報告。這不僅節省了放射科醫生的時間，而且還確保了統一和準確的報告，支持改善患者照護，並實現醫療專業人員之間的快速溝通。隨著醫療保健組織努力改善診斷和患者治療結果，對自動化報告的需求不斷成長。

人工智慧開拓與醫療機構的合作擴大了放射學報告中人工智慧的市場規模

在放射學報告的人工智慧市場中，人工智慧開發者和醫療機構之間的合作變得越來越重要。人工智慧開發人員在設計複雜演算法方面的獨特經驗，與醫療保健組織的深入主題知識相結合，提供了巨大的協同效應。醫療機構擁有龐大的醫療資料庫和真實的臨床資料，可用於訓練和檢驗人工智慧演算法。同時，人工智慧開發人員正在提供尖端工具和處理資源，以快速處理和分析大量醫療圖像資料。此類合作將有助於加速人工智慧驅動的放射學報告技術的開發和實施，鼓勵創新並提高診斷準確性。與醫學專家密切合作，使人工智慧解決方案能夠響應臨床需求並解決特定問題，從而改善患者照護並最佳化放射學工作流程。

北美是放射學報告領域人工智慧的市場領導者

北美被公認為放射學報告領域人工智慧的市場領導者。這是由於該地區強大的基礎設施、卓越的醫療保健系統以及在人工智慧技術上的大量支出。世界一流的醫學研究機構、科技公司的存在，以及醫療提供者與人工智慧研究人員和開發人員之間的合作，正在加速人工智慧在放射學領域的應用。此外，有利的法律規範和對將人工智慧融入醫療保健流程的重視正在支持北美在推動放射學報告系統的人工智慧改進方面發揮領導作用。因此，放射學報告生成領域的人工智慧市場隨著時間的推移正在顯著擴大。

在人工智慧市場中採用遠端醫療和遠端醫療解決方案進行放射學報告。

遠端醫療和遠端醫療解決方案的普及是放射學報告市場人工智慧的主要驅動力。遠端醫療允許醫療保健專業人員與遠端位置的患者進行通訊，並實現醫療資訊和診斷成像資料的交換。人工智慧驅動的放射學報告解決方案在此類環境中至關重要，因為它們可以有效分析醫學影像並即時提供正確的報告。將人工智慧應用於遠端醫療將改善放射服務的可近性，特別是在農村和服務不足的地區，並實現更快、更有效的診斷和治療計劃。此外，人工智慧驅動的遠端醫療解決方案可以消除面對面諮詢的需要，並實現醫療保健提供者之間的無縫協作。隨著遠端醫療在世界各地變得普及，在放射學報告中引入人工智慧預計將進一步改變醫療保健服務。

主要進展：

2023 年6 月，Aidoc 宣布與Ochsner Health 建立突破性的合作夥伴關係，Ochsner Health 是一家總部位於新奧爾良的領先醫療保健組織，在墨西哥灣南部營運著46 家醫院和370 多個醫療和緊急護理中心。此次合作將 Ochsner 的臨床人才與 Aidoc 先進的人工智慧技術的力量相結合，以改善路易斯安那州和全部區域的醫療保健提供、體驗和最佳化方式。
2022 年 8 月，領先的醫療保健資訊科技公司 Enlitic Inc. 宣布與 GE Healthcare (GE) 建立新的長期合作關係，以提高業務效率並造福GE 在全球範圍內的放射科醫生和患者，改善您的治療結果。 GE 將把 Enlitic 專有的基於人工智慧的 Curie 平台整合到 GE 的放射科醫生工作流程中，以促進資料標準化並提高系統效率和容量。
2021年11月，以色列影像處理公司Nanox以約1.1億美元股票完成與Zebra Medical Vision（現更名為Nanox.AI）的合併，並宣布可能追加8,400萬美元股權。

公司產品

Watson Imaging AI： IBM Watson Health 提供人工智慧驅動的影像分析功能，幫助放射科醫師更準確、更有效地分析醫學影像。 Watson Imaging AI 平台使用深度學習演算法來分析 X 光、MRI 和電腦斷層掃描等放射影像，以識別潛在的異常並建立完整的報告。
Nuance PowerScribe One： PowerScribe One 是一個使用 AI 和自然語言處理 (NLP) 建立放射學報告的完整平台。該平台與核醫影像設備結合，利用人工智慧演算法分析醫學影像，擷取關鍵資料，並自動提供完整、準確的報告。
Enlitic AI 平台： Enlitic 建立了強大的 AI 平台，用於分析 X 光、電腦斷層掃描和 MRI 等醫學影像。該公司的技術使深度學習演算法來幫助放射科醫生更準確、更快速地檢測和診斷許多醫療狀況。
Zebra AI1(TM) 分析平台： Zebra Medical Vision 建構了創新的人工智慧分析平台，用於分析醫學影像資料並提供完整的放射學報告。深度學習演算法已用於分析多種顯像模式，包括電腦斷層掃描、X 光和乳房 X 光檢查，使放射科醫生能夠更準確地檢測和診斷醫療狀況。

