表紙：臨床試験におけるAI市場（第2版）：AIソフトウェアとサービスプロバイダー- 治験フェーズ別、対象治療領域別、エンドユーザー別、主要地域別：業界動向と世界の予測、2023年～2035年

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臨床試験におけるAI市場（第2版）：AIソフトウェアとサービスプロバイダー- 治験フェーズ別、対象治療領域別、エンドユーザー別、主要地域別：業界動向と世界の予測、2023年～2035年

AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers - Distribution by Trial Phase, Target Therapeutic Area, End-user and Key Geographical Regions: Industry Trends and Global Forecasts, 2023-2035

出版日: 2023年07月18日 | 発行: Roots Analysis

| ページ情報: 英文 304 Pages | 納期: 即日から翌営業日

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概要

サンプル依頼リストに入れる

AIソリューションは、医薬品開発プロセスにおいて有望なツールとして浮上しています。これらのAIツールは、企業が検査の精度と効率を高め、医薬品開発を加速し、臨床試験の成果を最適化するのに役立ちます。さらに、臨床試験でAIソフトウェアを活用することで、患者のリクルートとリテンションを高め、試験にかかる時間とコストを削減し、より正確な臨床データ分析、個別化医療、試験デザイン、リアルタイムの患者モニタリングが可能になります。労働集約的な作業を自動化・合理化し、意思決定プロセスを改善し、複雑なデータセットのパターンと動向を特定するAIの能力が、製薬業界の利害関係者から大きな注目と関心を集めていることは注目に値します。2023年5月、米国を拠点とするOwkinは、独自のディープラーニングモデルを腫瘍学の臨床試験分析に使用することで、欧州医薬品庁（EMA）から支持書を受け取っています。同社は、これにより無作為化臨床試験における臨床試験の失敗率を低減できると考えています。さらに、複数の人工知能企業が、臨床試験のための患者識別を最適化するAI搭載プラットフォームを開発しています。さらに、AIアルゴリズムは、適格な参加者を特定するために電子カルテの大量のデータを分析するために訓練することができます。

このような応用や、研究者やスポンサーによるAIの計り知れない可能性の認識により、AI臨床試験の需要は今後も拡大し、臨床試験における患者の転帰を改善することで医薬品開発の状況を一変させる可能性が高いです。

臨床試験におけるAIの市場情勢には、大企業、中堅企業、中小企業が混在しています。現在、約130社が臨床試験を効率化するための様々なソフトウェアやサービスを提供するために必要な専門知識を有しています。特筆すべきは、現在、これらの臨床試験用AIソフトウェアおよびサービスプロバイダーの約80％が、機械学習およびディープラーニングアルゴリズムの活用に注力していることです。この分野における最近の動向は、臨床試験における人工知能企業が、これらのソフトウェアおよびサービスに対する現在および予想される需要に対応するために、その能力をアップグレードしていることを示しています。

近年、複数の人工知能企業が臨床試験領域のAIに関連するパートナーシップを他の業界/非業界のプレーヤーと締結しています。2018年以降、臨床試験におけるAI業界で相当数の戦略的パートナーシップが結ばれていることは注目に値します。AI臨床試験分野の利害関係者によって締結されたパートナーシップの最も一般的なタイプは、製品/技術利用および統合契約であることを強調する価値があります。臨床試験における人工知能にはいくつかの利点があるため、利害関係者は能力を拡大し、包括的な製品/サービスポートフォリオを構築するために、さまざまな臨床試験アプリケーション用のAIソリューション/AIソフトウェアを提供する他の業界プレーヤーを買収しています。2023年2月、ZSはインテリジェントな臨床試験デザイン会社であるTrials.aiを買収し、顧客の臨床試験デザインを再構築するエンドツーエンドのソリューションを強化しました。さらに、ブリストル・マイヤーズ・スクイブ、グラクソ・スミスクライン（GSK）、ジョンソン・エンド・ジョンソン、メルク、ファイザー、ロシュなどの大手製薬企業も、臨床試験におけるAIに関連するパートナーシップ構想に着手しており、AI技術が臨床試験において有望であり、メリットがあることを示しています。

過去6年間で、約600件の臨床試験が完了／進行中であり、さまざまな治療分野の医薬品／治療法の評価にAIツールや技術が利用されています。さらに、臨床試験のほとんどは診断と治療を目的としてデザインされました。カリフォルニア大学、国立アレルギー・感染症研究所、メイヨークリニックは、AIソリューションが関与する臨床試験を完了／実施中の最も積極的なスポンサーであることは注目に値します。

臨床試験におけるAI市場に対する関心の高まりは、過去5年間で、世界中に拠点を置く複数の投資家が臨床試験用のAIソフトウェアやサービスを提供する企業に25億米ドル近くを投資してきた事実からも検証できます。資金の大半はベンチャー・ラウンド、次いでシード・ファイナンシング・ラウンドを通じて調達されています。さらに、ブリストル・マイヤーズ・スクイブ、メルク、ノバルティス、ファイザー、サノフィなどの大手製薬企業も、臨床試験用のAIソフトウェアやサービスを提供する企業に投資しています。2021年6月、アンティドート・テクノロジーズは、デジタル患者エンゲージメント・プログラムと臨床試験募集サービスを拡大するため、2300万米ドルを調達しました。

臨床試験における人工知能に対する需要の高まりに後押しされ、臨床試験向けにAI技術を提供する企業には有利なビジネスチャンスが到来すると予想されます。臨床試験におけるAIの世界市場は著しいペースで成長し、予測期間中のCAGRは16%になると予測されます。臨床試験でAIツールが活用される治療領域のうち、腫瘍性疾患は、患者の募集と維持、試験デザイン、試験実施施設の選定、臨床データ分析、患者モニタリング、個別化治療などのプロセスを合理化するために、こうしたAIソリューションを採用する可能性が最も高いです。エンドユーザー別では、バイオテクノロジーおよび製薬企業が臨床試験AI市場の大半のシェア（75％）を占めるとみられます。

当レポートでは、世界の臨床試験におけるAI市場について調査し、市場の概要とともに、治験フェーズ別、対象治療領域別、エンドユーザー別動向、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。

