医療画像処理におけるAI市場：業界動向と世界の予測 - 応用分野別、画像処理タイプ別、主要地域別

医療用画像処理におけるAI市場：概要

世界の医療用画像処理におけるAIの市場規模は、今年17億5,000万米ドルとなりました。同市場は、予測期間中に30％の有利なCAGRで成長する見込みです。

市場サイジングと機会分析は、以下のパラメータにわたってセグメント化されています：

応用分野

• 肺感染症/呼吸器疾患
• 脳損傷/障害
• 肺がん
• 心臓疾患/心血管疾患
• 骨変形/整形外科疾患
• 乳がん
• その他

画像処理タイプ

• X線
• MRI
• CT
• 超音波

主な地域

• 北米
• 欧州
• アジア太平洋およびその他の地域

世界の医療画像処理におけるAI市場：成長と動向

近年、大量の医療画像により、汎用性の高い自己改善アルゴリズムと高度な並列化技術が必要とされています。医用画像技術におけるAIは、多層構造で相互接続されたノード/ニューロンのニューラルネットワークを使用する複雑な機械学習アルゴリズムであり、それによって大量の非構造化データを解釈して価値ある洞察を生み出すことを可能にします。その結果、この技術は、画像診断やデータ処理など、ヘルスケア分野のさまざまな用途に徐々に取り入れられるようになっています。さらに専門家は、医療画像ベースの画像処理ソリューションにAIの優れたパターン認識能力を活用することで、放射線科医はより多くの情報に基づいた意思決定を行うことができると考えています。

時間をかけて、いくつかの業界利害関係者は、医療画像解析に特化した独自の医療画像アルゴリズムAIを開発してきました。現在、多くのイノベーターが、その技術によりコンピュータを訓練して医用画像を解釈・分析し、時間的・空間的パターンを特定できると主張しています。さらに、人工知能主導型診断業界の多くの専門家が、医療画像におけるAIは放射線データの処理と解釈を大幅に加速できると考えていることは注目に値します。実際、診断速度が約20％向上し、その結果出力される偽陽性率が約10％減少したと報告している研究もあります。

世界の医療画像診断におけるAI市場：主要インサイト

当レポートでは、世界の医療画像診断におけるAI市場の現状を掘り下げ、業界内の潜在的な成長機会を特定しています。当レポートの主な調査結果は以下の通りです：

• 現在、放射線科医が時間効率よく正確な診断判断を下すのをサポートするため、医療画像ベースのAIソリューションが200近く、複数の企業別開発されている/開発中です。
• 利害関係者は、独自の戦略を実施することにより、この業界で強力な地位を確立しています。高い評価/純資産を持つ企業は、全体的な収益生成の可能性に最も貢献する可能性が高いです。

• より良い画像の可視化や解剖学的領域の詳細なセグメンテーションなどの機能により、これらのソリューションは放射線科医の効率を大幅に向上させ、ヘルスケアコストを節約する可能性を示しています。
• 有利な将来を予見して、複数の公的・民間投資家が150の事例で20億米ドル以上の投資を行っています。
• この分野への関心の高まりは、提携事例の増加にも反映されており、締結された取引の大半は、病院や診療所における医療画像ソリューションへのAIの導入に焦点を当てたものでした。
• 既存企業も新規参入企業も、ここ最近でいくつかのパートナーシップを結んでいます。これらの取引は、医療画像ベースのAIソリューションのアクセシビリティを高める目的で結ばれています。
• この分野でのパートナーシップ活動は、有利なCAGRで増加しています。実際、過去3年間で最大のパートナーシップが報告されています。
• パートナーシップの大部分（72％）は、非腫瘍学セグメントで締結されました。これらの取引のほとんどは、脳血管障害、心血管障害、肺感染症に関連していました。
• 医療用画像処理および医療用画像処理におけるAIに関連する特許は、ここ数年で相当数が様々な組織に出願/付与されています。
• 医療画像ベースのソリューションにおけるAIの評価のために複数の臨床研究が登録され、各社は様々な種類の医療画像の処理におけるアルゴリズムの性能に関する良好な結果を明らかにしています。

