세계의 의료 영상 처리 분야 AI 시장 : 업계 동향 및 예측 - 용도 분야별, 영상 처리 유형별, 주요 지역별

AI in Medical Imaging Market: Industry Trends and Global Forecasts - Distribution by Application Area, Type of Image Processed and Key Geographical Regions

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한글목차

의료 영상 처리 분야 AI 시장 : 개요

세계의 의료 영상 처리 분야 AI 시장 규모는 올해 17억 5,000만 달러에 달했습니다. 이 시장은 예측 기간 동안 30%의 유리한 CAGR로 성장할 전망입니다.

시장 규모 및 기회 분석은 다음 매개변수에 걸쳐 세분화됩니다.

용도 분야별

• 폐 감염 및 호흡기 질환
• 뇌 손상 및 장애
• 폐암
• 심장 질환 및 심혈관 질환
• 뼈 변형 및 정형외과 질환
• 유방암
• 기타

영상 처리 유형별

• X선
• MRI
• CT
• 초음파

주요 지역별

• 북미
• 유럽
• 아시아태평양 및 기타 지역

세계의 의료 영상 처리 분야 AI 시장 : 성장 및 동향

최근 많은 양의 의료 영상을 통해 범용성이 높은 자체 개선 알고리즘과 고급 병렬화 기술이 필요합니다. 의료 영상 기술에서 AI는 다층 구조로 상호 연결된 노드 및 뉴런 신경망을 사용하는 복잡한 기계 학습 알고리즘으로, 대량의 비구조화 데이터를 해석하여 가치 있는 인사이트를 생성할 수 있습니다. 그 결과, 이 기술은, 화상 진단이나 데이터 처리 등, 헬스케어 분야의 다양한 용도에 서서히 도입되게 되고 있습니다. 게다가 전문가는, 의료 영상 기반 영상 처리 솔루션에 AI의 뛰어난 패턴 인식 능력을 활용하는 것으로, 방사선과 의사는 보다 많은 정보에 근거한 의사결정을 실시할 수 있다고 생각하고 있습니다.

시간이 지남에 따라 일부 산업 이해 관계자는 의료 영상 분석에 특화된 독자적인 의료 영상 알고리즘 AI를 개발해 왔습니다. 현재 많은 혁신가들이 그 기술을 통해 컴퓨터를 훈련하고 의용 이미지를 해석 및 분석하며 시간적 및 공간적 패턴을 파악할 수 있다고 주장하고 있습니다. 더욱이 인공지능 주도형 진단 업계의 많은 전문가들이 의료 영상 분야 AI는 방사선 데이터의 처리와 해석을 크게 가속할 수 있다고 생각하고 있다는 점은 주목할 만합니다. 실제로 진단 속도가 약 20% 향상됐고 그 결과 출력되는 위양성률이 약 10% 감소했다고 보고하는 연구도 있습니다.

세계의 의료 영상 처리 분야 AI 시장 : 주요 인사이트

이 보고서는 세계 의료 영상 처리 분야 AI 시장의 현황을 파악하고 업계 내 잠재적인 성장 기회를 확인하고 있습니다. 본 보고서의 주요 조사 결과는 다음과 같습니다.

• 현재 방사선과 의사가 시간 효율적이고 정확한 진단 판단을 내리는 것을 지원하기 위해 의료 영상 기반 AI 솔루션이 200 가까이, 여러 기업별로 개발되고 있거나 개발 중입니다.
• 이해관계자는 독자적인 전략을 실시함으로써 이 업계에서 강력한 지위를 확립하고 있습니다. 높은 평가 및 순자산을 가진 기업은 전체적인 수익 생성 가능성에 가장 기여할 가능성이 높습니다.