LIST OF TABLES

Table 4.1. AI in Clinical Trials: Information on Year of Establishment, Company Size, Location of Headquarters
Table 4.2. AI in Clinical Trials: Information on Key Offering(s), Business Model(s) and Deployment Option(s)
Table 4.3. AI in Clinical Trials: Information on Type of AI Technology and Application Area(s)
Table 4.4. AI in Clinical Trials: Information on Potential End-user(s)
Table 5.1. AI in Clinical Trials: List of Companies Profiled
Table 5.2. AiCure: Company Snapshot
Table 5.3. AiCure: AI-based Clinical Trial Offerings
Table 5.4. AiCure: Recent Developments and Future Outlook
Table 5.5. Antidote Technologies: Company Snapshot
Table 5.6. Antidote Technologies: AI-based Clinical Trial Offerings
Table 5.7. Antidote Technologies: Recent Developments and Future Outlook
Table 5.8. Deep 6 AI: Company Snapshot
Table 5.9. Deep 6 AI: AI-based Clinical Trial Offerings
Table 5.10. Deep 6 AI: Recent Developments and Future Outlook
Table 5.11. Innoplexus: Company Snapshot
Table 5.12. Innoplexus: AI-based Clinical Trial Offerings
Table 5.13. Innoplexus: Recent Developments and Future Outlook
Table 5.14. IQVIA: Company Snapshot
Table 5.15. IQVIA: AI-based Clinical Trial Offerings
Table 5.16. IQVIA: Recent Developments and Future Outlook
Table 5.17. Median Technologies: Company Snapshot
Table 5.18. Median Technologies: AI-based Clinical Trial Offerings
Table 5.19. Median Technologies: Recent Developments and Future Outlook
Table 5.20. Medidata: Company Snapshot
Table 5.21. Medidata: AI-based Clinical Trial Offerings
Table 5.22. Medidata: Recent Developments and Future Outlook
Table 5.23. Mendel.ai: Company Snapshot
Table 5.24. Mendel.ai: AI-based Clinical Trial Offerings
Table 5.25. Mendel.ai: Recent Developments and Future Outlook
Table 5.26. Phesi: Company Snapshot
Table 5.27. Phesi: AI-based Clinical Trial Offerings
Table 5.28. Phesi: Recent Developments and Future Outlook
Table 5.29. Saama Technologies: Company Snapshot
Table 5.30. Saama Technologies: AI-based Clinical Trial Offerings
Table 5.31. Saama Technologies: Recent Developments and Future Outlook
Table 5.32. Signant Health: Company Snapshot
Table 5.33. Signant Health: AI-based Clinical Trial Offerings
Table 5.34. Signant Health: Recent Developments and Future Outlook
Table 5.35. Trials.ai: Company Snapshot
Table 5.36. Trials.ai: AI-based Clinical Trial Offerings
Table 6.1. AI in Clinical Trials: List of Partnerships and Collaborations, 2018-2023
Table 7.1 AI in Clinical Trials: List of Funding and Investments, 2018-2023
Table 7.2 Funding and Investment Analysis: Summary of Investments
Table 15.1. Ancora.ai: Company Snapshot
Table 15.2. Deep 6 AI: Company Snapshot
Table 15.3. Intelligencia: Company Snapshot
Table 15.4. nQ Medical: Company Snapshot
Table 15.5. Science 37: Company Snapshot
Table 16.1. AI in Clinical Trials: Distribution by Year of Establishment
Table 16.2. AI in Clinical Trials: Distribution by Company Size
Table 16.3. AI in Clinical Trials: Distribution by Location of Headquarters
Table 16.4. AI in Clinical Trials: Distribution by Company Size and Location of Headquarters (Region)
Table 16.5. AI in Clinical Trials: Distribution by Key Offering(s)
Table 16.6. AI in Clinical Trials: Distribution by Business Model(s)
Table 16.7. AI in Clinical Trials: Distribution by Deployment Option(s)
Table 16.8. AI in Clinical Trials: Distribution by Type of AI Technology
Table 16.9. AI in Clinical Trials: Distribution by Application Area(s)
Table 16.10. AI in Clinical Trials: Distribution by Potential End-user(s)
Table 16.11. IQVIA: Annual Revenues, 2018-3M 2023 (USD Million)
Table 16.12. Median Technologies: Annual Revenues, 2018-2022 (EUR Million)
Table 16.13. Dassault Systems (Parent Company of Medidata): Annual Revenues, 2018-2022 (EUR Million)
Table 16.14. Clinical Trial Analysis: Distribution by Trial Registration Year
Table 16.15. Clinical Trial Analysis: Distribution of Patients Enrolled by Trial Registration Year
Table 16.16. Clinical Trial Analysis: Distribution by Trial Phase
Table 16.17. Clinical Trial Analysis: Distribution by Trial Status
Table 16.18. Clinical Trial Analysis: Distribution by Trial Registration Year and Status
Table 16.19. Clinical Trial Analysis: Distribution by Type of Sponsor
Table 16.20. Clinical Trial Analysis: Distribution by Patient Gender
Table 16.21. Clinical Trial Analysis: Distribution by Patient Age
Table 16.22. Clinical Trial Analysis: Distribution by Target Therapeutic Area
Table 16.23. Clinical Trial Analysis: Distribution by Type of Patient Allocation Model Used
Table 16.24. Clinical Trial Analysis: Distribution by Type of Trial Masking Adopted
Table 16.25. Clinical Trial Analysis: Distribution by Type of Intervention
Table 16.26. Clinical Trial Analysis: Distribution by Trial Purpose
Table 16.27. Partnerships and Collaborations: Cumulative Year-wise Trend, 2018-2023
Table 16.28. Partnerships and Collaborations: Distribution by Type of Partnership
Table 16.29. Partnerships and Collaborations: Distribution by Year and Type of Partnership, 2018-2023
Table 16.30. Partnerships and Collaborations: Distribution by Application Area
Table 16.31. Partnerships and Collaborations: Distribution by Target Therapeutic Area
Table 16.32. Partnerships and Collaborations: Distribution by Type of Partner
Table 16.33. Most Active Players: Distribution by Number of Partnership
Table 16.34. Partnerships and Collaborations: Local and International Agreements
Table 16.35. Partnerships and Collaborations: Analysis by Location of Headquarters (Country-wise)
Table 16.36. Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
Table 16.37. Funding and Investment Analysis: Cumulative Year-wise Trend, 2018-2023
Table 16.38. Funding and Investment Analysis: Distribution by Amount Invested (USD Million)
Table 16.39. Funding and Investment Analysis: Distribution by Type of Funding
Table 16.40. Funding and Investment Analysis: Distribution by Type of Funding and Amount Invested (USD Million)
Table 16.41. Most Active Players: Distribution by Amount Raised and Number of Funding Instances
Table 16.42. Leading Investors: Distribution by Number of Funding Instances
Table 16.43. Funding and Investment Analysis: Distribution of Amount Invested by Geography
Table 16.44. Funding and Investment Analysis: Distribution of Number of Funding Instances by Geography
Table 16.45. Big Pharma Initiatives: Distribution by Year of Initiative
Table 16.46. Big Pharma Initiatives: Distribution by Type of Initiative
Table 16.47. Big Pharma Initiatives: Distribution by Application Area of AI
Table 16.48. Big Pharma Initiatives: Distribution by Target Therapeutic Area
Table 16.49. Value Creation Framework: Trends in Research Activity
Table 16.50. Value Creation Framework: Trends in Intellectual Property
Table 16.51. Overall Cost Saving Potential of AI in Clinical Trials, 2023 and 2035 (USD Million)
Table 16.52. Overall Cost Saving Potential of AI in Clinical Trials , 2023-2035 (USD Million)
Table 16.53. Cost Saving Potential: Distribution by Trial Phase, 2023 and 2035 (USD Million)
Table 16.54. Cost Saving Potential in Phase I Clinical Trials, 2023-2035 (USD Million)
Table 16.55. Cost Saving Potential in Phase II Clinical Trials, 2023-2035 (USD Million)
Table 16.56. Cost Saving Potential in Phase III Clinical Trials, 2023-2035 (USD Million)
Table 16.57. Cost Saving Potential: Distribution by Trial Procedures, 2023 and 2035 (USD Million)
Table 16.58. Cost Saving Potential in Patient Recruitment, 2023-2035 (USD Million)
Table 16.59. Cost Saving Potential in Patient Retention, 2023-2035 (USD Million)
Table 16.60. Cost Saving Potential in Staffing and Administration, 2023-2035 (USD Million)
Table 16.61. Cost Saving Potential in Site Monitoring, 2023-2035 (USD Million)
Table 16.62. Cost Saving Potential in Source Data Verification, 2023-2035 (USD Million)
Table 16.63. Cost Saving Potential in Other Procedures, 2023-2035 (USD Million)
Table 16.64. Global AI in Clinical Trials Market, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.65. AI in Clinical Trials Market: Distribution by Trial Phase, 2023 and 2035 (USD Million)
Table 16.66. AI in Clinical Trials Market for Phase I, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.67. AI in Clinical Trials Market for Phase II, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.68. AI in Clinical Trials Market for Phase III, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.69. AI in Clinical Trials Market: Distribution by Target Therapeutic Area, 2023 and 2035 (USD Million)
Table 16.70. AI in Clinical Trials Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.71. AI in Clinical Trials Market for CNS Disorders, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.72. AI in Clinical Trials Market for Infectious Diseases, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.73. AI in Clinical Trials Market for Metabolic Disorders, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.74. AI in Clinical Trials Market for Oncological Disorders, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.75. AI in Clinical Trials Market for Other Disorders, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.76. AI in Clinical Trials Market: Distribution by End-user, 2023 and 2035 (USD Million)
Table 16.77. AI in Clinical Trials Market for Biotechnology and Pharmaceutical Companies, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.78. AI in Clinical Trials Market for Academia and Other End-users, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.79. AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035 (USD Million)
Table 16.80. AI in Clinical Trials Market in North America, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.81. AI in Clinical Trials Market in Europe, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.82. AI in Clinical Trials Market in Asia-Pacific, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.83. AI in Clinical Trials Market in Middle East and North Africa, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 16.84. AI in Clinical Trials Market in Latin America, Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)