• 市場の成長機会は、放射線科医にかかる既存の負担を克服するための斬新なソリューションに対するニーズの高まりにより牽引される可能性が高いです。この機会は、さまざまな応用分野や画像の種類に分散されると予想されます。

医療画像におけるAI世界市場：主要セグメント

応用分野別では、市場は肺感染症/呼吸器疾患、脳損傷/疾患、肺がん、心疾患/心血管疾患、骨変形/整形外科疾患、乳がん、その他に区分されます。現在、世界の医用画像AI市場では、脳損傷/障害分野が最大シェアを占めています。この動向は今後数年間も変わらないと思われます。

主要地域別に見ると、市場は北米、欧州、アジア太平洋、その他の地域に区分されます。北米と欧州を拠点とする参入企業がシェアの大半を占めると予想されます。

世界の医療用画像処理におけるAI市場の参入企業例

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

当レポートでは、世界の医療画像処理におけるAI市場について調査し、市場の概要とともに、応用分野別、画像処理タイプ別動向、地域別の動向、および市場に参入する企業のプロファイルなどを提供しています。

目次

第1章 序文

第2章 エグゼクティブサマリー

第3章 イントロダクション

• 人間、機械、そして知性
• 学習の科学
• 人工知能
• ビッグデータ革命
• ヘルスケアにおける医用画像におけるAIの応用

第4章 ケーススタディ：IBM WatsonとGoogle Deepmind

• 章の概要
• International Business Machines（IBM）
• Google
• IBM対Google：人工知能関連の買収
• IBM対Google：ヘルスケアに特化したパートナーシップとコラボレーション
• IBM対Google：主な懸念事項と将来の見通し

第5章 市場概要

• 章の概要
• 医療画像処理におけるAI：市場情勢
• 医療画像処理におけるAI：主な特徴に関する情報
• 医療画像処理におけるAI：企業一覧

第6章 企業プロファイル

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

第7章 パートナーシップとコラボレーション

• 章の概要
• パートナーシップモデル
• 医療画像処理におけるAI：パートナーシップとコラボレーションのリスト
• 結論

第8章 資金調達と投資分析

• 章の概要
• 資金調達の種類
• 医療画像処理におけるAI：最近の資金調達事例

第9章 企業評価分析

• 章の概要
• 調査手法
• パラメータ別分類

第10章 ケーススタディ：米国で登録された医療画像ベースの臨床試験におけるAIの分析

• 章の概要
• 範囲と調査手法
• 臨床試験分析

第11章 特許分析

• 章の概要
• 範囲と調査手法
• 医用画像と医療画像処理におけるAI：特許分析
• 特許評価分析

第12章 コスト削減分析

• 章の概要
• 主要な前提と調査手法
• 2035年までの医療画像処理ソリューションにおけるAIの総合的なコスト削減可能性
• X線画像
• MRI画像
• CT画像
• 超音波画像
• 結論：コスト削減シナリオ

第13章 市場予測

• 章の概要
• 予測調査手法と主要な前提条件
• 医療画像処理市場におけるAI：応用分野別
• 医療画像処理市場におけるAI：画像処理タイプ別
• 医療画像処理市場におけるAI：主要地域別
• 結論