• 더 나은 이미지 시각화 및 해부학적 영역의 상세한 세분화와 같은 기능을 통해 이러한 솔루션은 방사선과 의사의 효율성을 크게 향상시켜 건강 관리 비용을 절약할 수 있음을 보여줍니다.
• 유리한 장래를 예견해, 복수의 공적 및 민간 투자가가 150의 사례로 20억 달러 이상의 투자를 실시했습니다.
• 이 분야에 대한 관심 증가는 제휴 사례 증가에도 반영되고 있으며 체결된 거래의 대부분은 병원이나 진료소에서의 의료 영상 솔루션에 대한 AI의 도입에 초점을 맞춘 것이었습니다.
• 기존 기업도 신규 참가 기업도 최근 몇몇 파트너십을 맺고 있습니다. 이러한 거래는 의료 이미지 기반 AI 솔루션의 접근성을 높이기 위한 목적으로 연결되어 있습니다.
• 이 분야에서의 파트너십 활동은 유리한 CAGR로 증가하고 있습니다. 실제로 지난 3년간 가장 큰 파트너십이 보고되었습니다.
• 파트너십의 대부분(72%)은 비종양학 부문에서 체결되었습니다. 이러한 거래의 대부분은 뇌혈관 장애, 심혈관 장애, 폐 감염증과 관련되어 있었습니다.
• 의료용 영상 처리 및 의료용 영상 처리 분야 AI와 관련된 특허는 최근 몇 년간 상당수가 다양한 조직에 출원 및 부여되고 있습니다.
• 의료 이미지 기반 솔루션에서 AI 평가를 위해 여러 임상 연구가 등록되었으며 각 회사는 다양한 유형의 의료 이미지 처리에서 알고리즘의 성능에 대한 좋은 결과를 밝혔습니다.

• 시장의 성장 기회는 방사선과의에 걸리는 기존의 부담을 극복하기 위한 참신한 솔루션에 대한 요구의 고조에 의해 견인될 가능성이 높습니다. 이 기회는 다양한 용도 분야 및 영상 유형에 분산될 것으로 예측됩니다.

세계의 의료 영상 처리 분야 AI 시장 : 주요 부문

용도 분야별로는 시장은 폐감염 및 호흡기 질환, 뇌손상 및 질환, 폐암, 심질환 및 심혈관 질환, 골변형 및 정형외과 질환, 유방암 등으로 구분됩니다. 현재 세계의 의료 영상 AI 시장에서는 뇌 손상 및 장애 분야가 최대 점유율을 차지하고 있습니다. 이 동향은 향후 몇 년간도 변하지 않을 것으로 생각됩니다.

주요 지역별로 볼 때 시장은 북미, 유럽, 아시아태평양 및 기타 지역으로 구분됩니다. 북미와 유럽을 거점으로 하는 참가 기업이 점유율의 대부분을 차지할 것으로 예상됩니다.

세계의 의료 영상 처리 분야 AI 시장의 진출기업 예

• Artelus
• Arterys
• Butterfly Network
• ContextVision
• Enlitic
• Echonous
• GE Healthcare
• InferVision
• VUNO

본 보고서에서는 세계의 의료 영상 처리 분야 AI 시장에 대해 조사했으며, 시장 개요와 함께, 용도 분야별, 영상 처리 유형별 동향, 지역별 동향, 시장 진출기업 프로파일 등의 정보를 제공합니다.

목차

제1장 서문

제2장 주요 요약

제3장 서문

• 인간, 기계 및 지성
• 학습의 과학
• 인공지능
• 빅 데이터 혁명
• 헬스케어에서 의료 영상 처리 분야 AI 용도

제4장 사례 연구 : IBM Watson과 Google Deepmind

• 장의 개요
• International Business Machines(IBM)
• Google
• IBM 대 구글 : 인공지능 관련 인수
• IBM 대 구글 : 헬스케어에 특화된 파트너십 및 협업
• IBM 대 구글 : 주요 우려 사항 및 미래 전망

제5장 시장 개요

• 장의 개요
• 의료 영상 처리 분야 AI : 시장 상황
• 의료 영상 처리 분야 AI : 주요 특징에 관한 정보
• 의료 영상 처리 분야 AI : 기업 일람

제6장 기업 프로파일

• 장의 개요
• Artelus
• Arterys
• Butterfly Network
• ContextVision
• Enlitic
• Echonous
• GE Healthcare
• InferVision
• VUNO