LIST OF FIGURES

Figure 2.1. Executive Summary: Overall Market Landscape
Figure 2.2. Executive Summary: Clinical Trial Analysis
Figure 2.3. Executive Summary: Partnerships and Collaborations
Figure 2.4. Executive Summary: Funding and Investment Analysis
Figure 2.5. Executive Summary: Market Sizing and Opportunity Analysis
Figure 3.1. Evolution of AI
Figure 3.2. Subfields of AI
Figure 3.3. Types of Algorithms of Machine Learning
Figure 3.4. Applications of AI in Healthcare
Figure 3.5. Recent Examples of AI in Healthcare
Figure 3.6. Applications of AI in Clinical Trials
Figure 3.7. Challenges Associated with the Adoption of AI
Figure 4.1. AI in Clinical Trials: Distribution by Year of Establishment
Figure 4.2. AI in Clinical Trials: Distribution by Company Size
Figure 4.3. AI in Clinical Trials: Distribution by Location of Headquarters
Figure 4.4. AI in Clinical Trials: Distribution by Company Size and Location of Headquarters (Region-wise)
Figure 4.5. AI in Clinical Trials: Distribution by Key Offering(s)
Figure 4.6. AI in Clinical Trials: Distribution by Business Model(s)
Figure 4.7. AI in Clinical Trials: Distribution by Deployment Option(s)
Figure 4.8. AI in Clinical Trials: Distribution by Type of AI Technology
Figure 4.9. AI in Clinical Trials: Distribution by Application Area(s)
Figure 4.10. AI in Clinical Trials: Distribution by Potential End-user(s)
Figure 5.1. IQVIA: Annual Revenues, 2018-Q1 2023 (USD Million)
Figure 5.2. Median Technologies: Annual Revenues, 2018-2022 (EUR Million)
Figure 5.3. Dassault Systems (Parent Company of Medidata): Annual Revenues, 2018-2022 (EUR Million)
Figure 6.1. Clinical Trial Analysis: Distribution by Trial Registration Year
Figure 6.2. Clinical Trial Analysis: Distribution of Patients Enrolled by Trial Registration Year
Figure 6.3. Clinical Trial Analysis: Distribution by Trial Phase
Figure 6.4. Clinical Trial Analysis: Distribution by Trial Status
Figure 6.5. Clinical Trial Analysis: Distribution by Trial Registration Year and Status
Figure 6.6. Clinical Trial Analysis: Distribution by Type of Sponsor
Figure 6.7. Clinical Trial Analysis: Distribution by Patient Gender
Figure 6.8. Clinical Trial Analysis: Distribution by Patient Age
Figure 6.9. Word Cloud Analysis: Emerging Focus Areas
Figure 6.10. Clinical Trial Analysis: Distribution by Target Therapeutic Area
Figure 6.11. Clinical Trial Analysis: Distribution by Type of Patient Allocation Model Used
Figure 6.12. Clinical Trial Analysis: Distribution by Type of Trial Masking Adopted
Figure 6.13. Clinical Trial Analysis: Distribution by Type of Intervention
Figure 6.14. Clinical Trial Analysis: Distribution by Trial Purpose
Figure 6.15. Most Active Players: Distribution by Number of Clinical Trials
Figure 6.16. Clinical Trial Analysis: Distribution of Clinical Trials by Geography
Figure 6.17. Clinical Trial Analysis: Distribution of Clinical Trials by Geography and Trial Status
Figure 6.18. Clinical Trial Analysis: Distribution of Patients Enrolled by Geography and Trial Registration Year
Figure 6.19. Clinical Trial Analysis: Distribution of Patients Enrolled by Geography and Trial Status
Figure 7.1. Partnerships and Collaborations: Cumulative Year-wise Trend, 2018-2023
Figure 7.2. Partnerships and Collaborations: Distribution by Type of Partnership
Figure 7.3. Partnerships and Collaborations: Distribution by Year and Type of Partnership, 2018-2023
Figure 7.4. Partnerships and Collaborations: Distribution by Application Area
Figure 7.5. Partnerships and Collaborations: Distribution by Target Therapeutic Area
Figure 7.6. Partnerships and Collaborations: Distribution by Type of Partner
Figure 7.7. Most Active Players: Distribution by Number of Partnership
Figure 7.8. Partnerships and Collaborations: Local and International Agreements
Figure 7.9. Partnerships and Collaborations: Analysis by Location of Headquarters (Country-wise)
Figure 7.10. Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
Figure 8.1. Funding and Investment Analysis: Cumulative Year-wise Trend, 2018-2023
Figure 8.2. Funding and Investment Analysis: Distribution by Amount Invested (USD Million)
Figure 8.3. Funding and Investment Analysis: Distribution by Type of Funding
Figure 8.4. Funding and Investment Analysis: Distribution by Type of Funding and Year of Establishment of Recipient Companies
Figure 8.5. Funding and Investment Analysis: Distribution by Type of Funding and Amount Invested (USD Million)
Figure 8.6. Most Active Players: Distribution by Amount Raised and Number of Funding Instances
Figure 8.7. Leading Investors: Distribution by Number of Funding Instances
Figure 8.8. Funding and Investment Analysis: Distribution of Amount Invested by Geography
Figure 8.9. Funding and Investment Analysis: Distribution of Number of Funding Instances by Geography
Figure 8.10. Funding and Investment Summary, 2018-2023 (USD Million)
Figure 9.1. Big Pharma Initiatives: Distribution by Year of Initiative
Figure 9.2. Big Pharma Initiatives: Distribution by Type of Initiative
Figure 9.3. Heat Map: Distribution by Type of Initiative
Figure 9.4. Big Pharma Initiatives: Distribution by Application Area of AI
Figure 9.5. Heat Map: Distribution by Application Area of AI
Figure 9.6. Big Pharma Initiatives: Distribution by Target Therapeutic Area
Figure 9.7. Heat Map: Distribution by Target Therapeutic Area
Figure 9.8. Benchmarking Analysis: Wind Rose Chart
Figure 11.1. Value Creation Framework: Trends in Research Activity
Figure 11.2. Value Creation Framework: Trends in Intellectual Property
Figure 11.3. Value Creation Framework: Extent of Innovation versus Associated Risk Matrix
Figure 11.4. Value Creation Framework: Comparison of Key Tools / Technologies
Figure 11.5. Value Creation Framework: Summary
Figure 12.1. Overall Cost Saving Potential of AI in Clinical Trials, 2023 and 2035 (USD Million)
Figure 12.2. Overall Cost Saving Potential of AI in Clinical Trials, 2023-2035 (USD Million)
Figure 12.3. Cost Saving Potential: Distribution by Trial Phase, 2023 and 2035 (USD Million)
Figure 12.4. Cost Saving Potential in Phase I Clinical Trials, 2023-2035 (USD Million)
Figure 12.5. Cost Saving Potential in Phase II Clinical Trials, 2023-2035 (USD Million)
Figure 12.6. Cost Saving Potential in Phase III Clinical Trials, 2023-2035 (USD Million)
Figure 12.7. Cost Saving Potential: Distribution by Trial Procedure, 2023 and 2035 (USD Million)
Figure 12.8. Cost Saving Potential in Patient Recruitment, 2023-2035 (USD Million)
Figure 12.9. Cost Saving Potential in Patient Retention, 2023-2035 (USD Million)
Figure 12.10. Cost Saving Potential in Staffing and Administration, 2023-2035 (USD Million)
Figure 12.11. Cost Saving Potential in Site Monitoring, 2023-2035 (USD Million)
Figure 12.12. Cost Saving Potential in Source Data Verification, 2023-2035 (USD Million)
Figure 12.13. Cost Saving Potential in Other Procedures, 2023-2035 (USD Million)
Figure 13.1. Global AI in Clinical Trials Market, 2023-2035 (USD Million)
Figure 13.2. AI in Clinical Trials Market: Distribution by Trial Phase, 2023 and 2035 (USD Million)
Figure 13.3. AI in Clinical Trials Market for Phase I, 2023-2035 (USD Million)
Figure 13.4. AI in Clinical Trials Market for Phase II, 2023-2035 (USD Million)
Figure 13.5. AI in Clinical Trials Market for Phase III, 2023-2035 (USD Million)
Figure 13.6. AI in Clinical Trials Market: Distribution by Target Therapeutic Area, 2023 and 2035 (USD Million)
Figure 13.7. AI in Clinical Trials Market for Cardiovascular Disorders, 2023-2035 (USD Million)
Figure 13.8. AI in Clinical Trials Market for CNS Disorders, 2023-2035 (USD Million)
Figure 13.9. AI in Clinical Trials Market for Infectious Diseases, 2023-2035 (USD Million)
Figure 13.10. AI in Clinical Trials Market for Metabolic Disorders, 2023-2035 (USD Million)
Figure 13.11. AI in Clinical Trials Market for Oncological Disorders, 2023-2035 (USD Million)
Figure 13.12. AI in Clinical Trials Market for Other Disorders, 2023-2035 (USD Million)
Figure 13.13. AI in Clinical Trials Market: Distribution by End-user, 2023 and 2035 (USD Million)
Figure 13.14. AI in Clinical Trials Market for Biotechnology and Pharmaceutical Companies, 2023-2035 (USD Million)
Figure 13.15. AI in Clinical Trials Market for Academia and Other End-users, 2023-2035 (USD Million)
Figure 13.16. AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035 (USD Million)
Figure 13.17. AI in Clinical Trials Market in North America, 2023-2035 (USD Million)
Figure 13.18. AI in Clinical Trials Market in Europe, 2023-2035 (USD Million)
Figure 13.19. AI in Clinical Trials Market in Asia-Pacific, 2023-2035 (USD Million)
Figure 13.20. AI in Clinical Trials Market in Middle East and North Africa, 2023-2035 (USD Million)
Figure 13.21. AI in Clinical Trials Market in Latin America, 2023-2035 (USD Million)
Figure 14.1. Concluding Remarks: Overall Market Landscape
Figure 14.2. Concluding Remarks: Clinical Trial Analysis
Figure 14.3. Concluding Remarks: Partnerships and Collaborations
Figure 14.4. Concluding Remarks: Funding and Investment Analysis
Figure 14.5. Concluding Remarks: Big Pharma Initiatives
Figure 14.5. Concluding Remarks: Market Sizing and Opportunity Analysis