第14章 ヘルスケアにおける医用画像診断におけるAI：専門家の洞察

• 章の概要
• 業界の専門家
• 大学と病院の専門家
• その他の専門家の意見

第15章 インタビュー記録

第16章 COVID-19の流行が医療画像市場力学におけるAIに与える影響

• 章の概要
• COVID-19パンデミックの影響評価
• 対応戦略：ルーツ分析の観点

第17章 結論

第18章 付録1：表形式データ

第19章 付録2：企業・団体一覧

List of Tables

• Table 3.1 Machine Learning: A Brief History
• Table 4.1 IBM: Artificial Intelligence Focused Acquisitions
• Table 4.2 Google: Artificial Intelligence Focused Acquisitions
• Table 4.3 IBM Watson: Partnerships and Collaborations in Healthcare
• Table 4.4 Google DeepMind: Partnerships and Collaborations in Healthcare
• Table 5.1 AI in medical imaging in Medical Image Processing Solutions: Information on Status of Development and Regulatory Approvals
• Table 5.2 AI in medical imaging in Medical Image Processing Solutions: Information on Type of Offering and Type of Image Processed
• Table 5.3 AI in medical imaging in Medical Image Processing Solutions: Information on Anatomical Region and Application Area
• Table 5.4 AI in medical imaging in Medical Image Processing Solutions: Information on Key Characteristics of Solutions and Affiliated Technologies
• Table 5.5 AI in medical imaging in Medical Image Processing (List of Companies): Information on Year of Establishment, Company Size, Location of Headquarters, Type of Deployment Model, Number of Solutions
• Table 6.1 List of Companies Profiled
• Table 6.2 Artelus: Company Overview
• Table 6.3 Artelus: Information on Medical Image Processing Solutions
• Table 6.4 Artelus: Recent Developments and Future Outlook
• Table 6.5 Arterys: Company Overview
• Table 6.6 Arterys: Information on Medical Image Processing Solutions
• Table 6.7 Arterys: Recent Developments and Future Outlook
• Table 6.8 Butterfly Network: Company Overview
• Table 6.9 Butterfly Network: Information on Medical Image Processing Solutions
• Table 6.10 Butterfly Network: Recent Developments and Future Outlook
• Table 6.11 ContextVision: Company Overview
• Table 6.12 ContextVision: Information on Medical Image Processing Solutions
• Table 6.13 ContextVision: Recent Developments and Future Outlook
• Table 6.14 Enlitic: Company Overview
• Table 6.15 Enlitic: Information on Medical Image Processing Solutions
• Table 6.16 Enlitic: Recent Developments and Future Outlook
• Table 6.17 Echonous: Company Overview
• Table 6.18 Echonous: Information on Medical Image Processing Solutions
• Table 6.19 Echonous: Recent Developments and Future Outlook
• Table 6.20 GE Healthcare: Company Overview
• Table 6.21 GE Healthcare: Information on Medical Image Processing Solutions
• Table 6.22 GE Healthcare: Recent Developments and Future Outlook
• Table 6.23 InferVision: Company Overview
• Table 6.24 InferVision: Information on Medical Image Processing Solutions
• Table 6.25 InferVision: Recent Developments and Future Outlook
• Table 6.26 VUNO: Company Overview
• Table 6.27 VUNO: Information on Medical Image Processing Solutions
• Table 6.28 VUNO: Recent Developments and Future Outlook
• Table 7.1 AI in medical imaging in Medical Image Processing: List of Partnerships and Collaborations
• Table 8.1 AI in medical imaging in Medical Image Processing: List of Funding and Investments
• Table 9.1 Company Valuation Analysis: Sample Dataset
• Table 9.2 Company Valuation Analysis: Weighted Average Valuation
• Table 9.3 Company Valuation Analysis: Estimated Valuation
• Table 9.4 Company Valuation Analysis: Distribution by Specialization
• Table 11.1 AI in medical imaging in Medical Image Processing, Patent Portfolio: IPCR Classification Symbol Definitions
• Table 11.2 AI in medical imaging in Medical Image Processing, Patent Portfolio: Most Popular IPCR Classification Symbols
• Table 11.3 AI in medical imaging in Medical Image Processing, Patent Portfolio: List of Top IPCR Classification Symbols
• Table 11.4 AI in medical imaging in Medical Image Processing, Patent Portfolio: Summary of Benchmarking Analysis
• Table 11.5 AI in medical imaging in Medical Image Processing, Patent Portfolio: Categorizations based on Weighted Valuation Scores
• Table 11.6 AI in medical imaging in Medical Image Processing, Patent Portfolio: List of Leading Patents (by Highest Relative Valuation)
• Table 12.1 Cost Saving Analysis: Information on Number of Radiologists in Various Countries
• Table 12.2 Cost Saving Analysis: Information on Yearly Count of X-Ray Scans across Different Geographical Regions (Million Scans)
• Table 12.3 Cost Saving Analysis: Information on Yearly Count of Ultrasound Scans across Different Geographical Regions (Million Scans)
• Table 12.4 Cost Saving Analysis: Information on Yearly Count of MRI Scans across Different Geographical Regions (Million Scans)
• Table 12.5 Cost Saving Analysis: Information on Yearly Count of CT scans across Different Geographical Regions (Million Scans)