제7장 파트너십 및 협업

• 장의 개요
• 파트너십 모델
• 의료 영상 처리 분야 AI : 파트너십 및 협업 리스트
• 결론

제8장 자금 조달 및 투자 분석

• 장의 개요
• 자금 조달의 유형
• 의료 영상 처리 분야 AI : 최근의 자금 조달 사례

제9장 기업 평가 분석

• 장의 개요
• 조사 방법
• 파라미터별 분류

제10장 사례 연구 : 미국에서 등록된 의료 영상 기반 임상시험에서 AI 분석

• 장의 개요
• 범위 및 조사 방법
• 임상 시험 분석

제11장 특허 분석

• 장의 개요
• 범위 및 조사 방법
• 의료 영상 및 의료 영상 처리 분야 AI :특허 분석
• 특허 평가 분석

제12장 비용 절감 분석

• 장의 개요
• 주요 전제 및 조사 방법
• 의료 영상 처리 솔루션에서 AI의 종합적인 비용 삭감 가능성(-2035년)
• X선 영상
• MRI 이미지
• CT 이미지
• 초음파 영상
• 결론 : 비용 절감 시나리오

제13장 시장 예측

• 장의 개요
• 예측 조사 방법 및 주요 전제조건
• 의료 영상 처리 분야 AI : 용도 분야별
• 의료 영상 처리 분야 AI : 영상 처리 유형별
• 의료 영상 처리 분야 AI : 주요 지역별
• 결론

제14장 헬스케어에서 의료 영상 처리 분야 AI : 전문가 인사이트

• 장의 개요
• 업계 전문가
• 대학과 병원 전문가
• 기타 전문가의 의견

제15장 인터뷰 기록

제16장 COVID-19의 유행이 의료 영상 시장 역학에서 AI에 미치는 영향

• 장의 개요
• COVID-19 팬데믹의 영향 평가
• 대응 전략 : 뿌리 분석의 관점

제17장 결론

제18장 부록 1 : 표 형식 데이터

제19장 부록 2 : 기업 및 단체 일람

AJY

영문 목차

영문목차

AI IN MEDICAL IMAGING MARKET: OVERVIEW

As per Roots Analysis, the global AI in medical imaging market valued at USD 1.75 billion in the current year is expected to grow at a lucrative CAGR of 30% during the forecast period.

The market sizing and opportunity analysis has been segmented across the following parameters:

Application Area

• Lung Infections / Respiratory Disorders
• Brain Injuries / Disorders
• Lung Cancer
• Cardiac Conditions / Cardiovascular Disorders
• Bone Deformities / Orthopedic Disorders
• Breast Cancer
• Others Application Areas

Type of Image Processed

• X-ray
• MRI
• CT
• Ultrasound

Key Geographical Regions

• North America
• Europe
• Asia-Pacific and Rest of the World

GLOBAL AI IN MEDICAL IMAGING MARKET: GROWTH AND TRENDS

In recent years, large volumes of medical images have created the need for versatile, self-improving algorithms and advanced parallelization techniques. AI in medical imaging technology is a complex machine learning algorithm that uses a neural network of interconnected nodes / neurons in a multi-layered structure, thereby enabling the interpretation of large volumes of unstructured data to generate valuable insights. As a result, this technology is gradually being incorporated into a variety of applications across the healthcare sector, including diagnostic imaging and data processing. Moreover, experts believe that by leveraging the superior pattern recognition ability of AI in medical imaging-based image processing solutions, radiologists can make better informed decisions.

Over time, several industry stakeholders have developed proprietary AI in medical imaging algorithms specifically for medical image analysis. Currently, numerous innovators assert that their technologies can train computers to interpret and analyze medical images, identifying both temporal and spatial patterns. Further, it is worth highlighting that many experts in the artificial intelligence driven diagnostics industry believe that AI in medical imaging can significantly accelerate the processing and interpretation of radiological data. In fact, some studies report approximately a 20% improvement in diagnostic speed and a reduction of false positive rates by about 10% in the resulting outputs.