Product Code: RA100441

INTRODUCTION

The global AI in clinical trials market is estimated to be worth $ 1.4 billion in 2023 and expected to grow at compounded annual growth rate (CAGR) of 16% during the forecast period.

The process of successfully developing a novel therapeutic intervention is both time and cost intensive. In fact, it is estimated that a drug requires around 10 years and over $ 2.5 billion capital investment, before reaching the market. , In this process, clinical trials play a crucial role for assessing the drug's efficacy and safety in humans. These trials account for nearly 50% of the time and capital expenditure during drug development. However, sponsors face financial burdens and significant delays in marketing drugs due to unsuccessful clinical trials. Over the past few decades, the success rate of a drug candidate advancing the clinical trials to obtaining marketing approval has remained relatively constant at approximately 10% - 20%. This can be attributed to the factors contributing to clinical stage intervention failure, including inadequate study design, incomplete patient recruitment, improper subject stratification and high rate of clinical trial participant attrition. In order to overcome these challenges and streamline the clinical trial processes, stakeholders in the pharmaceutical industry are exploring innovative solutions and strategies. One such innovative strategy involves integrating AI in drug development, which has the potential to revolutionize traditional methods, particularly in clinical trials. It is worth noting that artificial intelligence in clinical trials can help integrate and analyze large volumes of data, enabling trial sponsors to optimize future research initiatives. Additionally, by addressing issues related to trial design, patient recruitment and retention, site selection, data interpretation, and treatment evaluation, AI has the potential to enhance and refine the entire process of clinical drug development. Moreover, in the first nine months of 2021, more than $20 billion was invested into artificial intelligence companies focused on healthcare, exceeding the prior investment, which was around $15 billion in 2020. Therefore, with the rising interest of investors in this field, we anticipate the AI in clinical trials market to witness healthy growth during the forecast period.

SCOPE OF THE REPORT

The AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers, Distribution by Trial Phase (Phase I, Phase II and Phase III), Target Therapeutic Area (Cardiovascular Disorders, CNS Disorders, Infectious Diseases, Metabolic Disorders, Oncological Disorders and Other Disorders), End-user (Pharmaceutical and Biotechnology Companies, and Other End-users) and Key Geographical Regions (North America, Europe, Asia-Pacific, Latin America, and Middle East and North Africa ): Industry Trends and Global Forecasts, 2023-2035 report features an extensive study of the current market landscape, market size and future opportunities associated with the AI in clinical trials market, during the given forecast period. Further, the report highlights the efforts of several stakeholders engaged in this rapidly emerging segment of the pharmaceutical industry. Key takeaways of the AI in clinical trials market report are briefly discussed below.

Benefits and Growing Demand for Artificial Intelligence Solutions for Patient Recruitment and Clinical Data Analysis

AI solutions have emerged as a promising tool in the drug development process. These AI tools help companies improve the accuracy and efficiency of testing, accelerate drug development and optimize clinical trial outcomes. In addition, leveraging AI software in clinical trials helps increasing patient recruitment and retention, reduces trial time and cost, and provides more accurate clinical data analysis, personalized medicine, trial design and real-time patient monitoring. It is worth highlighting that the ability of AI to automate and streamline labor-intensive tasks, improve decision-making processes, and identify patterns and trends in complex datasets has garnered significant attention and interest from stakeholders in the pharmaceutical industry. In May 2023, US based Owkin received letter of support from the European Medicines Agency (EMA) for the use of proprietary deep learning models for oncology clinical trial analysis; the company believes that this can reduce the clinical trial failure rates in randomized clinical trial. Further several artificial intelligence companies have developed AI-powered platforms that optimize patient identification for clinical trials. Additionally, AI algorithms can be trained to analyze large amounts of data in electronic health records to identify eligible participants.

Owing to these applications and recognition of the immense potential of AI by researchers and sponsors, the demand for AI clinical trials is likely to continue to grow and transform the landscape of drug development by improving patient outcomes in clinical trials.

Current Market Landscape of AI in Clinical Trials: AI Software and Service Providers

The AI in clinical trials market landscape features a mix of large, mid-sized and small companies. Currently, around 130 players have the required expertise to offer various software and services to streamline clinical studies. Notably, at present, around 80% of these AI in clinical trials software and service providers are focusing on leveraging machine learning and deep learning algorithms, as they minimize data-based errors by accessing various data points simultaneously. Recent developments in this field indicate that the artificial intelligence companies in clinical trials are upgrading their capabilities to accommodate the current and anticipated demand for these software and services.

Partnership and Collaboration Trends in the AI in Clinical Trials Market

In recent years, several artificial intelligence companies have inked partnerships related to AI in clinical trials domain with other industry / non-industry players. It is worth highlighting that, since 2018, a significant number of strategic partnerships have been inked in the AI in clinical trials industry. It is worth highlighting that product / technology utilization and integration agreements are the most common types of partnerships inked by stakeholders in the AI clinical trials field. Owing to several advantages of artificial intelligence in clinical trials, stakeholders are acquiring other industry players offering AI solutions / AI software for different clinical trial applications in order to expand their capabilities and build a comprehensive product / service portfolio. In February 2023, ZS acquired Trials.ai, an intelligent study design company, to enhance its end-to-end solutions to reimagine study design for its clients. In addition, several big pharma companies, such as Bristol Myers Squibb, GlaxoSmithKline (GSK), Johnson & Johnson, Merck, Pfizer and Roche, have also taken partnership initiatives related to AI in clinical trials, indicating the promise and benefits that AI technology holds in clinical trials.