• Table 13.1 AI in medical imaging in Medical Image Processing Solutions: Information on Adoption by Radiologists Across Different Geographical Regions
• Table 15.1 Advenio Technosys: Company Snapshot
• Table 15.2 Arterys: Company Snapshot
• Table 15.3 Arya.ai: Company Snapshot
• Table 15.4 AlgoSurg: Company Snapshot
• Table 15.5 Context Vision: Company Snapshot
• Table 18.1 IBM: Annual Revenues (USD Billion)
• Table 18.2 Alphabet: Annual Revenues (USD Billion)
• Table 18.3 AI in medical imaging in Medical Image Processing: Distribution by Status of Development
• Table 18.4 AI in medical imaging in Medical Image Processing: Distribution by Regulatory Approvals Received
• Table 18.5 AI in medical imaging in Medical Image Processing: Distribution by Type of Offering
• Table 18.6 AI in medical imaging in Medical Image Processing: Distribution by Type of Image Processed
• Table 18.7 AI in medical imaging in Medical Image Processing: Distribution by Anatomical Region
• Table 18.8 AI in medical imaging in Medical Image Processing: Distribution by Application Area
• Table 18.9 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Year of Establishment
• Table 18.10 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Company Size
• Table 18.11 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Location of Headquarters
• Table 18.12 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Type of Deployment Model
• Table 18.13 Leading AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Number of Solutions
• Table 18.14 Partnerships and Collaborations: Distribution by Year of Partnership
• Table 18.15 Partnerships and Collaborations: Distribution by Type of Partnership
• Table 18.16 Partnerships and Collaborations: Distribution by Year and Type of Partnership
• Table 18.17 Partnerships and Collaborations: Distribution by Type of Partner
• Table 18.18 Partnerships and Collaborations: Distribution by Therapeutic Area
• Table 18.19 Most Active Players: Distribution by Number of Partnerships
• Table 18.20 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
• Table 18.21 Funding and Investments: Distribution of Instances by Year of Establishment of Companies and Type of Funding
• Table 18.22 Funding and Investments: Cumulative Year-wise Trend
• Table 18.23 Funding and Investments: Distribution by Number of Funding Instances and Amount Invested
• Table 18.24 Funding and Investments: Distribution by Type of Funding
• Table 18.25 Funding and Investments: Distribution by Type of Funding and Total Amount Invested (USD Million)
• Table 18.26 Most Active Players: Distribution by Number of Funding Instances and Amount of Funding (USD Million)
• Table 18.27 Most Active Companies: Summary of Funding Raised by Type of Funding and Amount of Funding (USD Million)
• Table 18.28 Most Active Investors: Distribution by Number of Funding Instances
• Table 18.29 Clinical Trial Analysis: Distribution by Trial Recruitment Status
• Table 18.30 Clinical Trial Analysis: Cumulative Distribution by Trial Registration Year
• Table 18.31 Clinical Trial Analysis: Distribution by Trial Recruitment Status and Trial Registration Year
• Table 18.32 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment
• Table 18.33 Clinical Trial Analysis: Distribution by Study Design
• Table 18.34 Clinical Trial Analysis: Distribution by Patient Segment
• Table 18.35 Clinical Trial Analysis: Distribution by Therapeutic Area
• Table 18.36 Clinical Trial Analysis: Distribution by Trial Objective
• Table 18.37 Clinical Trial Analysis: Distribution by Type of Image Processed
• Table 18.39 Clinical Trial Analysis: Distribution by Type of Sponsors / Collaborators
• Table 18.40 Leading Sponsors / Collaborators: Analysis by Number of Trials
• Table 18.41 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Type of Patent
• Table 18.42 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Application Year and Publication Year
• Table 18.43 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Issuing Authority / Patent Offices Involved
• Table 18.44 AI in medical imaging in Medical Image Processing, Patent Portfolio: North America
• Table 18.45 AI in medical imaging in Medical Image Processing, Patent Portfolio: Europe
• Table 18.46 AI in medical imaging in Medical Image Processing, Patent Portfolio: Asia Pacific and RoW
• Table 18.47 AI in medical imaging in Medical Image Processing, Patent Portfolio: Leading Assignees (Industry Players)
• Table 18.48 AI in medical imaging in Medical Image Processing, Patent Portfolio: Leading Assignees (Non-Industry Players)
• Table 18.49 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Patent Age
• Table 18.50 AI in medical imaging in Medical Image Processing, Patent Portfolio: Valuation Analysis
• Table 18.51 AI in medical imaging in Medical Image Processing: Efficiency Profile of Radiologists