GLOBAL AI IN MEDICAL IMAGING MARKET: KEY INSIGHTS

The report delves into the current state of global AI in medical imaging market and identifies potential growth opportunities within the industry. Some key findings from the report include:

• Presently, close to 200 AI in medical imaging-based solutions have been / are being developed by several companies in order to support radiologists in making accurate diagnosis decisions in a time efficient manner.
• Stakeholders have established strong positions in the industry by implementing unique strategies; companies with high valuation / net worth are likely to contribute the most to the overall revenue generation potential.

• Owing to features, such as better image visualization and detailed segmentation of anatomical regions, these solutions can significantly enhance a radiologists' efficiency, demonstrating the potential to save healthcare cost.
• Foreseeing a lucrative future, several public and private investors have made investments worth over USD 2 billion, across 150 instances.
• The growing interest in this field is reflected in the increasing number of partnership instances; majority of the deals inked were focused on the deployment of AI in medical imaging solutions at hospitals and clinics.
• Both established players and new entrants have forged several partnerships in the recent past; these deals have been inked with an aim to increase accessibility of the AI in medical imaging-based solutions.
• The partnership activity in this domain has increased at a lucrative CAGR. In fact, the maximum partnerships were reported in the last three years.
• Majority (72%) of the partnerships were inked for non-oncology segment; most of these deals were related to cerebrovascular disorders, cardiovascular disorders and lung infections.
• Over the years, significant number of patents related to AI in medical imaging and medical image processing have been filed / granted to various organizations.
• Multiple clinical studies were registered for the evaluation of AI in medical imaging-based solutions; companies have revealed positive results related to performance of algorithms in processing various types of medical images.

• The market growth is likely to be driven by the rising need for novel solutions to overcome the existing burden on radiologists; we expect the opportunity to be distributed across various application areas and types of images.

GLOBAL AI IN MEDICAL IMAGING MARKET: KEY SEGMENTS

Brain Injuries / Disorders Segment Occupy the Largest Share of the Global AI in Medical Imaging Market

Based on the application area, the market is segmented into lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and other application areas. At present, brain injuries / disorders segment hold the maximum share of the global AI in medical imaging market. This trend is likely to remain the same in the forthcoming years.

North America Accounts for the Largest Share of the Market

Based on key geographical regions, the market is segmented into North America, Europe, and Asia-Pacific and Rest of the World. The majority share is expected to be captured by players based in North America and Europe.

Example Players in the Global AI in Medical Imaging Market

• Artelus
• Arterys
• Butterfly Network
• ContextVision
• Enlitic
• Echonous
• GE Healthcare
• InferVision
• VUNO

PRIMARY RESEARCH OVERVIEW

The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders. The research report features detailed transcripts of interviews conducted with the following industry stakeholders:

• Chief Executive Officer, Company A
• Head of Strategy and Marketing, and Senior Director of Product, Company B
• Chief Technical Officer and Chief Operating Officer, Company C
• Founder and Chief Executive Officer, Company D
• Research Scientist, Company E