Key Trends in the AI in Clinical Trials Market

In the past six years, around 600 completed / ongoing clinical trials utilized AI tools and technologies for evaluating drugs / therapies for different therapeutic areas, indicating the substantial efforts made by researchers engaged in this domain. Further, most of the clinical studies were designed for the purpose of diagnostics and treatment. It is worth noting that the University of California, the National Institute of Allergy and Infectious Diseases, and Mayo Clinic are among the most active sponsors of completed / ongoing clinical trials involving AI solutions.

Rise in Investment in AI in Clinical Trials Market

The heightened interest in the AI in clinical trials market can be validated by the fact that, in the last five years, close to $2.5 billion has been invested in companies engaged in providing AI software and services for clinical trials by several investors based across the globe. The majority of the funds have been raised through venture rounds, followed by seed financing rounds. In addition, several big pharma players, such as Bristol Myers Squibb, Merck, Novartis, Pfizer and Sanofi have also invested in AI software and service providers for clinical trials. In June 2021, Antidote Technologies raised $23 million to expand its digital patient engagement programs and clinical trial recruitment services.

AI in Clinical Trials Market Size

Driven by the rising demand for artificial intelligence in clinical trials, lucrative opportunities are expected to emerge for players offering AI technology for clinical studies. The global market for AI in clinical trials is anticipated to grow at a significant pace, with a CAGR of 16% during the forecast period. Among the therapeutic areas for which AI tools are leveraged in clinical trials, oncological disorders are most likely to adopt these AI solutions for streamlining processes, such as patient recruitment and retention, trial design, site selection, clinical data analysis, patient monitoring and personalized treatment. In terms of end-users, biotechnology and pharmaceutical companies are likely to hold the majority share (75%) of the AI in clinical trials market.

Key Artificial Intelligence Companies Supporting Clinical Trials

Examples of the key companies engaged in the AI in clinical trials domain (the complete list of players is available in the full report) include (in alphabetic order) Acclinate, AiCure, Aidar Health, Aitia, A.I. VALI, Ancora.ai, Antidote Technologies, Beacon Biosignals, BUDDI.AI, ConcertAI, Curify, Deep 6 AI, ICON, Innoplexus, Massive Bio, Median Technologies, Novadiscovery, Owkin, PHASTAR, SiteRx and Viz.ai. This market report also includes an easily searchable excel database of all the AI software / AI solutions and service providers for clinical trials worldwide.

The research report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this market, across different geographies. Amongst other elements, the market report includes:

An executive summary of the insights captured during our research. It offers a high-level view on the current scenario of AI in clinical trials market and its likely evolution in the mid to long term.
A general overview of artificial intelligence in clinical trials, highlighting details on artificial intelligence and its subfields. It also presents information on the applications of AI in healthcare and clinical trials, and challenges associated with the adoption of AI. Additionally, it features a discussion on the future perspectives of the AI in clinical trials industry.
A detailed assessment of the current market landscape of the companies offering AI software and service for clinical trials, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, key offering(s) (device, technology / platform and service), business model(s) (software as a service (SaaS), technology licensing, CRO / fee-for-service model and product provider), deployment option(s) (cloud-based and on-premise), type of AI technology (machine learning, deep learning, natural language processing and others), application area(s) (data analysis, medical imaging, patient recruitment, trial design, site selection, patient engagement, integrated patient care, patient trial monitoring, personalized treatment and report generation) and potential end-user(s) (pharmaceutical / biotechnology companies, hospitals, research institutes and CROs).
Elaborate profiles of the prominent companies (shortlisted based on a proprietary criterion) developing AI software / AI solutions and offering services for clinical trials. Each profile features a brief overview of the company (including information on its year of establishment, number of employees, location of headquarters and key members of the leadership team), financial information (if available), details related to AI-based clinical trial offerings, recent developments and an informed future outlook.
An insightful clinical trial analysis of completed / ongoing clinical trials leveraging AI, based on various relevant parameters, such as trial registration year, number of patients enrolled, trial phase, trial status, type of sponsor, patient gender, patient age, emerging focus areas, target therapeutic area, patient allocation model used, trial masking adopted, type of intervention, trial purpose, most active players (in terms of number of clinical trials sponsored) and geography.
A detailed analysis of the partnerships inked between stakeholders in the AI in clinical trials market, since 2018, covering product / technology utilization agreements, product / technology integration agreements, technology licensing agreements, research and development agreements, product development agreements, mergers and acquisitions, service agreements, service alliances and other relevant agreements.
An analysis of the investments made, including seed financing, venture capital financing, capital raised from IPOs, grants, debt financing and other equity, and subsequent offerings, at various stages of development in start-ups, small and mid-sized companies that are focused on offering AI software and services for clinical trials.
A detailed analysis of the initiatives taken by big pharma players related to AI in clinical trials, based on various relevant parameters, such as year of initiative, type of initiative, application area of AI, target therapeutic area and leading big pharma players (in terms of number of AI in clinical trials focused initiatives).
An insightful framework depicting the implementation of several advanced tools and technologies, such as blockchain, big data analytics, real-world evidence, digital twins, cloud computing and internet of things (IoT) at different steps of a clinical study, which can assist service providers in addressing existing unmet needs. Further, it provides a detailed analysis on ease of implementation and associated risk in integrating above-mentioned technologies, based on the trends highlighted in published literature and patents.
A detailed cost saving analysis, highlighting the overall cost saving potential of AI in clinical trials till 2035. We have highlighted the cost saving potential of AI in clinical trials for different trial phases (phase I, phase II and phase III) and trial procedures (patient recruitment, patient retention, staffing and administration, site monitoring, source data verification and other procedures).

One of the key objectives of this market report was to estimate the current market size, opportunity and the future growth potential of AI in clinical trials market, over the forecast period. We have provided informed estimates on the likely evolution of the market for the forecast period, 2023-2035. Additionally, historical trends of the market have also been presented for the time period, 2018-2022. Further, our year-wise projections of the current and forecasted opportunity have been segmented based on relevant parameters, such as trial phase (phase I, phase II and phase III), target therapeutic area (cardiovascular disorders, CNS disorders, infectious diseases, metabolic disorders, oncological disorders and other disorders), end-user (pharmaceutical and biotechnology companies, and other end-users) and key geographical regions (North America, Europe, Asia-Pacific, Latin America, and Middle East and North Africa). In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the market growth.

The opinions and insights presented in the report were influenced by discussions held with stakeholders in this industry. The report also features detailed transcripts of interviews held with various industry stakeholders:

Danielle Ralic (Co-Founder, Chief Executive Officer and Chief Technology Officer, Ancora.ai)

Wout Brusselaers (Founder and Chief Executive Officer, Deep 6 AI)

Dimitrios Skaltsas (Co-Founder and Executive Director, Intelligencia)

R. A. Bavasso (Founder and Chief Executive Officer, nQ Medical)

Grazia Mohren (Head of Marketing), Michael Shipton (Chief Commercial Officer), Darcy Forman (Chief Delivery Officer), Troy Bryenton (Chief Technology Officer, Science 37)

All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.

RESEARCH METHODOLOGY

The data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews / surveys with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Wherever possible, the available data has been checked for accuracy from multiple sources of information.

The secondary sources of information include:

Annual reports
Investor presentations
SEC filings
Industry databases
News releases from company websites
Government policy documents
Industry analysts' views

While the focus has been on forecasting the market till 2035, the report also provides our independent view on various technological and non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information.

KEY QUESTIONS ANSWERED:

Question 1: How is AI and ML used in clinical trials?

Answer: AI and machine learning are used to enhance various aspects of the clinical trial process. They can help in patient recruitment by analyzing large datasets to identify suitable candidates, improving the trial design by simulating and optimizing protocols, and aiding in data analysis by automating the extraction and interpretation of information from medical records and trial data. Additionally, AI and ML can contribute to diverse event detection and monitoring, improving safety and efficiency in clinical trials.

Question 2: How AI can improve clinical trials?

Answer: AI and machine learning can help reduce the time and cost associated with conducting clinical studies.

Question 3: What are the challenges associated with the integration of AI in clinical trials?

