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放射學人工智能 (AI) 市場 - 預測 2023-2028
市場調查報告書
商品編碼
1279626

放射學人工智能 (AI) 市場 - 預測 2023-2028

Artificial Intelligence (AI) in Radiology Market - Forecasts from 2023 to 2028
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 145 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

放射學領域的人工智能市場預計到 2028 年將達到 6,433,214,000 美元，複合年增長率為 29.4%，較 2021 年的市場規模 1,058,824,000 美元增長。 人工智能（AI）中使用的深度學習算法極大地推進了視覺識別應用。 隨著變分自動編碼器和卷積神經網絡等技術的實現，醫學圖像分析領域正在迅速發展。 與放射線圖像質量的傳統定性評估相比，人工智能技術擅長自動發現圖像數據中的複雜模式。 在放射學領域，人工智能 (AI) 算法已被創建來量化特定的輻射特性，例如腫瘤的 3D 形狀、腫瘤內的紋理以及像素強度的分佈。

在放射攝影中，由合格的醫療專業人員對醫學圖像進行視覺評估，並記錄結果以用於疾病識別、描述和隨訪。 此類評估通常依賴於知識和經驗，有時還會受到意見的影響。 與這種主觀分析相反，人工智能擅長看穿圖像數據中的複雜模式，並可以自動進行定量評估。 通過將人工智能引入醫療領域作為醫生的輔助工具，更準確、高重複性的放射診斷成為可能。

主要醫療公司特定應用程序和將人工智能引入醫學的企業的興起是放射學人工智能市場的主要驅動力。

人工智能 (AI) 在醫學成像（包括成像、放射成像和判讀）中的應用是最有前途的健康創新領域之一。 隨著醫療保健許多領域的技術進步，集成人工智能 (AI) 的軟件，例如機器學習 (ML) 技術和系統，正在成為一些醫療設備中越來越重要的因素。 其最重要的優勢之一是機器學習能夠從醫療保健行業每天捕獲的大量數據中獲取有用且有意義的見解。 機器學習系統和軟件應用於常規放射線照片、CT、MRI、PET 掃描和放射學報告等放射診斷數據，可自動識別複雜的模式，幫助醫生做出明智的決策。支持您做

此外，還有幾家政府支持的初創企業正在推進人工智能用於放射治療。 例如，2016年成立並得到印度政府支持的印度初創公司Qure.ai，採用深度學習算法來分析CT、X射線和MRI圖像，以檢測並自動診斷疾病，並生成報告。 該公司通過政府資源中心 NITI Aayog 獲得政府支持，其放射學解決方案已在印度多個邦實施。

從應用來看，放射學人工智能市場預計在神經治療領域將強勁增長。

在體積腫瘤分割的基礎上，人工智能（AI）可以以高精度和一致性增強腦腫瘤和其他神經系統癌症的識別和檢測。 還可以通過 MRI 掃描自動定位腦腫瘤。 這些方法不僅對於做出準確的診斷非常有用，而且對於以可重複和公正的方式追蹤腫瘤治療的功效也非常有用。 預測治療結果是人工智能在神經腫瘤學中應用的另一種方式。 正在開發一種機器學習技術，利用基於 MRI 的血容量分佈數據來預測術前神經膠質瘤的存活率。

亞太地區預計將佔據放射學人工智能市場的很大一部分。

按地區劃分，診斷放射學領域的人工智能市場分為北美、南美、歐洲、中東和非洲以及亞太地區。 由於該地區研究支出的增加以及醫療和生物技術行業的進步，預計亞太地區將在放射學人工智能市場中佔據很大份額。 此外，龐大患者群體的存在預計將推動對增強治療設施的需求，並刺激醫療保健行業的增長，從而支持人工智能在該地區放射治療領域的擴展。 亞太地區的市場發展也受益於支出，特別是在醫療領域開發和採用新技術改進的支出。 該地區的經濟越來越注重建立強大的醫療保健系統來診斷和治療患者。