GLOBAL AI IN MEDICAL IMAGING MARKET: RESEARCH COVERAGE

• Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the global AI in medical imaging market, focusing on key market segments, including [A] application area, [B] type of image processed and [C] key geographical regions.
• Market Landscape: A comprehensive evaluation of AI based medical imaging solutions for medical image processing, considering various parameters, such as [A] status of development, [B] regulatory approvals, [C] type of offering, [D] type of image processed, [E] application area. Additionally, a comprehensive evaluation of companies developing such solutions, based on parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] focus area.
• Company Profiles: In-depth profiles of companies engaged in the development of AI in medical imaging-based solutions intended for processing of medical images, focusing on [A] company overviews, [B] solutions portfolio and [C] recent developments and an informed future outlook.
• Partnerships and Collaborations: An insightful analysis of the deals inked by stakeholders in the global AI in medical imaging market, based on several parameters, such as [A] year of partnership, [B] type of partnership, [C] type of partner, [D] therapeutic area, [E] most active players (in terms of the number of partnerships signed) and [F] geographical distribution of partnership activity.
• Funding and Investments: An in-depth analysis of the fundings received by players in AI in the medical imaging market, based on relevant parameters, such as [A] number of funding instances, [B] amount invested, [C] type of funding, [D] most active players, [E] most active investors and [F] geography.
• Company Valuation Analysis: A comprehensive valuation analysis of companies that are engaged in applying AI in medical imaging in solutions intended for processing of medical images.
• Clinical Trial Analysis: An insightful analysis of clinical trials related to AI in medical imaging, based on several parameters, such as [A] trial registration year, [B] trial recruitment status, [C] trial design, [D] target therapeutic area, [E] leading industry and non-industry players, and [F] geographical locations of trials.
• Patent Analysis: An in-depth analysis of patents filed / granted till date in the AI in medical imaging domain, based on various relevant parameters, such as [A] type of patent, [B] publication year, [C] application year, [D] regional applicability, [E] CPC symbols, [F] emerging focus areas, [G] leading patent assignees, and [H] patent benchmarking and valuation.
• Cost Saving Analysis: A comprehensive analysis of cost saving potential associated with the use of AI in medical imaging solutions intended for processing of medical images, examining factors, such as [A] total number of radiologists, [B] annual salary of radiologists, [C] number of scans performed, and [D] increase in efficiency by adoption of AI in medical imaging solutions.

KEY QUESTIONS ANSWERED IN THIS REPORT

• How many companies are currently engaged in this market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

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TABLE OF CONTENTS

1. PREFACE

• 1.1. Scope of the Report
• 1.2. Research Methodology
  • 1.2.1. Research Assumptions
  • 1.2.2. Project Methodology
  • 1.2.3. Forecast Methodology
  • 1.2.4. Robust Quality Control
  • 1.2.5. Key Considerations
    • 1.2.5.1. Demographics
    • 1.2.5.2. Economic Factors
    • 1.2.5.3. Government Regulations
    • 1.2.5.4. Supply Chain
    • 1.2.5.5. COVID Impact / Related Factors
    • 1.2.5.6. Market Access
    • 1.2.5.7. Healthcare Policies
    • 1.2.5.8. Industry Consolidation
• 1.3 Key Questions Answered
• 1.4. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

• 3.1. Humans, Machines and Intelligence
• 3.2. The Science of Learning
  • 3.2.1. Teaching Machines
    • 3.2.1.1. Machines for Computing
    • 3.2.1.2. Artificial Intelligence for Understanding the Human Brain
• 3.3. Artificial Intelligence
• 3.4. The Big Data Revolution
  • 3.4.1. Overview of Big Data
  • 3.4.2. Role of Internet of Things (IoT)
  • 3.4.3. Growing Adoption of Big Data
  • 3.4.4. Key Application Areas
    • 3.4.4.1. Big Data Analytics in Healthcare
    • 3.4.4.2. Machine Learning
    • 3.4.4.3. AI in medical imaging: The Amalgamation of Machine Learning and Big Data
• 3.5. Applications for AI in medical imaging in Healthcare
  • 3.5.1. Personalized Medicine
  • 3.5.2. Personal Fitness and Lifestyle Management
  • 3.5.3. Drug Discovery
  • 3.5.4. Clinical Trial Management
  • 3.5.5. Medical Image Processing

4. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND

• 4.1. Chapter Overview
• 4.2. International Business Machines (IBM)
  • 4.2.1. Company Overview
  • 4.2.2. Financial Information
  • 4.2.3. IBM Watson
• 4.3. Google
  • 4.3.1. Company Overview
  • 4.3.2. Financial Information
  • 4.3.3. Google DeepMind
• 4.4. IBM versus Google: Artificial Intelligence-related Acquisitions
• 4.5. IBM versus Google: Healthcare Focused Partnerships and Collaborations
• 4.6. IBM versus Google: Primary Concerns and Future Outlook