Answer: Integrating AI in clinical trials involves various challenges, such as ensuring data quality and availability, enhancing interpretability and transparency of AI algorithms, addressing regulatory compliance and ethical considerations, and relying on human expertise to validate and interpret AI-generated insights. Furthermore, incorporating AI tools into existing clinical trial processes and workflows can give rise to logistic and operational complexities.

Question 4: What is the role of AI in electronic health records of clinical trials data?

Answer: AI in electronic health records (EHRs) of clinical trials offer several benefits. It can help automate data extraction and analysis from EHRs, improving efficiency and accuracy. Additionally, AI algorithms can identify patterns and trends in patient data, aiding in patient stratification, adverse event detection, and treatment response prediction. Furthermore, AI can assist in identifying potential eligibility criteria for clinical trials and facilitate the identification of suitable participants.

Question 5: What are the upcoming trends in AI in clinical trial market?

Answer: The field of AI is rapidly evolving; new trends and advancements of artificial intelligence in clinical trials include the integration of tools and technologies, such as digital twins, real-world evidence, blockchain, big data analytics, cloud computing and internet of things (IoT) in order to streamline clinical trials and achieve desired outcome.

Question 6: What is the global market size of AI in clinical trials market?

Answer: The global AI in clinical trials market is estimated to be worth $ 1.4 billion in 2023.

Question 7: What are the leading market segments in the global AI in clinical trials market ?

Answer: In terms of target therapeutic area, oncological disorders are likely to capture close to 35% of the current market.

Question 8: Which region captures the largest share in the AI in clinical trials market?

Answer: Presently, the AI in clinical trials market is dominated by North America, capturing around 35% of the overall market size, followed by Asia-Pacific.

Question 9: What is the likely growth rate (CAGR) for AI in clinical trial market?

Answer: The AI in clinical trials market is projected to grow at an annualized rate (CAGR) of 16%, during the forecast period 2023-2035.

Question 10: Which are the leading artificial intelligence companies in clinical trials market?

Answer: At present, around 130 companies are engaged in providing AI software / AI solutions and services for clinical trials. Examples of top players engaged in this market (which have also been captured in this report) include Acclinate, AiCure, Beacon Biosignals, Labcorp, Owkin and SiteRx.

CHAPTER OUTLINES

Chapter 1: is a preface providing an overview of the full report, AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers, 2023-2035.

Chapter 2: is an executive summary of the insights captured during our research. It offers a high-level view on the current scenario of AI in clinical trials market and its likely evolution in the mid-term and long term.

Chapter 3: provides a general overview of AI in clinical trials, highlighting details on artificial intelligence and its subfields. It also presents information on the applications of AI in healthcare and clinical trials, and challenges associated with the adoption of AI. Additionally, it features a discussion on the future perspectives of the AI in clinical trials industry.

Chapter 4: includes detailed assessment of the current market landscape of the companies offering AI in clinical trials software and service, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, key offering(s) (device, technology / platform and service), business model(s) (software as a service (SaaS), technology licensing, CRO / fee-for-service model and product provider), deployment option(s) (cloud-based and on-premise), type of AI technology (machine learning, deep learning, natural language processing and others), application area(s) (data analysis, medical imaging, patient recruitment, trial design, site selection, patient engagement, integrated patient care, patient trial monitoring, personalized treatment and report generation) and potential end-user(s) (pharmaceutical / biotechnology companies, hospitals, research institutes and CROs).

Chapter 5: features profiles of the prominent companies (shortlisted based on a proprietary criterion) developing AI software / AI solutions and offering services for clinical trials. Each profile features a brief overview of the company (including information on its year of establishment, number of employees, location of headquarters and key members of the leadership team), financial information (if available), details related to AI-based clinical trial offerings, recent developments and an informed future outlook.

Chapter 6: includes insightful clinical trial analysis of completed / ongoing clinical trials leveraging AI, based on various relevant parameters, such as trial registration year, number of patients enrolled, trial phase, trial status, type of sponsor, patient gender, patient age, emerging focus areas, target therapeutic area, patient allocation model used, trial masking adopted, type of intervention, trial purpose, most active players (in terms of number of clinical trials sponsored) and geography.

Chapter 7: provides a detailed analysis of the partnerships inked between stakeholders in the AI in clinical trials market, since 2018, covering product / technology utilization agreements, product / technology integration agreements, technology licensing agreements, research and development agreements, product development agreements, mergers and acquisitions, service agreements, service alliances and other relevant agreements.

Chapter 8: includes detailed analysis of the investments made, including seed financing, venture capital financing, capital raised from IPOs, grants, debt financing and other equity, and subsequent offerings, at various stages of development in start-ups, small and mid-sized companies that are focused on offering AI software and services for clinical trials.

Chapter 9: includes detailed analysis of the initiatives taken by big pharma players related to AI in clinical trials, based on various relevant parameters, such as year of initiative, type of initiative, application area of AI, target therapeutic area and leading big pharma players (in terms of number of AI in clinical trials focused initiatives)

Chapter 10: features a detailed case study of the use cases of AI in clinical trials, presenting information on collaborations inked between various AI software and service providers, and healthcare organizations. Each use case provides a brief overview of the companies involved, business needs and details on the objectives achieved and solutions provided.

Chapter 11: features an insightful framework depicting the implementation of several advanced tools and technologies, such as blockchain, big data analytics, real-world evidence, digital twins, cloud computing and internet of things (IoT), at different steps of a clinical study, which can assist service providers in addressing existing unmet needs. Further, it provides a detailed analysis on ease of implementation and associated risk in integrating above-mentioned technologies, based on the trends highlighted in published literature and patents.

Chapter 12: includes detailed cost saving analysis, highlighting the overall cost saving potential of AI in clinical trials till 2035. We have highlighted the cost saving potential of AI in clinical trials at different trial phases (phase I, phase II and phase III) and trial procedures (patient recruitment, patient retention, staffing and administration, site monitoring, source data verification and other procedures).

Chapter 13: presents a comprehensive market forecast and opportunity analysis, highlighting the future potential of the AI in clinical trials market till 2035. We have segregated the current and upcoming opportunity based on trial phase (phase I, phase II and phase III), target therapeutic area (cardiovascular disorders, CNS disorders, infectious diseases, metabolic disorders, oncological disorders and other disorders), end-user (pharmaceutical and biotechnology companies, and other end-users) and key geographical regions (North America, Europe, Asia-Pacific, Latin America, Middle East and North Africa).

Chapter 14: summarizes the overall report. In this chapter, we have provided a list of key takeaways from the report, and expressed our independent opinion related to the research and analysis described in the previous chapters.

Chapter 15: provides the transcripts of interviews conducted with key stakeholders in this industry.

Chapter 16: is an appendix, which contains tabulated data and numbers for all the figures included in this report.

Chapter 17: is an appendix, which contains a list of companies and organizations mentioned in this report.

1. PREFACE

1.1. Introduction
1.2. Key Market Insights
1.3. Scope of the Report
1.4. Research Methodology
1.5. Frequently Asked Questions
1.6. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

3.1. Chapter Overview
3.2. Overview of Artificial Intelligence (AI)
3.3. Subfields of AI
3.4. Applications of AI in Healthcare
- 3.4.1. Drug Discovery
- 3.4.2. Drug Manufacturing
- 3.4.3. Marketing
- 3.4.4. Diagnosis and Treatment
- 3.4.5. Clinical Trials
3.5. Applications of AI in Clinical Trials
3.6. Challenges Associated with the Adoption of AI
3.7. Future Perspective

4. MARKET LANDSCAPE

4.1. Chapter Overview
4.2. AI in Clinical Trials: AI Software and Service Providers Landscape
- 4.2.1. Analysis by Year of Establishment
- 4.2.2. Analysis by Company Size
- 4.2.3. Analysis by Location of Headquarters
- 4.2.4. Analysis by Company Size and Location of Headquarters (Region)
- 4.2.5. Analysis by Key Offering(s)
- 4.2.6. Analysis by Business Model(s)
- 4.2.7. Analysis by Deployment Option(s)
- 4.2.8. Analysis by Type of AI Technology
- 4.2.9. Analysis by Application Area(s)
- 4.2.10. Analysis by Potential End-user(s)