• Table 18.52 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions: Growth Scenarios
• Table 18.53 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images), Till 2035 (USD Billion)
• Table 18.54 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in North America, Till 2035 (USD Billion)
• Table 18.55 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Europe, Till 2035 (USD Billion)
• Table 18.56 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Table 18.57 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (X-Ray Images) in High Income Countries, Till 2035 (USD Billion)
• Table 18.58 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, Till 2035 (USD Billion)
• Table 18.59 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images), Till 2035 (USD Billion)
• Table 18.60 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in North America, Till 2035 (USD Billion)
• Table 18.61 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Europe, Till 2035 (USD Billion)
• Table 18.62 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Table 18.63 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (MRI Images) in High Income Countries, Till 2035 (USD Billion)
• Table 18.64 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (MRI Images) in Middle Income Countries, Till 2035 (USD Billion)
• Table 18.65 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images), Till 2035 (USD Billion)
• Table 18.66 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in North America, Till 2035 (USD Billion)
• Table 18.67 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Europe, Till 2035 (USD Billion)
• Table 18.68 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Table 18.69 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (CT Images) in High Income Countries, Till 2035 (USD Billion)
• Table 18.70 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (CT Images) in Middle Income Countries, Till 2035 (USD Billion)
• Table 18.71 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images), Till 2035 (USD Billion)
• Table 18.72 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in North America, Till 2035 (USD Billion)
• Table 18.73 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Europe, Till 2035 (USD Billion)
• Table 18.74 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Table 18.75 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, Till 2035 (USD Billion)
• Table 18.76 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, Till 2035 (USD Billion)
• Table 18.77 Overall AI in medical imaging in Medical Image Processing Market, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.78 AI in medical imaging in Medical Image Processing Market: Distribution by Application Area, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.79 AI in medical imaging in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.80 AI in medical imaging in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.81 AI in medical imaging in Medical Image Processing Market for Breast Cancer, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.82 AI in medical imaging in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.83 AI in medical imaging in Medical Image Processing Market for Lung Infections / Lung Disorders, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.84 AI in medical imaging in Medical Image Processing Market for Other Disorders, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.85 AI in medical imaging in Medical Image Processing Market: Distribution by Type of Image Processed, Conservative, Base and Optimistic Scenarios, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.86 AI in medical imaging in Medical Image Processing Market for X-Rays, Conservative, Base and Optimistic Scenarios, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.87 AI in medical imaging in Medical Image Processing Market for MRI, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.88 AI in medical imaging in Medical Image Processing Market for CT, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.89 AI in medical imaging in Medical Image Processing Market for Ultrasound, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.90 AI in medical imaging in Medical Image Processing Market: Distribution by Key Geographical Regions, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.91 AI in medical imaging in Medical Image Processing Market in North America, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.92 AI in medical imaging in Medical Image Processing Market in Europe, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.93 AI in medical imaging in Medical Image Processing Market in Asia Pacific / RoW, Conservative, Base and Optimistic Scenarios, Till 2035 (USD Million)
• Table 18.94 Opportunity for AI in medical imaging in Medical Image Processing Market, Till 2035 (COVID Impact Scenario)