5. MARKET OVERVIEW

• 5.1. Chapter Overview
• 5.2. AI in medical imaging in Medical Image Processing: Overall Market Landscape
  • 5.2.1. Analysis by Status of Development
    • 5.2.1.1. Analysis by Regulatory Approvals Received
  • 5.2.2. Analysis by Type of Offering
  • 5.2.3. Analysis by Type of Image Processed
  • 5.2.4. Analysis by Anatomical Region
  • 5.2.5. Analysis by Application Area
  • 5.2.6. Grid Representation: Analysis by Type of Offering, Type of Image Processed and Application Area
• 5.3. AI in medical imaging in Medical Image Processing: Information on Key Characteristics
• 5.4. AI in medical imaging in Medical Image Processing: List of Companies
  • 5.4.1. Analysis by Year of Establishment
  • 5.4.2. Analysis by Company Size
  • 5.4.3. Analysis by Location of Headquarters
    • 5.4.3.1. World Map Representation: Regional Activity
  • 5.4.4. Analysis by Type of Deployment Model
  • 5.4.5. Leading Companies: Analysis by Number of Solutions

6. COMPANY PROFILES

• 6.1. Chapter Overview
• 6.2. Artelus
  • 6.2.1. Company Overview
  • 6.2.2. Product / Technology Portfolio
  • 6.2.3. Recent Developments and Future Outlook
• 6.3. Arterys
  • 6.3.1. Company Overview
  • 6.3.2. Product / Technology Portfolio
  • 6.3.3. Recent Developments and Future Outlook
• 6.4. Butterfly Network
  • 6.4.1. Company Overview
  • 6.4.2. Product / Technology Portfolio
  • 6.4.3. Recent Developments and Future Outlook
• 6.5. ContextVision
  • 6.5.1. Company Overview
  • 6.5.2. Product / Technology Portfolio
  • 6.5.3. Recent Developments and Future Outlook
• 6.6. Enlitic
  • 6.6.1. Company Overview
  • 6.6.2. Product / Technology Portfolio
  • 6.6.3. Recent Developments and Future Outlook
• 6.7. Echonous
  • 6.7.1. Company Overview
  • 6.7.2. Product / Technology Portfolio
  • 6.7.3. Recent Developments and Future Outlook
• 6.8. GE Healthcare
  • 6.8.1. Company Overview
  • 6.8.2. Product / Technology Portfolio
  • 6.8.3. Recent Developments and Future Outlook
• 6.9. InferVision
  • 6.9.1. Company Overview
  • 6.9.2. Product / Technology Portfolio
  • 6.9.3. Recent Developments and Future Outlook
• 6.10. VUNO
  • 6.10.1. Company Overview
  • 6.10.2. Product / Technology Portfolio
  • 6.10.3. Recent Developments and Future Outlook

7. PARTNERSHIPS AND COLLABORATIONS

• 7.1. Chapter Overview
• 7.2. Partnership Models
• 7.3. AI in medical imaging in Medical Image Processing: List of Partnerships and Collaborations
  • 7.3.1. Analysis by Year of Partnership
  • 7.3.2. Analysis by Type of Partnership
  • 7.3.3. Analysis by Year and Type of Partnership
  • 7.3.4. Analysis by Type of Partner
  • 7.3.5. Analysis by Therapeutic Area
  • 7.3.6. Most Active Players: Analysis by Number of Partnerships
  • 7.3.7. Regional Analysis
  • 7.3.8. Intercontinental and Intracontinental Agreements
• 7.4. Concluding Remarks

8. FUNDING AND INVESTMENT ANALYSIS

• 8.1. Chapter Overview
• 8.2. Types of Funding
• 8.3. AI in medical imaging in Medical Image Processing: Recent Funding Instances
  • 8.3.1. Analysis by Number of Funding Instances
  • 8.3.2. Analysis by Amount Invested
  • 8.3.3. Analysis by Type of Funding
  • 8.3.4. Most Active Players: Analysis by Number of Funding Instances and Amount Invested
  • 8.3.5. Most Active Investors: Analysis by Number of Funding Instances
  • 8.3.6. Geographical Analysis by Amount Invested