5. COMPANY PROFILES

5.1. Chapter Overview
5.2. AiCure
- 5.2.1. Company Overview
- 5.2.2. AI-based Clinical Trial Offerings
- 5.2.3. Recent Developments and Future Outlook
5.3. Antidote Technologies
- 5.3.1. Company Overview
- 5.3.2. AI-based Clinical Trial Offerings
- 5.3.3. Recent Developments and Future Outlook
5.4. Deep 6 AI
- 5.4.1. Company Overview
- 5.4.2. AI-based Clinical Trial Offerings
- 5.4.3. Recent Developments and Future Outlook
5.5. Innoplexus
- 5.5.1. Company Overview
- 5.5.2. AI-based Clinical Trial Offerings
- 5.5.3. Recent Developments and Future Outlook
5.6. IQVIA
- 5.6.1. Company Overview
- 5.6.2. Financial Information
- 5.6.3. AI-based Clinical Trial Offerings
- 5.6.4. Recent Developments and Future Outlook
5.7. Median Technologies
- 5.7.1. Company Overview
- 5.7.2. Financial Information
- 5.7.3. AI-based Clinical Trial Offerings
- 5.7.4. Recent Developments and Future Outlook
5.8. Medidata
- 5.8.1. Company Overview
- 5.8.2. Financial Information
- 5.8.3. AI-based Clinical Trial Offerings
- 5.8.4. Recent Developments and Future Outlook
5.9. Mendel.ai
- 5.9.1. Company Overview
- 5.9.2. AI-based Clinical Trial Offerings
- 5.9.3. Recent Developments and Future Outlook
5.10. Phesi
- 5.10.1. Company Overview
- 5.10.2. AI-based Clinical Trial Offerings
- 5.10.3. Recent Developments and Future Outlook
5.11. Saama Technologies
- 5.11.1. Company Overview
- 5.11.2. AI-based Clinical Trial Offerings
- 5.11.3. Recent Developments and Future Outlook
5.12. Signant Health
- 5.12.1. Company Overview
- 5.12.2. AI-based Clinical Trial Offerings
- 5.12.3. Recent Developments and Future Outlook
5.13. Trials.ai
- 5.13.1. Company Overview
- 5.13.2. AI-based Clinical Trial Offerings
- 5.13.3. Recent Developments and Future Outlook

6. CLINICAL TRIAL ANALYSIS

6.1. Chapter Overview
6.2. Scope and Methodology
6.3. AI in Clinical Trials
- 6.3.1. Analysis by Trial Registration Year
- 6.3.2. Analysis by Number of Patients Enrolled
- 6.3.3. Analysis by Trial Phase
- 6.3.4. Analysis by Trial Status
- 6.3.5. Analysis by Trial Registration Year and Status
- 6.3.6. Analysis by Type of Sponsor
- 6.3.7. Analysis by Patient Gender
- 6.3.8. Analysis by Patient Age
- 6.3.9. Word Cloud Analysis: Emerging Focus Areas
- 6.3.10. Analysis by Target Therapeutic Area
- 6.3.11. Analysis by Study Design
  - 6.3.11.1. Analysis by Type of Patient Allocation Model Used
  - 6.3.11.2. Analysis by Type of Trial Masking Adopted
  - 6.3.11.3. Analysis by Type of Intervention
  - 6.3.11.4. Analysis by Trial Purpose
- 6.3.12. Most Active Players: Analysis by Number of Clinical Trials
- 6.3.13. Analysis of Clinical Trials by Geography
- 6.3.14. Analysis of Clinical Trials by Geography and Trial Status
- 6.3.15. Analysis of Patients Enrolled by Geography and Trial Registration Year
- 6.3.16. Analysis of Patients Enrolled by Geography and Trial Status

7. PARTNERSHIPS AND COLLABORATIONS

7.1. Chapter Overview
7.2. Partnership Models
7.3. AI in Clinical Trials: 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 Application Area
- 7.3.5. Analysis by Target Therapeutic Area
- 7.3.6. Analysis by Type of Partner
- 7.3.7. Most Active Players: Analysis by Number of Partnerships
- 7.3.8. Analysis by Geography
  - 7.3.8.1. Local and International Agreements
  - 7.3.8.2. Analysis by Location of Headquarters (Country-wise)
  - 7.3.8.3. Intercontinental and Intracontinental Agreements

8. FUNDING AND INVESTMENT ANALYSIS

8.1. Chapter Overview
8.2. Types of Funding
8.3. AI in Clinical Trials: List of Funding and Investments
- 8.3.1. Analysis by Year of Funding
- 8.3.2. Analysis by Amount Invested
- 8.3.3. Analysis by Type of Funding
- 8.3.4. Analysis by Type of Funding and Amount Invested
- 8.3.5. Most Active Players: Analysis by Amount Raised and Number of Funding

Instances

8.3.6. Leading Investors: Analysis by Number of Funding Instances
8.3.7. Analysis of Amount Invested by Geography
8.3.8. Analysis of Number of Funding Instances by Geography

8.4. Concluding Remarks

9. BIG PHARMA INITIATIVES

9.1. Chapter Overview
9.2. Scope and Methodology
9.3. Analysis by Year of Initiative
9.4. Analysis by Type of Initiative
9.5. Analysis by Application Area of AI
9.6. Analysis by Target Therapeutic Area
9.7. Benchmarking Analysis: Big Pharma Players

10. AI IN CLINICAL TRIALS: USE CASES

10.1. Chapter Overview
10.2. Use Case 1: Collaboration between Roche and AiCure
- 10.2.1. Roche
- 10.2.2. AiCure
- 10.2.3. Business Needs
- 10.2.4. Objectives Achieved and Solutions Provided
10.3. Use Case 2: Collaboration between Takeda and AiCure
- 10.3.1. Takeda
- 10.3.2. AiCure
- 10.3.3. Business Needs
- 10.3.4. Objectives Achieved and Solutions Provided
10.4. Use Case 3: Collaboration between Teva Pharmaceuticals and Intel
- 10.4.1. Teva Pharmaceuticals
- 10.4.2. Intel
- 10.4.3. Business Needs
- 10.4.4. Objectives Achieved and Solutions Provided
10.5. Use Case 4: Collaboration between Unnamed Pharmaceutical Company and Antidote
- 10.5.1. Antidote
- 10.5.2. Business Needs
- 10.5.3. Objectives Achieved and Solutions Provided
10.6. Use Case 5: Collaboration between Unnamed Pharmaceutical Company and Cognizant
- 10.6.1. Cognizant
- 10.6.2. Business Needs
- 10.6.3. Objectives Achieved and Solutions Offered
10.7. Use Case 6: Collaboration between Cedars-Sinai Medical Center and Deep 6 AI
- 10.7.1. Cedars-Sinai Medical Center
- 10.7.2. Deep 6 AI
- 10.7.3. Business Needs
- 10.7.4. Objectives Achieved and Solutions Offered
10.8. Use Case 7: Collaboration between GlaxoSmithKline (GSK) and PathAI
- 10.8.1. PathAI
- 10.8.2. GlaxoSmithKline (GSK)
- 10.8.3. Business Needs
- 10.8.4. Objectives Achieved and Solutions Provided
10.9. Use Case 8: Collaboration between Bristol Myers Squibb (BMS) and Concert AI
- 10.9.1. Concert AI
- 10.9.2. Bristol Myers Squibb (BMS)
- 10.9.3. Business Needs
- 10.9.4. Objectives Achieved and Solutions Provided

11. VALUE CREATION FRAMEWORK: A STRATEGIC GUIDE TO ADDRESS UNMET NEEDS IN CLINICAL TRIALS

11.1. Chapter Overview
11.2. Unmet Needs in Clinical Trials
11.3. Key Assumptions and Methodology
11.4. Key Tools / Technologies
- 11.4.1. Blockchain
- 11.4.2. Big Data Analytics
- 11.4.3. Real-world Evidence
- 11.4.4. Digital Twins
- 11.4.5. Cloud Computing
- 11.4.6. Internet of Things (IoT)
11.5. Trends in Research Activity
11.6. Trends in Intellectual Capital
11.7. Extent of Innovation versus Associated Risks
11.8. Results and Discussion
11.9. Summary