The AI in radiology report generation market has been transformed by AI's transformational powers in healthcare. AI algorithms analyse and interpret medical pictures with unprecedented precision and speed by seamlessly integrating with radiological imaging equipment. This game-changing technology automates report production, increasing productivity and decreasing radiologists' workload. The AI-generated reports are extremely accurate, allowing for earlier detection of irregularities and better patient treatment. Furthermore, AI-powered technologies speed up radiologists' workflow, allowing them to focus on more difficult situations. As the need for quick and precise diagnoses develops, AI in radiology report generation market has shown to be a game changer, offering better patient outcomes and simplifying healthcare operations for a more efficient and effective future.

Increasing Volume of Medical Imaging Data Enhances the AI in Radiology Report Generation Market Growth.

The growing volume of medical imaging data is a major driving force in the AI in radiology report generation market. The volume of medical pictures created has increased tremendously as medical facilities and healthcare organisations use digital imaging technologies. This flood of data comprises X-rays, MRIs, CT scans, and other diagnostic tools, resulting in a large library of vital diagnostic information. Manually analysing such a large number of photos can be time-consuming and prone to human error. Deep learning algorithms, in particular, excel in processing and interpreting such data at unparalleled speed and precision. AI algorithms can swiftly analyse and extract significant information from these pictures, assisting radiologists in fast producing thorough and exact reports. The capacity of AI to successfully handle this data flood has accelerated its acceptance in the radiology area, greatly improving healthcare results.

Rising Demand for Automated Report Generation in AI in Radiology Report Generation Market.

The need for greater efficiency, accuracy, and workflow optimisation is driving the growing demand for automated report generating in the AI in radiology report generating market. Traditional manual report-generating procedures can be time-consuming and prone to human mistakes, potentially resulting in patient care delays. Automation with AI-powered algorithms streamlines the report-generating process, drastically cutting turnaround times and enhancing radiology departments' overall efficiency. AI systems can evaluate medical pictures and extract pertinent information to provide complete and standardised reports by utilising modern natural language processing (NLP) and image recognition algorithms. This not only saves radiologists time but also assures uniform and accurate reporting, supporting improved patient care and allowing prompt communication among healthcare professionals. The need for automated report production continues to rise as healthcare institutions strive for better diagnosis and patient outcomes.

Collaborations between AI Developers and Healthcare Institutions Boost the AI in Radiology Report Generation Market Size.

Collaborations between AI developers and healthcare institutions are becoming increasingly important in the AI in radiology report generation market. AI developers' unique experience in designing complex algorithms, combined with healthcare institutions' in-depth topic knowledge, results in tremendous synergy. Healthcare facilities include enormous medical databases and real-world clinical data that may be used to train and validate AI algorithms. AI developers, on the other hand, contribute cutting-edge tools and processing resources to rapidly handle and analyse massive volumes of medical imaging data. These collaborations help to speed the development and implementation of AI-powered radiology report generating technologies, while also encouraging innovation and boosting diagnostic accuracy. Working closely with healthcare professionals also ensures that AI solutions correspond with clinical requirements and handle specific difficulties, resulting in improved patient care and optimised radiology workflows.

North America is the Market Leader in the AI in Radiology Report Generation Market.

North America was regarded as the market leader in the AI in radiology report generation market. This is due to the region's robust infrastructure, superior healthcare systems, and substantial expenditures in artificial intelligence technology. The existence of world-class medical research institutes, technology firms, and cooperation between healthcare providers and AI developers has accelerated the implementation of AI in radiology practices. Furthermore, favourable regulatory frameworks and an emphasis on integrating AI into healthcare processes have aided North America's leadership in pushing improvements in AI-powered radiology report production systems. So, AI in radiology report generation market is significantly expanding over time.

Adoption of Telemedicine and Remote Healthcare Solutions in AI in Radiology Report Generation Market.

The widespread use of telemedicine and remote healthcare solutions has been a major driving force in the AI in radiology report generation market. Telemedicine enables healthcare practitioners to communicate with patients at a distance, allowing the interchange of medical information and diagnostic imaging data. AI-powered radiology report creation solutions are critical in this setting because they effectively analyse medical pictures and provide correct reports in real time. The application of AI in telemedicine improves radiological service accessibility, particularly in rural or underserved locations, and enables rapid and effective diagnosis and treatment planning. Furthermore, AI-powered remote healthcare solutions eliminate the need for in-person consultations and enable seamless cooperation among healthcare providers. As telemedicine gains popularity throughout the world, the incorporation of AI in radiology report generation is projected to further revolutionise healthcare delivery.

