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臨床試驗人工智能市場(第2版):人工智能軟件和服務提供商-按臨床試驗階段,目標治療領域,最終用戶和主要地區劃分:2023-2035年行業趨勢.全球預測
市場調查報告書
商品編碼
1319262

臨床試驗人工智能市場(第2版):人工智能軟件和服務提供商-按臨床試驗階段,目標治療領域,最終用戶和主要地區劃分:2023-2035年行業趨勢.全球預測

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
出版日期: | 出版商: Roots Analysis | 英文 304 Pages | 商品交期: 最快1-2個工作天內

價格

人工智能解決方案已成為藥物開發過程中很有前景的工具。這些人工智能工具可幫助公司提高測試準確性和效率、加速藥物開發並優化臨床試驗結果。此外，在臨床試驗中使用人工智能軟件可以增加患者招募和保留，減少試驗時間和成本，並使更準確的臨床數據分析、個性化醫療、試驗設計和實時患者監測成為可能。值得注意的是，人工智能能夠自動化和簡化勞動密集型任務、改進決策流程以及識別複雜數據集中的模式和趨勢，這些能力受到了製藥行業利益相關者的極大關注和興趣。2023 年 5 月，總部位於美國的 Owkin 收到了歐洲藥品管理局 (EMA) 的認可函，認可其使用其專有的深度學習模型進行腫瘤學臨床試驗分析。該公司相信這將降低隨機臨床試驗的臨床試驗失敗率。此外，一些人工智能公司正在開發人工智能驅動的平台，以優化臨床試驗的患者識別。此外，可以訓練人工智能算法來分析大量電子病歷數據，以識別合格的參與者。

像這樣的應用程序，以及研究人員和讚助商對人工智能巨大潛力的認識，將繼續推動對人工智能臨床試驗的需求，通過改善臨床試驗中的患者結果來改變藥物開發的格局。

臨床試驗中人工智能的市場格局是大型、中型和小型企業的混合體。如今，大約 130 家公司擁有必要的專業知識，可以提供一系列軟件和服務來簡化臨床試驗。值得注意的是，大約 80% 的臨床試驗人工智能軟件和服務提供商現在專注於利用機器學習和深度學習算法。該領域的最新發展表明，臨床試驗中的人工智能公司正在升級其能力，以滿足這些軟件和服務當前和預計的需求。

近年來，多家人工智能公司已與其他行業和非行業參與者建立了與人工智能臨床試驗相關的合作夥伴關係。值得注意的是，自2018年以來，臨床試驗行業的人工智能已經建立了大量的戰略合作夥伴關係。值得強調的是，人工智能臨床試驗領域利益相關者最常見的合作夥伴類型是產品/技術利用和集成協議。由於人工智能在臨床試驗中的多項優勢，利益相關者正在尋找適用於各種臨床試驗應用的人工智能解決方案/人工智能軟件，以擴展其能力並建立全面的產品/服務組合。2023 年 2 月，ZS 收購了智能臨床試驗設計公司 Trials.ai，以增強其端到端解決方案，為客戶重新構想臨床試驗設計。此外，Bristol-Myers Squibb, GlaxoSmithKline (GSK), Johnson & Johnson, Merck, Pfizer, Roche等主要製藥公司也推出了與人工智能臨床試驗相關的合作計劃，該技術在臨床試驗中顯示出前景和益處。

過去六年來，已經完成/正在進行近 600 項臨床試驗，利用人工智能工具和技術來評估各個治療領域的藥物/治療方法。此外，大多數臨床試驗都是為了診斷和治療目的而設計的。值得注意的是，加州大學、國家過敏和傳染病研究所和Mayo Clinic是涉及人工智能解決方案的已完成/正在進行的臨床試驗最積極的讚助商。

人們對用於臨床試驗的AI市場日益濃厚的興趣源於這樣一個事實：在過去的五年裡，來自世界各地的多個投資者向為臨床試驗提供AI軟件和服務的公司投資了近25億美元。也可以得到驗證。大部分資金是通過風險投資輪和種子輪融資籌集的。此外，Bristol-Myers Squibb, Merck, Novartis, Pfizer, Sanofi等主要製藥公司也在投資為臨床試驗提供人工智能軟件和服務的公司。2021 年 6 月，Antidote Technologies 籌集了 2300 萬美元，用於擴大其數字患者參與計劃和臨床試驗招募服務。

由於臨床試驗中人工智能的需求不斷增長，我們預計為臨床試驗提供人工智能技術的公司將獲得利潤豐厚的商機。臨床試驗中人工智能的全球市場預計將以驚人的速度增長，在預測期內復合年增長率為 16%。在臨床試驗中利用人工智能工具的治療領域中，腫瘤學簡化了患者招募和保留、研究設計、地點選擇、臨床數據分析、患者監測和個性化護理等流程。從最終用戶來看，生物技術和製藥公司將佔據臨床試驗人工智能市場的大部分份額（75%）。

本報告審視了全球用於臨床試驗的人工智能市場，並提供了市場概況、臨床試驗階段、目標治療領域、最終用戶趨勢、地區趨勢以及進入市場的公司概況。

目錄

第一章 前言

第 2 章執行摘要

第 3 章 簡介

第四章 市場狀況

  • 章節概述
  • 臨床試驗中的人工智能：人工智能軟件和服務提供商的前景

第五章 公司簡介

  • 章節概述
  • AiCure
  • Antidote Technologies
  • Deep 6 AI
  • Innoplexus
  • IQVIA
  • Median Technologies
  • Medidata
  • Mendel.ai
  • Phesi
  • Saama Technologies
  • Signant Health
  • Trials.ai

第六章 臨床試驗分析

  • 章節概述
  • 範圍和方法
  • 臨床試驗中的人工智能

第七章 夥伴關係與合作

  • 章節概述
  • 合作模式
  • 臨床試驗中的人工智能：合作夥伴關係列表

第八章 資金及投資分析

第九章 各大藥企的努力

第 10 章臨床試驗中的人工智能：用例

第十一章價值創造框架：解決臨床試驗中未滿足需求的戰略指南

  • 章節概述
  • 臨床試驗中未滿足的需求
  • 主要假設和研究方法
  • 關鍵工具/技術
  • 調查活動趨勢
  • 智力資本趨勢
  • 創新程度和相關風險
  • 結果與討論
  • 概括

第十二章成本節約分析

  • 章節概述
  • 主要假設和研究方法
  • 人工智能在臨床試驗中的總體成本節約潛力（2023-2035）

第十三章市場規模與機會分析

  • 章節概述
  • 預測研究方法和關鍵假設
  • 全球人工智能臨床試驗市場，2023-2035

第14章結論

第十五章管理見解

  • 章節概述
  • Ancora.ai
  • Deep 6 AI
  • Intelligencia
  • nQ Medical
  • Science 37

第十六章附錄一：表格數據

17 附錄二：公司和組織名單

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.

TABLE OF CONTENTS

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)

16. APPENDIX I: TABULATED DATA

17. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

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