12. COST SAVING ANALYSIS

12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Cost Saving Potential of AI in Clinical Trials, 2023-2035
- 12.3.1. Cost Saving Potential in Phase I Clinical Trials, 2023-2035
- 12.3.2. Cost Saving Potential in Phase II Clinical Trials, 2023-2035
- 12.3.3. Cost Saving Potential in Phase III Clinical Trials, 2023-2035
- 12.3.4. Cost Saving Potential in Patient Recruitment, 2023-2035
- 12.3.5. Cost Saving Potential in Patient Retention, 2023-2035
- 12.3.6. Cost Saving Potential in Staffing and Administration, 2023-2035
- 12.3.7. Cost Saving Potential in Site Monitoring, 2023-2035
- 12.3.8. Cost Saving Potential in Source Data Verification, 2023-2035
- 12.3.9. Cost Saving Potential in Other Procedures, 2023-2035

13. MARKET SIZING AND OPPORTUNITY ANALYSIS

13.1. Chapter Overview
13.2. Forecast Methodology and Key Assumptions
13.3. Global AI in Clinical Trials Market, 2023-2035
- 13.3.1. AI in Clinical Trials Market: Distribution by Trial Phase, 2023 and 2035
  - 13.3.1.1. AI in Clinical Trials Market for Phase I, 2023-2035
  - 13.3.1.2. AI in Clinical Trials Market for Phase II, 2023-2035
  - 13.3.1.3. AI in Clinical Trials Market for Phase III, 2023-2035
- 13.3.2. AI in Clinical Trials Market: Distribution by Target Therapeutic Area, 2023 and 2035
  - 13.3.2.1. AI in Clinical Trials Market for Cardiovascular Disorders, 2023-2035
  - 13.3.2.2. AI in Clinical Trials Market for CNS Disorders, 2023-2035
  - 13.3.2.3. AI in Clinical Trials Market for Infectious Diseases, 2023-2035
  - 13.3.2.4. AI in Clinical Trials Market for Metabolic Disorders, 2023-2035
  - 13.3.2.5. AI in Clinical Trials Market for Oncological Disorders, 2023-2035
  - 13.3.2.6. AI in Clinical Trials Market for Other Disorders, 2023-2035
- 13.3.3. AI in Clinical Trials Market: Distribution by End-user, 2023 and 2035
  - 13.3.3.1. AI in Clinical Trials Market for Biotechnology and Pharmaceutical Companies, 2023-2035
  - 13.3.3.2. AI in Clinical Trials Market for Other End-users, 2023-2035
- 13.3.4. AI in Clinical Trials Market: Distribution by Key Geographical Regions, 2023 and 2035
  - 13.3.4.1. AI in Clinical Trials Market in North America, 2023-2035
  - 13.3.4.2. AI in Clinical Trials Market in Europe, 2023-2035
  - 13.3.4.3. AI in Clinical Trials Market in Asia-Pacific, 2023-2035
  - 13.3.4.4. AI in Clinical Trials Market in Middle East and North Africa, 2023-2035
  - 10.3.4.4. AI in Clinical Trials Market in Latin America, 2023-2035

14. CONCLUSION

15. EXECUTIVE INSIGHTS

15.1. Chapter Overview
15.2. Ancora.ai
- 15.2.1. Company Snapshot
- 15.2.2. Interview Transcript: Danielle Ralic, Co-Founder, Chief Executive Officer and Chief Technology Officer
15.3. Deep 6 AI
- 15.3.1. Company Snapshot
- 15.3.2. Interview Transcript: Wout Brusselaers, Founder and Chief Executive Officer
15.4. Intelligencia
- 15.4.1. Company Snapshot
- 15.4.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director
15.5. nQ Medical
- 15.5.1. Company Snapshot
- 15.5.2. Interview Transcript: R. A. Bavasso, Founder and Chief Executive Officer
15.6. Science 37
- 15.6.1. Company Snapshot
- 15.6.2. Interview Transcript: Grazia Mohren (Head of Marketing), Michael Shipton (Chief Commercial Officer), Darcy Forman (Chief Delivery Officer), Troy Bryenton (Chief Technology Officer)

臨床試験におけるAI市場（第2版）：AIソフトウェアとサービスプロバイダー- 治験フェーズ別、対象治療領域別、エンドユーザー別、主要地域別：業界動向と世界の予測、2023年～2035年

AI in Clinical Trials Market (2nd Edition): AI Software and Service Providers - Distribution by Trial Phase, Target Therapeutic Area, End-user and Key Geographical Regions: Industry Trends and Global Forecasts, 2023-2035

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

第4章 市場情勢

第5章 企業プロファイル

第6章 臨床試験の分析

第7章 パートナーシップとコラボレーション

第8章 資金調達と投資の分析

第9章 大手製薬会社の取り組み

第10章 臨床試験におけるAI：使用例

第11章 価値創造フレームワーク：臨床試験でアンメットニーズに対処するための戦略的ガイド

第12章 コスト削減の分析

第13章 市場規模と機会分析

第14章 結論

第15章 経営陣の洞察

第16章 付録I：表形式のデータ

第17章 付録II：企業および組織のリスト

LIST OF TABLES

LIST OF FIGURES

INTRODUCTION

SCOPE OF THE REPORT

Benefits and Growing Demand for Artificial Intelligence Solutions for Patient Recruitment and Clinical Data Analysis

Current Market Landscape of AI in Clinical Trials: AI Software and Service Providers

Partnership and Collaboration Trends in the AI in Clinical Trials Market

Key Trends in the AI in Clinical Trials Market

Rise in Investment in AI in Clinical Trials Market

AI in Clinical Trials Market Size

Key Artificial Intelligence Companies Supporting Clinical Trials

RESEARCH METHODOLOGY

The secondary sources of information include:

KEY QUESTIONS ANSWERED:

Question 1: How is AI and ML used in clinical trials?

Question 2: How AI can improve clinical trials?

Question 3: What are the challenges associated with the integration of AI in clinical trials?

Question 4: What is the role of AI in electronic health records of clinical trials data?

Question 5: What are the upcoming trends in AI in clinical trial market?

Question 6: What is the global market size of AI in clinical trials market?

Question 7: What are the leading market segments in the global AI in clinical trials market ?

Question 8: Which region captures the largest share in the AI in clinical trials market?

Question 9: What is the likely growth rate (CAGR) for AI in clinical trial market?

Question 10: Which are the leading artificial intelligence companies in clinical trials market?

CHAPTER OUTLINES

TABLE OF CONTENTS

1. PREFACE

2. EXECUTIVE SUMMARY

3. INTRODUCTION

4. MARKET LANDSCAPE

5. COMPANY PROFILES

6. CLINICAL TRIAL ANALYSIS

7. PARTNERSHIPS AND COLLABORATIONS

8. FUNDING AND INVESTMENT ANALYSIS

9. BIG PHARMA INITIATIVES

10. AI IN CLINICAL TRIALS: USE CASES

11. VALUE CREATION FRAMEWORK: A STRATEGIC GUIDE TO ADDRESS UNMET NEEDS IN CLINICAL TRIALS

12. COST SAVING ANALYSIS

13. MARKET SIZING AND OPPORTUNITY ANALYSIS

14. CONCLUSION

15. EXECUTIVE INSIGHTS

16. APPENDIX I: TABULATED DATA

17. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

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

第4章市場情勢

第5章企業プロファイル

第6章臨床試験の分析

第7章パートナーシップとコラボレーション

第8章資金調達と投資の分析

第9章大手製薬会社の取り組み

第10章臨床試験におけるAI：使用例

第11章価値創造フレームワーク：臨床試験でアンメットニーズに対処するための戦略的ガイド

第12章コスト削減の分析

第13章市場規模と機会分析

第14章結論

第15章経営陣の洞察

第16章付録I：表形式のデータ

第17章付録II：企業および組織のリスト