內容

第 1 章簡介

  • 市場概覽
  • 市場定義
  • 調查範圍
  • 市場細分
  • 貨幣
  • 先決條件
  • 基準年和預測年時間表

第 2 章研究方法

  • 調查數據
  • 調查設計

第 3 章執行摘要

  • 調查要點

第 4 章市場動態

  • 市場促進因素
  • 市場抑制因素
  • 波特五力分析
  • 行業價值鏈分析

第 5 章放射學人工智能 (AI) 市場：按技術分類

  • 簡介
  • 計算機輔助檢測
  • 器官自動分割
  • 自然語言處理
  • 諮詢
  • 定量和反應動力學
  • 其他

第 6 章放射學人工智能 (AI) 市場：按應用分類

  • 簡介
  • 乳房X光檢查
  • 胸部影像學檢查
  • 神經病學
  • 心血管
  • 其他

第 7 章放射學領域的人工智能 (AI) 市場：按最終用戶劃分

  • 簡介
  • 醫院
  • 影像診斷中心
  • 其他

第 8 章放射學領域的人工智能 (AI) 市場：按地區劃分

  • 簡介
  • 北美
    • 美國
    • 加拿大
    • 墨西哥
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他
  • 歐洲
    • 德國
    • 法國
    • 英國
    • 西班牙
    • 其他
  • 中東和非洲
    • 沙特阿拉伯
    • 阿拉伯聯合酋長國
    • 以色列
    • 其他
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 印度尼西亞
    • 台灣
    • 其他

第 9 章競爭格局與分析

  • 主要公司及戰略分析
  • 新興公司和市場盈利能力
  • 合併、收購、協議與合作
  • 供應商競爭力矩陣

第 10 章公司簡介

  • Microsoft Corporation
  • Amazon Web Services Inc.
  • IBM Corporation
  • Rad AI
  • Behold.ai
  • IMAGEN
  • Aidoc
  • Koninklijke Philips N.V.
  • GE Healthcare
  • Siemens Healthcare GmbH
簡介目錄
Product Code: KSI061614385

AI in radiology market is expected to grow at a CAGR of 29.4% from a market size of US$1,058.824 million in 2021 to reach US$6,433.214 million in 2028. Deep learning algorithms used in artificial intelligence (AI) have made significant advancements in visual identification applications. The domain of medical image analysis is developing quickly due to several implementations of techniques like variational autoencoders and convolutional neural networks. In contrast to traditional qualitative evaluations of radiographic qualities, AI techniques excel at automatically spotting intricate patterns in imaging data. In radiology, artificial intelligence (AI) algorithms are created to quantify particular radiographic properties, such as the 3D geometry of a tumor or the intratumoral texture and distribution of pixel intensities.

In radiography, qualified medical professionals visually evaluate medical pictures and record conclusions to locate, describe, and track diseases. Such evaluation is frequently dependent on knowledge and experience and is occasionally susceptible to opinion. In comparison to such subjective analysis, AI is excellent at seeing intricate patterns in imaging data and can automatically deliver a quantitative assessment. When AI is incorporated into the medical system as a tool to aid doctors, radiological assessments can then be conducted with greater accuracy and reproducibility.

Growing applications and ventures by major medical firms to incorporate AI in healthcare have been major drivers of the AI in radiology market.

The use of artificial intelligence (AI) in medical imaging, including image processing, radiography, and interpretation, is one of the most prospective sectors of health innovation. As technology progresses in many areas of healthcare, software integrating artificial intelligence (AI), such as machine learning (ML) technology and systems, has become an increasingly important part of several medical equipment. The capacity of machine learning to derive useful and essential insights from the enormous amounts of data acquired daily in the healthcare industry is one of its most important advantages. When applied to radiology data such as traditional radiography, CT, MRI, and PET scans as well as radiology reports, machine learning systems, and the software automatically recognize complicated patterns and assist doctors in making informed decisions.

Furthermore, there are several start-ups that have received support from the government to promote AI for radiology purposes. For instance, Qure.ai, an Indian start-up started in 2016 and supported by the Indian government, employs deep learning algorithms to analyze CT, X-ray, and MRI images to detect disease and generate automated diagnostic reports. The company has received support from the government through the NITI Aayog, a government resource center, and its radiology solutions have been implemented in several states of India.

Recent Developments:

  • In February 2023, photographic film supplier Fujifilm Australia announced the expansion of its line of artificial intelligence products into the healthcare business by collaborating with Australian imaging experts annalise.ai and their CXR Edge solution, a chest X-ray AI decision-support solution for mobile and stationary X-ray equipment. As its Medical Systems division has grown in recent years, Fujifilm has achieved some notable landmarks, including the creation of Fuji Computed Radiography, which employs the first digital method for digitizing X-ray pictures ever devised. The businesses worked together to create Annalise CXR Edge, a software medical gadget for mobile and fixed X-ray machines that would aid point-of-care doctors and radiologists in the interpretation of chest X-rays. The device's AI system detects the presence of radiological discoveries and notifies the user of potential findings in less than 10 seconds.
  • In February 2023, Avicenna.ai, an AI platform for radiology screening that improves the identification of common illnesses, announced a €7 million series A investment, increasing its capital base to €10 million. The solution offered by Avicenna.AI employs image-trained deep learning to recognize, identify, and quantify life-threatening illnesses in radiology laboratories. The Avicenna.AI platform is instructed using CT scan imaging that radiologists use to prioritize patients who are exhibiting symptoms before a diagnosis has been established. The AI software offers two variants, one to analyze symptoms and the danger of a heart attack and the other to detect brain injury and stroke risk. The platform from Avicenna.AI is used to categorize the severity of patient conditions, assisting radiologists in deciding whether the patient's life is in danger.