List of Figures

• Figure 3.1 Key Stages of Observational Learning
• Figure 3.2 Understanding Neurons and the Human Brain: Key Scientific Contributions
• Figure 3.3 Big Data: The Three V's
• Figure 3.4 Internet of Things: Framework
• Figure 3.5 Internet of Things: Applications in Healthcare
• Figure 3.6 Big Data: Google Trends
• Figure 3.7 Big Data: Application Areas
• Figure 3.8 Big Data: Opportunities in Healthcare
• Figure 3.9 Machine Learning Algorithm: Workflow
• Figure 3.10 Machine Learning Algorithms: Timeline
• Figure 3.11 Neural Networks: Architecture
• Figure 3.12 AI in medical imaging: Image Recognition
• Figure 3.13 Google Trends: Artificial Intelligence versus Machine Learning versus AI in medical imaging versus Cognitive Computing
• Figure 3.14 Google Trends: Popular Keywords (AI in medical imaging)
• Figure 3.15 AI in medical imaging Frameworks: Relative Performance
• Figure 3.16 Personalized Medicine: Applications in Healthcare
• Figure 4.17 IBM: Annual Revenues (USD Billion)
• Figure 4.18 Alphabet: Annual Revenues (USD Billion)
• Figure 5.1 AI in medical imaging in Medical Image Processing: Distribution by Status of Development
• Figure 5.2 AI in medical imaging in Medical Image Processing: Distribution by Regulatory Approvals Received
• Figure 5.3 AI in medical imaging in Medical Image Processing: Distribution by Type of Offering
• Figure 5.4 AI in medical imaging in Medical Image Processing: Distribution by Type of Image Processed
• Figure 5.5 AI in medical imaging in Medical Image Processing: Distribution by Anatomical Region
• Figure 5.6 AI in medical imaging in Medical Image Processing: Distribution by Application Area
• Figure 5.6 Grid Representation: Distribution by Type of Offering, Type of Image Processed and Application Area
• Figure 5.7 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Year of Establishment
• Figure 5.8 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Company Size
• Figure 5.9 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Location of Headquarters
• Figure 5.10 World Map Representation: Regional Activity of AI in medical imaging in Medical Image Processing Solution Developers
• Figure 5.11 AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Type of Deployment Model
• Figure 5.12 Leading AI in medical imaging in Medical Image Processing Solution Developers: Distribution by Number of Solutions
• Figure 7.1 Partnerships and Collaborations: Distribution by Year of Partnership
• Figure 7.2 Partnerships and Collaborations: Distribution by Type of Partnership
• Figure 7.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
• Figure 7.4 Partnerships and Collaborations: Distribution by Type of Partner
• Figure 7.5 Partnerships and Collaborations: Distribution by Therapeutic Area
• Figure 7.6 Most Active Players: Distribution by Number of Partnerships
• Figure 7.7 Partnerships and Collaborations: Regional Distribution
• Figure 7.8 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
• Figure 7.9 Partnerships and Collaborations: Summary of Partnership Activity
• Figure 8.1 Funding and Investments: Distribution of Instances by Year of Establishment of Companies and Type of Funding
• Figure 8.2 Funding and Investments: Cumulative Year-wise Trend
• Figure 8.3 Funding and Investments: Distribution by Number of Funding Instances and Amount Invested
• Figure 8.4 Funding and Investments: Distribution by Type of Funding
• Figure 8.5 Funding and Investments: Distribution by Type of Funding and Total Amount Invested (USD Million)
• Figure 8.6 Most Active Players: Distribution by Number of Funding Instances and Amount of Funding (USD Million)
• Figure 8.7 Most Active Companies: Summary of Funding Raised by Type of Funding and Amount of Funding (USD Million)
• Figure 8.8 Most Active Investors: Distribution by Number of Funding Instances
• Figure 8.9 Funding and Investments: Geographical Distribution by Amount Invested (USD Million)
• Figure 9.1 Company Valuation Analysis: A/F Ratio, Input Dataset
• Figure 9.2 Company Valuation Analysis: A/Y Ratio, Input Dataset
• Figure 9.3 Company Valuation Analysis: A/E Ratio, Input Dataset
• Figure 9.4 Company Valuation Analysis: Categorization by Twitter Followers Score
• Figure 9.5 Company Valuation Analysis: Categorization by Google Hits Score
