AI-based Clinical Trial Solution Providers Market, 2020-2030

New York, Aug. 12, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "AI-based Clinical Trial Solution Providers Market, 2020-2030" - https://www.reportlinker.com/p05950923/?utm_source=GNW
5 billion in capital investment, before reaching the market. In this process, clinical trials are a crucial requirement, enabling both innovators and regulators to assess the efficacy of a candidate drug and establish whether it is safe for use in humans. It is estimated that nearly 50% of the total time and capital expenditure during the drug development process, is on conducting clinical research. However, all trials are not successful; they are prone to delays (due to various reasons), and failure, both of which are known to impose enormous financial burdens on sponsors. According to a study conducted by the MIT Sloan School of Management, the rate of clinical success, defined as the proportion of trials that result in approval of the drug / therapy under investigation, was currently estimated to be 14%. The study further demonstrated that there is significant variance in the aforementioned rate across different types of therapies; for instance, for vaccines against infectious diseases, clinical success was estimated to be slightly above 30%, while for investigational anti-cancer drugs, it was 3%. Some of the key factors responsible for clinical stage product failure include inadequate study design, insufficient / incomplete patient recruitment, improper subject stratification during study conduct, and high rate of participant attrition. ,

In attempts to address the abovementioned challenges, stakeholders in the pharmaceutical industry are actively exploring diverse strategies and solutions, one of which involves the collection and processing of real-world data. In fact, real-world data analysis is deemed to possess the potential to offer valuable insights from patient / healthcare provider testimonies, in order to drive future trial optimization efforts and facilitate better decision making during clinical research conduct. However, in order to generate actionable insights from real world medical data, there is a need for robust and advanced data mining technologies, such as big data analytics and artificial intelligence (AI) powered tools. Data integration, evolutionary modelling and pattern recognition using predictive AI models, can enable trial sponsors to aggregate, curate, and analyze large volumes of data, thereby, harnessing information captured during past trials to drive future therapy development initiatives. Experts also believe that the use of AI-powered solutions have the potential to address some of the commonly reported challenges, such as concerns related to clinical trial design, patient recruitment and retention, site selection, medical data interpretation and evaluation of treatment efficacy, which are encountered during trial conduct. Considering that the aforementioned issues are addressed, it is safe to presume that opting to use AI-enabled technologies in clinical trials may eventually improve clinical R&D, and allow innovators to optimize on both time and capital investments made in such initiatives. Currently, this technology is still in its early stages, with limited adoption across the world. However, it is worth mentioning that close to USD 4 billion was invested into AI-focused healthcare startups, in 2019. We are led to believe that the opportunity for AI-based solution providers within the healthcare industry is likely to grow at a significant in the foreseen future.

SCOPE OF THE REPORT
The ‘AI-based Clinical Trial Solution Providers Market, 2020-2030’ report features an extensive study of the companies offering AI-based platforms for clinical trial applications, in addition to the current market landscape and their future potential.

Amongst other elements, the report features:
- A detailed assessment of the competitive landscape of AI-based solution providers based on parameters, such as area of application, year of establishment, company size and location of headquarters.
- Brief profiles of prominent players engaged in offering AI-based solutions for clinical trial applications. Each profile features a brief overview of the company and its proprietary technology platform(s), recent developments and an informed future outlook.
- An analysis of the partnerships and collaborations inked in the domain, in the period between 2014 and 2020 (till May), based on several parameters, such as year of partnership, type of partnership, application mentioned in agreement, target therapeutic area mentioned in the agreement, year of partnership and type of partner, most active players and geographical analysis.
- An analysis of the funding and investments made in the domain, in the period between 2014 and 2020 (till May), including seed financing, venture capital financing, debt financing, grants, capital raised from IPOs and subsequent offerings, at various stages of development in companies that are engaged in this field, based on several parameters, such as number of funding instances, amount invested, type of funding, leading players and investors, and geographical analysis
- A detailed analysis of completed, ongoing and planned clinical trials involving the use of AI, based on multiple parameters, such as trial registration year, trial phase, trial status, type of sponsor / collaborator, target therapeutic area, trial design, top sponsor, geographical location of trial and enrolled patient population.
- An analysis of various AI related initiatives of top 10 big pharma players (based on revenue), based on multiple parameters, such as year of initiative, type of initiative, focus of initiative, area of application and target therapeutic area. In addition, leading players and leading partners have been highlighted based on the number of initiatives.
- A case study on recent use cases, wherein various pharmaceutical / healthcare companies have employed AI-based solutions for different processes of clinical trials, highlighting different business needs of such players and key takeaways of the solution provided by AI- based solution providers.
- An in-depth analysis of the cost saving potential across various processes of clinical drug development that can be brought about by the implementation of bespoke AI-based solutions.