9. COMPANY VALUATION ANALYSIS

• 9.1. Chapter Overview
• 9.2. Methodology
• 9.3. Categorization by Parameters
  • 9.3.1. Twitter Followers Score
  • 9.3.2. Google Hits Score
  • 9.3.3. Partnerships Score
  • 9.3.4. Weighted Average Score
  • 9.3.5. Company Valuation: Roots Analysis Proprietary Scores

10. CASE STUDY: ANALYSIS OF AI IN MEDICAL IMAGING-BASED CLINICAL TRIALS REGISTERED IN THE US

• 10.1. Chapter Overview
• 10.2. Scope and Methodology
• 10.3. Clinical Trial Analysis
  • 10.3.1. Analysis by Trial Registration Year
  • 10.3.2. Analysis by Trial Registration Year and Recruitment Status
  • 10.3.3. Analysis by Trial Registration Year and Patient Enrollment
  • 10.3.4. Analysis by Trial Design
  • 10.3.5. Analysis by Patient Segment
  • 10.3.6. Analysis by Therapeutic Area
  • 10.3.7. Analysis by Trial Objective
  • 10.3.8. Analysis by Focus Areas
  • 10.3.9. Analysis by Type of Image Processed
  • 10.3.10. Most Active Players: Analysis by Number of Clinical Trials
  • 10.3.11. Analysis by Number of Clinical Trials and Geography
  • 10.3.12. Analysis by Enrolled Patient Population and Geography

11. PATENT ANALYSIS

• 11.1. Chapter Overview
• 11.2. Scope and Methodology
• 11.3. AI in medical imaging and Medical Image Processing: Patent Analysis
  • 11.3.1. Analysis by Application Year and Publication Year
  • 11.3.2. Analysis by Issuing Authority / Patent Offices Involved
  • 11.3.3. Analysis by IPCR Symbols
  • 11.3.4. Emerging Focus Areas
  • 11.3.5. Leading Assignees: Analysis by Number of Patents
  • 11.3.6. Patent Benchmarking Analysis
    • 11.3.6.1. Analysis by Patent Characteristics
• 11.4. Patent Valuation Analysis

12. COST SAVING ANALYSIS

• 12.1. Chapter Overview
• 12.2. Key Assumptions and Methodology
• 12.3. Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions, Till 2035
• 12.4. X-Ray Images
  • 12.4.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images): Analysis by Geography
    • 12.4.1.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in North America, Till 2035
    • 12.4.1.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Europe, Till 2035
    • 12.4.1.3. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Asia-Pacific and RoW, Till 2035
  • 12.4.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions: Analysis by Economic Strength
    • 12.4.2.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in High Income Countries, Till 2035
    • 12.4.2.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, Till 2035
• 12.5. MRI Images
  • 12.5.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images): Analysis by Geography
    • 12.5.1.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in North America, Till 2035
    • 12.5.1.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Europe, Till 2035
    • 12.5.1.3. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Asia-Pacific and RoW, Till 2035
  • 12.5.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images): Analysis by Economic Strength
    • 12.5.2.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in High Income Countries, Till 2035
    • 12.5.2.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Middle Income Countries, Till 2035
• 12.6. CT Images
  • 12.6.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images): Analysis by Geography
    • 12.6.1.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in North America, Till 2035
    • 12.6.1.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Europe, Till 2035
    • 12.6.1.3. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Asia-Pacific and RoW, Till 2035
  • 12.6.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images): Analysis by Economic Strength
    • 12.6.2.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in High Income Countries, Till 2035
    • 12.6.2.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Middle Income Countries, Till 2035
• 12.7. Ultrasound Images
  • 12.7.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images): Analysis by Geography
    • 12.7.1.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in North America, Till 2035
    • 12.7.1.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Europe, Till 2035
    • 12.7.1.3. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Asia-Pacific and RoW, Till 2035
  • 12.7.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images): Analysis by Economic Strength
    • 12.7.2.1. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, Till 2035
    • 12.7.2.2. Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, Till 2035
• 12.8. Concluding Remarks: Cost Saving Scenarios