• Figure 9.6 Company Valuation Analysis: Categorization by Partnerships Score
• Figure 9.7 Company Valuation Analysis: Categorization by Weighted Average Score
• Figure 9.8 Company Valuation Analysis: Unicorns in AI in medical imaging in Medical Image Processing Sector
• Figure 10.1 Clinical Trial Analysis: Distribution by Trial Recruitment Status
• Figure 10.2 Clinical Trial Analysis: Cumulative Distribution by Trial Registration Year
• Figure 10.3 Clinical Trial Analysis: Distribution by Trial Recruitment Status and Trial Registration Year
• Figure 10.4 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment
• Figure 10.5 Clinical Trial Analysis: Distribution by Study Design
• Figure 10.6 Clinical Trial Analysis: Distribution by Patient Segment
• Figure 10.7 Clinical Trial Analysis: Distribution by Therapeutic Area
• Figure 10.8 Clinical Trial Analysis: Distribution by Trial Objective
• Figure 10.9 Clinical Trial Analysis: Focus Areas
• Figure 10.10 Clinical Trial Analysis: Distribution by Type of Image Processed
• Figure 10.11 Clinical Trial Analysis: Distribution by Type of Sponsors / Collaborators
• Figure 10.12 Leading Sponsors / Collaborators: Analysis by Number of Trials
• Figure 10.13 Clinical Trial Analysis: Geographical Distribution of Trials
• Figure 10.14 Clinical Trial Analysis: Geographical Distribution of Trials and Patient Population
• Figure 11.1 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Type of Patent
• Figure 11.2 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Application Year and Publication Year
• Figure 11.3 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Issuing Authority / Patent Offices Involved
• Figure 11.4 AI in medical imaging in Medical Image Processing, Patent Portfolio: North America
• Figure 11.5 AI in medical imaging in Medical Image Processing, Patent Portfolio: Europe
• Figure 11.6 AI in medical imaging in Medical Image Processing, Patent Portfolio: Asia Pacific and RoW
• Figure 11.7 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by IPCR Symbols
• Figure 11.8 AI in medical imaging in Medical Image Processing, Patent Portfolio: Focus Areas
• Figure 11.9 AI in medical imaging in Medical Image Processing, Patent Portfolio: Leading Assignees (Industry Players)
• Figure 11.10 AI in medical imaging in Medical Image Processing, Patent Portfolio: Leading Assignees (Non-Industry Players)
• Figure 11.11 AI in medical imaging in Medical Image Processing, Patent Portfolio: Leading Industry Players (Benchmarking by Patent Characteristics)
• Figure 11.12 AI in medical imaging in Medical Image Processing, Patent Portfolio: Distribution by Patent Age
• Figure 11.13 AI in medical imaging in Medical Image Processing, Patent Portfolio: Valuation Analysis
• Figure 12.1 AI in medical imaging in Medical Image Processing: Efficiency Profile of Radiologists
• Figure 12.2 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions: Growth Scenarios
• Figure 12.3 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images), Till 2035 (USD Billion)
• Figure 12.4 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in North America, Till 2035 (USD Billion)
• Figure 12.5 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Europe, Till 2035 (USD Billion)
• Figure 12.6 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (X-Ray Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Figure 12.7 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (X-Ray Images) in High Income Countries, Till 2035 (USD Billion)
• Figure 12.8 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, Till 2035 (USD Billion)
• Figure 12.9 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images), Till 2035 (USD Billion)
• Figure 12.10 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in North America, Till 2035 (USD Billion)
• Figure 12.11 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Europe, Till 2035 (USD Billion)
• Figure 12.12 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (MRI Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Figure 12.13 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (MRI Images) in High Income Countries, Till 2035 (USD Billion)
• Figure 12.14 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (MRI Images) in Middle Income Countries, Till 2035 (USD Billion)