One of the key objectives of the report was to understand the primary growth drivers and estimate the future opportunity within this market. Based on several parameters, such as annual number of clinical trials, average capital investment per trial across different phases and therapeutic areas, cost saving potential of AI and expected annual growth rate across various geographies, we have provided an informed estimate of the likely evolution of the market, in the mid to long term, for the period 2020-2030. The chapter features the likely distribution of the opportunity across different [A] trial phase (phase I, phase II and phase III), [B] therapeutic areas (cardiovascular disorders, CNS disorders, infectious disorders, metabolic disorders, oncological disorders and other disorders), [C] end-users (pharmaceutical companies, and academia and other users) and [D] key geographical regions (North America, Europe, Asia-Pacific and rest of the world).

In order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution. The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders in this domain.

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

RESEARCH METHODOLOGY
The research, analysis and insights presented in this report are backed by a deep understanding of insights gathered from both secondary and primary sources. For all our projects, we conduct interviews with experts in the area (academia, industry 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. Where 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 2030, the report also provides our independent view on various 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
- Who are the leading AI-based clinical trial solution providers?
- How has the clinical activity involving the use of AI evolved in recent years?
- What is the focus area of big pharma players in the AI domain?
- Which companies have raised significant amount of money in the domain?
- What is the total cost saving potential of AI-based clinical solutions across different steps of a clinical trial?
- What kind of partnership models are presently being used by stakeholders in the industry?
- What factors are likely to influence the evolution of this upcoming market?
- How is the current and future opportunity likely to be distributed across key market segments?

CHAPTER OUTLINES
Chapter 2 is an executive summary of the insights captured in our research. It offers a high-level view on the likely evolution of the artificial intelligence in clinical trials market in the mid to long term.

Chapter 3 provides a brief overview of artificial intelligence, machine learning and natural language processing. It also highlights the classification of AI, as well as the applications of AI in the healthcare domain. Further, the chapter includes various challenges associated with the adoption of AI in healthcare and its future perspectives.

Chapter 4 provides an overview of the competitive landscape of AI-based solution providers based on parameters, such as area of application, year of establishment, company size and location of headquarters.

Chapter 5 includes brief profiles of prominent companies engaged in offering AI-based platforms for clinical trials, featuring a brief overview of the company and its proprietary platform(s), recent developments and an informed future outlook.

Chapter 6 features an in-depth analysis and discussion on the various partnerships inked between the players in this domain, in the time period between 2014 and 2020, based on several parameters, such as year of partnership, type of partnership, application mentioned in agreement, target therapeutic area mentioned in the agreement, year of partnership and type of partner, most active players and geographical analysis.

Chapter 7 presents details on various investments received by companies that are engaged in offering clinical trial services using artificial intelligence. It also includes an analysis of the funding instances that have taken place in the market, up to 2020 (till May), based on several parameters, such as number of funding instances, amount invested, type of funding, leading players and investors, and geographical analysis

Chapter 8 provides an analysis of completed, ongoing and planned clinical studies using artificial intelligence, featuring details on registration year, trial phase, trial status, type of sponsor, target therapeutic area, trial design, top sponsor, geographical location of trial and enrolled patient population.

Chapter 9 provides various AI related initiatives of top 10 big pharma players (based on revenue), based on multiple parameters, such as year of initiative, type of initiative, focus of initiative, area of application and target therapeutic area. In addition, leading players and leading partners have been highlighted based on the number of initiatives.

Chapter 10 presents a case study on recent use cases, wherein various pharmaceutical / healthcare companies have employed AI-based platforms for different processes of clinical trials, highlighting different business needs of such players and key takeaways of the solution provided by AI- based solution providers.

Chapter 11 features an insightful analysis, highlighting the cost saving potential offered by AI-based solutions across various processes of clinical trials, such as patient recruitment, patient adherence, source data verification and site monitoring, across phase I, phase II and phase III clinical trials.

Chapter 12 presents a detailed market forecast, highlighting the future potential of the AI-based clinical trial solutions market, for the time period 2020-2030. The chapter features the likely distribution of the opportunity across different [A] trial phase (phase I, phase II and phase III), [B] therapeutic areas (cardiovascular disorders, CNS disorders, infectious disorders, metabolic disorders, oncological disorders and other disorders), [C] end-users (pharmaceutical companies, and academia and other users) and [D] key geographical regions (North America, Europe, Asia-Pacific and rest of the world).

Chapter 13 summarizes the entire report. It presents a list of key takeaways and offers our independent opinion on the current market scenario. Further, it summarizes the various evolutionary trends that are likely to influence the future of this market.

Chapter 14 is a collection of executive insight(s) of the discussions that were held with various key stakeholders in this market.

Chapter 15 is an appendix, which provides tabulated data and numbers for all the figures included in the report.

Chapter 16 is an appendix, which contains the list of companies and organizations mentioned in the report.
Read the full report: https://www.reportlinker.com/p05950923/?utm_source=GNW

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