Key Developments:

In June 2023, Aidoc announced a groundbreaking alliance with Ochsner Health, a big healthcare organisation based in New Orleans that operates 46 hospitals and over 370 health and urgent care centres throughout the Gulf South. This collaboration combines Ochsner's clinical brilliance with the power of Aidoc's sophisticated AI technologies, resulting in an alliance that improves the way healthcare is given, experienced, and optimised throughout Louisiana and the Gulf South area.
In August 2022, Enlitic Inc., a leading healthcare information technology firm, announced a new long-term relationship with GE Healthcare (GE) to improve operational efficiency and results for GE's radiologists and patients worldwide. GE will integrate Enlitic's proprietary AI-based Curie platform into GE radiologist workflows to promote data standardisation and drive system efficiency and capacity.
In November 2021, Nanox, an Israeli imaging business, announced the completion of its merger with Zebra Medical Vision, now renamed as Nanox.AI, for about $110 million in stock, with the potential for an additional $84 million in shares dependent on performance.

Company Products:

Watson Imaging AI: IBM Watson Health offers image analysis capabilities driven by AI to help radiologists analyse medical pictures more correctly and effectively. Deep learning algorithms were used by the Watson Imaging AI platform to analyse radiological images including as X-rays, MRIs, and CT scans in order to identify probable anomalies and create complete reports.
Nuance PowerScribe One: PowerScribe One was a complete platform that used AI and natural language processing (NLP) to generate radiology reports. The platform was coupled with radiological imaging equipment, and AI algorithms were utilised to analyse medical pictures, extract key data, and provide thorough and accurate reports automatically.
Enlitic AI Platform: Enlitic created a powerful AI platform for analysing medical pictures such as X-rays, CT scans, and MRIs. Their technology uses deep learning algorithms to help radiologists discover and diagnose numerous medical disorders more accurately and quickly.
Zebra AI1™ Analytics Platform: Zebra Medical Vision created an innovative artificial intelligence analytics platform to analyse medical imaging data and provide complete radiology reports. Deep learning algorithms were used to analyse numerous imaging modalities, such as CT scans, X-rays, and mammograms, enabling radiologists to detect and diagnose medical disorders more correctly.

Segmentation:

By Technology

Natural Language Processing (Nlp)
Machine Learning
Deep Learning
Computer Vision
Others

By Application

MRI Scan Report Generation
CT Scan Report Generation
X-Ray Report Generation
Ultrasound Report Generation
Mammography Report Generation
Others

By End-User

Hospitals And Clinics
Diagnostic Imaging Centers
Research Institutes And Academic Centers
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 RADIOLOGY REPORT GENERATION MARKET, BY TECHNOLOGY

5.1. Introduction
5.2. NATURAL LANGUAGE PROCESSING (NLP)
5.3. MACHINE LEARNING
5.4. DEEP LEARNING
5.5. COMPUTER VISION
5.6. OTHERS

6. AI IN RADIOLOGY REPORT GENERATION MARKET, BY APPLICATION

6.1. Introduction
6.2. MRI SCAN REPORT GENERATION
6.3. CT SCAN REPORT GENERATION
6.4. X-RAY REPORT GENERATION
6.5. ULTRASOUND REPORT GENERATION
6.6. MAMMOGRAPHY REPORT GENERATION
6.7. OTHERS

7. AI IN RADIOLOGY REPORT GENERATION MARKET, BY END-USER

7.1. Introduction
7.2. HOSPITALS AND CLINICS
7.3. DIAGNOSTIC IMAGING CENTERS
7.4. RESEARCH INSTITUTES AND ACADEMIC CENTERS
7.5. OTHERS
7.6. AI IN RADIOLOGY REPORT GENERATION MARKET, BY GEOGRAPHY
7.7. Introduction
7.8. North America
- 7.8.1. United States
- 7.8.2. Canada
- 7.8.3. Mexico
7.9. South America
- 7.9.1. Brazil
- 7.9.2. Argentina
- 7.9.3. Others
7.10. Europe
- 7.10.1. United Kingdom
- 7.10.2. Germany
- 7.10.3. France
- 7.10.4. Italy
- 7.10.5. Spain
- 7.10.6. Others
7.11. Middle East and Africa
- 7.11.1. Saudi Arabia
- 7.11.2. UAE
- 7.11.3. Others
7.12. Asia Pacific
- 7.12.1. Japan
- 7.12.2. China
- 7.12.3. India
- 7.12.4. South Korea
- 7.12.5. Indonesia
- 7.12.6. Taiwan
- 7.12.7. Others