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放射学人工智能 (AI) 市场 - 预测 2023-2028
市场调查报告书
商品编码
1279626

放射学人工智能 (AI) 市场 - 预测 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个工作天内

价格
简介目录

放射学领域的人工智能市场预计到 2028 年将达到 6,433,214,000 美元，复合年增长率为 29.4%，较 2021 年的市场规模 1,058,824,000 美元增长。 人工智能（AI）中使用的深度学习算法极大地推进了视觉识别应用。 随着变分自动编码器和卷积神经网络等技术的实现，医学图像分析领域正在迅速发展。 与放射线图像质量的传统定性评估相比，人工智能技术擅长自动发现图像数据中的复杂模式。 在放射学领域，人工智能 (AI) 算法已被创建来量化特定的辐射特性，例如肿瘤的 3D 形状、肿瘤内的纹理以及像素强度的分布。

在放射摄影中，由合格的医疗专业人员对医学图像进行视觉评估，并记录结果以用于疾病识别、描述和随访。 此类评估通常依赖于知识和经验，有时还会受到意见的影响。 与这种主观分析相反，人工智能擅长看穿图像数据中的复杂模式，并可以自动进行定量评估。 通过将人工智能引入医疗领域作为医生的辅助工具，更准确、高重复性的放射诊断成为可能。

主要医疗公司特定应用程序和将人工智能引入医学的企业的兴起是放射学人工智能市场的主要驱动力。

人工智能 (AI) 在医学成像（包括成像、放射成像和判读）中的应用是最有前途的健康创新领域之一。 随着医疗保健许多领域的技术进步，集成人工智能 (AI) 的软件，例如机器学习 (ML) 技术和系统，正在成为一些医疗设备中越来越重要的因素。 其最重要的优势之一是机器学习能够从医疗保健行业每天捕获的大量数据中获取有用且有意义的见解。 机器学习系统和软件应用于常规放射线照片、CT、MRI、PET 扫描和放射学报告等放射诊断数据，可自动识别复杂的模式，帮助医生做出明智的决策。支持您做

此外，还有几家政府支持的初创企业正在推进人工智能用于放射治疗。 例如，2016年成立并得到印度政府支持的印度初创公司Qure.ai，采用深度学习算法来分析CT、X射线和MRI图像，以检测并自动诊断疾病，并生成报告。 该公司通过政府资源中心 NITI Aayog 获得政府支持，其放射学解决方案已在印度多个邦实施。

从应用来看，放射学人工智能市场预计在神经治疗领域将强劲增长。

在体积肿瘤分割的基础上，人工智能（AI）可以以高精度和一致性增强脑肿瘤和其他神经系统癌症的识别和检测。 还可以通过 MRI 扫描自动定位脑肿瘤。 这些方法不仅对于做出准确的诊断非常有用，而且对于以可重复和公正的方式追踪肿瘤治疗的功效也非常有用。 预测治疗结果是人工智能在神经肿瘤学中应用的另一种方式。 正在开发一种机器学习技术，利用基于 MRI 的血容量分布数据来预测术前神经胶质瘤的存活率。

亚太地区预计将占据放射学人工智能市场的很大一部分。

按地区划分，诊断放射学领域的人工智能市场分为北美、南美、欧洲、中东和非洲以及亚太地区。 由于该地区研究支出的增加以及医疗和生物技术行业的进步，预计亚太地区将在放射学人工智能市场中占据很大份额。 此外，庞大患者群体的存在预计将推动对增强治疗设施的需求，并刺激医疗保健行业的增长，从而支持人工智能在该地区放射治疗领域的扩展。 亚太地区的市场发展也受益于支出，特别是在医疗领域开发和采用新技术改进的支出。 该地区的经济越来越注重建立强大的医疗保健系统来诊断和治疗患者。

内容

第 1 章简介

  • 市场概览
  • 市场定义
  • 调查范围
  • 市场细分
  • 货币
  • 先决条件
  • 基准年和预测年时间表

第 2 章研究方法

  • 调查数据
  • 调查设计

第 3 章执行摘要

  • 调查要点

第 4 章市场动态

  • 市场促进因素
  • 市场抑制因素
  • 波特五力分析
  • 行业价值链分析

第 5 章放射学人工智能 (AI) 市场：按技术分类

  • 简介
  • 计算机辅助检测
  • 器官自动分割
  • 自然语言处理
  • 咨询
  • 定量和反应动力学
  • 其他

第 6 章放射学人工智能 (AI) 市场：按应用分类

  • 简介
  • 乳房X光检查
  • 胸部影像学检查
  • 神经病学
  • 心血管
  • 其他

第 7 章放射学领域的人工智能 (AI) 市场：按最终用户划分

  • 简介
  • 医院
  • 影像诊断中心
  • 其他

第 8 章放射学领域的人工智能 (AI) 市场：按地区划分

  • 简介
  • 北美
    • 美国
    • 加拿大
    • 墨西哥
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他
  • 欧洲
    • 德国
    • 法国
    • 英国
    • 西班牙
    • 其他
  • 中东和非洲
    • 沙特阿拉伯
    • 阿拉伯联合酋长国
    • 以色列
    • 其他
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 印度尼西亚
    • 台湾
    • 其他

第 9 章竞争格局与分析

  • 主要公司及战略分析
  • 新兴公司和市场盈利能力
  • 合併、收购、协议与合作
  • 供应商竞争力矩阵

第 10 章公司简介

  • Microsoft Corporation
  • Amazon Web Services Inc.
  • IBM Corporation
  • Rad AI
  • Behold.ai
  • IMAGEN
  • Aidoc
  • Koninklijke Philips N.V.
  • GE Healthcare
  • Siemens Healthcare GmbH
简介目录
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