13. MARKET FORECAST

• 13.1. Chapter Overview
• 13.2. Forecast Methodology and Key Assumptions
• 13.3. Overall AI in medical imaging in Medical Image Processing Market
  • 13.3.1. AI in medical imaging in Medical Image Processing Market: Distribution by Application Area
    • 13.3.1.1. AI in medical imaging in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders
    • 13.3.1.2. AI in medical imaging in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders
    • 13.3.1.3. AI in medical imaging in Medical Image Processing Market for Breast Cancer
    • 13.3.1.4. AI in medical imaging in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders
    • 13.3.1.5. AI in medical imaging in Medical Image Processing Market for Lung Infections / Lung Disorders
    • 13.3.1.6. AI in medical imaging in Medical Image Processing Market for Other Disorders
• 13.4 AI in medical imaging in Medical Image Processing Market: Distribution by Type of Image Processed
  • 13.4.1. AI in medical imaging in Medical Image Processing Market for X-Rays
  • 13.4.2. AI in medical imaging in Medical Image Processing Market for MRI
  • 13.4.3. AI in medical imaging in Medical Image Processing Market for CT
  • 13.4.4. AI in medical imaging in Medical Image Processing Market for Ultrasound
• 13.5 AI in medical imaging in Medical Image Processing Market: Distribution by Key Geographical Regions
  • 13.5.1. AI in medical imaging in Medical Image Processing Market in North America
  • 13.5.2. AI in medical imaging in Medical Image Processing Market in Europe
  • 13.5.3. AI in medical imaging in Medical Image Processing Market in Asia Pacific / RoW
• 13.6 Concluding Remarks

14. AI IN MEDICAL IMAGING IN HEALTHCARE: EXPERT INSIGHTS

• 14.1. Chapter Overview
• 14.2. Industry Experts
  • 14.2.1. Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (Company A)
  • 14.2.2. Vice President of Radiology Informatics (Philips) and Jonathan Laserson, Lead AI Strategist (Company B)
  • 14.2.3. Chief Executive Officer (Company C)
  • 14.2.4. Chief Scientist and Senior Vice President (Company D)
  • 14.2.5. Founder and Researcher (Company E)
  • 14.2.6. Serial Entrepreneur and Data Engineer (Company F)
• 14.3. University and Hospital Experts
  • 14.3.1. Chairman of Medical Oncology (University A)
  • 14.3.2. Professor (University B) and Senior AI Fellowship (University C)
  • 14.3.3. Yoshua Bengio, Professor (University D) and Scientific Director (University E)
• 14.4. Other Expert Opinions

15. INTERVIEW TRANSCRIPTS

• 15.1. Chapter Overview
• 15.2. Company A
  • 15.2.1. Company Snapshot
  • 15.2.2. Interview Transcript: CEO
• 15.3. Company B
  • 15.3.1. Company Snapshot
  • 15.3.2. Interview Transcript: Head of Strategy and Marketing
  • 15.3.3. Interview Transcript: Senior Director of Product
• 15.4. Company C
  • 15.4.1. Company Snapshot
  • 15.4.2. Interview Transcript: CTO and COO
• 15.5. Company D
  • 15.5.1. Company Snapshot
  • 15.5.2. Interview Transcript: Founder / CEO
• 15.6. Company E
  • 15.6.1. Company Snapshot
  • 15.6.2. Interview Transcript: Research Scientist

16. IMPACT OF COVID-19 OUTBREAK ON AI IN MEDICAL IMAGING MARKET DYNAMICS

• 16.1. Chapter Overview
• 16.2. Evaluation of Impact of COVID-19 Pandemic
  • 16.2.1. Current Initiatives and Recuperative Strategies of Key Players
  • 16.2.2. Impact on Opportunity for AI in medical imaging in Medical Image Processing Market
• 16.3. Response Strategies: A Roots Analysis Perspective
  • 16.3.1. Propositions for Immediate Implementation
  • 16.3.2. Propositions for Short / Long Term Implementation

17. CONCLUSION

18. APPENDIX 1: TABULATED DATA

19. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

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