• Figure 12.15 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images), Till 2035 (USD Billion)
• Figure 12.16 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in North America, Till 2035 (USD Billion)
• Figure 12.17 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Europe, Till 2035 (USD Billion)
• Figure 12.18 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (CT Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Figure 12.19 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (CT Images) in High Income Countries, Till 2035 (USD Billion)
• Figure 12.20 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (CT Images) in Middle Income Countries, Till 2035 (USD Billion)
• Figure 12.21 Overall Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images), Till 2035 (USD Billion)
• Figure 12.22 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in North America, Till 2035 (USD Billion)
• Figure 12.23 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Europe, Till 2035 (USD Billion)
• Figure 12.24 Cost Saving Potential of AI in medical imaging in Medical Image Processing Solutions (Ultrasound Images) in Asia Pacific and RoW, Till 2035 (USD Billion)
• Figure 12.25 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, Till 2035 (USD Billion)
• Figure 12.26 Cost Saving Potential of AI in medical imaging-based Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, Till 2035 (USD Billion)
• Figure 13.1 Overall AI in medical imaging in Medical Image Processing Market, Till 2035 (USD Million)
• Figure 13.2 AI in medical imaging in Medical Image Processing Market: Distribution by Application Area, Till 2035 (USD Million)
• Figure 13.3 AI in medical imaging in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders, Till 2035 (USD Million)
• Figure 13.4 AI in medical imaging in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders, Till 2035 (USD Million)
• Figure 13.5 AI in medical imaging in Medical Image Processing Market for Breast Cancer, Till 2035 (USD Million)
• Figure 13.6 AI in medical imaging in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders, Till 2035 (USD Million)
• Figure 13.7 AI in medical imaging in Medical Image Processing Market for Lung Infections / Lung Disorders, Till 2035 (USD Million)
• Figure 13.8 AI in medical imaging in Medical Image Processing Market for Other Disorders, Till 2035 (USD Million)
• Figure 13.9 AI in medical imaging in Medical Image Processing Market: Distribution by Type of Image Processed, Till 2035 (USD Million)
• Figure 13.10 AI in medical imaging in Medical Image Processing Market for X-Rays, Till 2035 (USD Million)
• Figure 13.11 AI in medical imaging in Medical Image Processing Market for MRI, Till 2035 (USD Million)
• Figure 13.12 AI in medical imaging in Medical Image Processing Market for CT, Till 2035 (USD Million)
• Figure 13.13 AI in medical imaging in Medical Image Processing Market for Ultrasound, Till 2035 (USD Million)
• Figure 13.14 AI in medical imaging in Medical Image Processing Market: Distribution by Key Geographical Regions, Till 2035 (USD Million)
• Figure 13.15 AI in medical imaging in Medical Image Processing Market in North America, Till 2035 (USD Million)
• Figure 13.16 AI in medical imaging in Medical Image Processing Market in Europe, Till 2035 (USD Million)
• Figure 13.17 AI in medical imaging in Medical Image Processing Market in Asia Pacific / RoW, Till 2035 (USD Million)
• Figure 13.18 Concluding Remarks
• Figure 14.1 AI in medical imaging in Healthcare: Other Expert Insights
• Figure 16.1 Opportunity for AI in medical imaging in Medical Image Processing Market, Till 2035 (COVID Impact Scenario)
• Figure 17.1 Concluding Remarks: Current Market Landscape
• Figure 17.2 Concluding Remarks: Partnerships and Collaborations
• Figure 17.3 Concluding Remarks: Funding and Investments
• Figure 17.4 Concluding Remarks: Company Valuation
• Figure 17.5 Concluding Remarks: Clinical Trials
• Figure 17.6 Concluding Remarks: Patents
• Figure 17.7 Concluding Remarks: Cost Saving Potential
• Figure 17.8 Concluding Remarks: Market Forecast and Opportunity

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?

REASONS TO BUY THIS REPORT

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

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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