Based on application, the AI in radiology market is expected to witness strong growth in the neurological treatment sector.

Using volumetric tumor segmentation as the basis for its work, artificial intelligence (AI) can help enhance the identification and detection of brain tumors and other neurological cancers with high accuracy and consistency. The system can also automatically locate brain tumors on MRI scans. These methods can be very helpful in making accurate diagnoses as well as helping to track the effectiveness of tumor therapy in a repeatable and unbiased manner. Outcome prediction is another way AI is used in neuro-oncology. Machine learning techniques have been developed to forecast preoperative glioma survival using MRI-based blood volume distribution data.

Asia Pacific is anticipated to hold a sizable portion of the AI in radiology market.

Based on geography, the AI in radiology market is segmented into North America, South America, Europe, the Middle East and Africa, and Asia Pacific. Due to increased research spending and advancements in the medical and biotech industries in the area, the Asia Pacific is anticipated to hold significant shares of the AI in radiology market. Additionally, the presence of a sizable patient base is anticipated to boost the requirement for enhanced treatment facilities and stimulate the growth of the healthcare sector, which will assist the expansion of AI in radiology in the area. The market in Asia Pacific has also benefited from expenditure in the healthcare sector, particularly to develop and incorporate new technological improvements. The region's economies are putting more of an emphasis on building a strong healthcare system for patient diagnosis and treatment.

Market Segmentation

By Technology

  • Computer-aided Detection
  • Auto-segmentation of Organs
  • Natural Language Processing
  • Consultation
  • Quantification and Kinetics
  • Others

By Application

  • Mammography
  • Chest Imaging
  • Neurology
  • Cardiovascular
  • Others

By End-User

  • Hospitals
  • Diagnostic Imaging Centers
  • Others

By Geography

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

TABLE OF CONTENTS

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. Research Design

3. EXECUTIVE SUMMARY

  • 3.1. Research Highlights

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's 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 MARKET BY TECHNOLOGY

  • 5.1. Introduction
  • 5.2. Computer-aided Detection
  • 5.3. Auto-segmentation of Organs
  • 5.4. Natural Language Processing
  • 5.5. Consultation
  • 5.6. Quantification and Kinetics
  • 5.7. Others

6. AI IN RADIOLOGY MARKET BY APPLICATION

  • 6.1. Introduction
  • 6.2. Mammography
  • 6.3. Chest Imaging
  • 6.4. Neurology
  • 6.5. Cardiovascular
  • 6.6. Others

7. AI IN RADIOLOGY MARKET BY END-USER

  • 7.1. Introduction
  • 7.2. Hospitals
  • 7.3. Diagnostic Imaging Centers
  • 7.4. Others

8. AI IN RADIOLOGY MARKET BY GEOGRAPHY

  • 8.1. Introduction
  • 8.2. North America
    • 8.2.1. USA
    • 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. Germany
    • 8.4.2. France
    • 8.4.3. United Kingdom
    • 8.4.4. Spain
    • 8.4.5. Others
  • 8.5. Middle East and Africa
    • 8.5.1. Saudi Arabia
    • 8.5.2. UAE
    • 8.5.3. Israel
    • 8.5.4. Others
  • 8.6. Asia Pacific
    • 8.6.1. China
    • 8.6.2. Japan
    • 8.6.3. India
    • 8.6.4. South Korea
    • 8.6.5. Indonesia
    • 8.6.6. Taiwan
    • 8.6.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 9.1. Major Players and Strategy Analysis
  • 9.2. Emerging Players and Market Lucrativeness
  • 9.3. Mergers, Acquisition, Agreements, and Collaborations
  • 9.4. Vendor Competitiveness Matrix

10. COMPANY PROFILES

  • 10.1. Microsoft Corporation
  • 10.2. Amazon Web Services Inc.
  • 10.3. IBM Corporation
  • 10.4. Rad AI
  • 10.5. Behold.ai
  • 10.6. IMAGEN
  • 10.7. Aidoc
  • 10.8. Koninklijke Philips N.V.
  • 10.9. GE Healthcare
  • 10.10. Siemens Healthcare GmbH