Artificial Intelligence-based Clinical Trial Solutions for Patient Matching Market Size, Share | Report 2022-2030

Drug discovery and development is a cost-intensive and time-consuming process.

Drug discovery and development is a cost-intensive and time-consuming process. According to several studies, the capital investment required for drug development could cost more than USD 1.0 billion and a time period of more than 10 years. Adoption of AI-powered solutions in managing and conducting clinical trials can be beneficial in curbing these obstacles, thereby, reducing clinical trial cycle time & cost and increasing productivity & accuracy of the trial development. Drug makers have to invest a significant amount in drug development to replenish their respective product pipelines as a majority of big sellers go off patent, which is one of the major reasons for the clinical trials’ market growth.

In addition, growing public and private initiatives to support the adoption of AI-powered technological solutions in clinical trial studies are driving the market growth. Pharmaceutical and biotechnology companies are rapidly adopting AI-based tools and platforms to support their clinical research studies. Companies are using these solutions for enhanced patient identification, recruitment, engagement, and real-time monitoring. Healthcare IT expenditures account for a large share of healthcare expense in developed countries. Rising awareness levels and the rate of digital transformation in developed countries are growing rapidly. Nations are adopting AI-based tools to enhance efficiency and reduce costs.

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Digitization in biomedical and clinical research is creating the way for the growth of AI-based clinical trials solutions for the patient matching market. Major pharmaceutical companies are adopting technologically advanced solutions for better clinical trial results and patient management. In addition, the COVID-19 pandemic has changed the perception of clinical trials. It has increased the utilization and penetration of AI-based solutions for reducing time & cost. Hence, these factors are increasing the adoption of AI in pharmaceutical companies. For instance, major pharmaceutical companies such as Pfizer, Johnson & Johnson, Sanofi, Novartis, and Bayer have initiated strategic alliances for the adoption of AI-based solutions in drug discovery & development and clinical trials management. Governments of developed nations such as the U.S. are providing both funding and laying out a stringent regulatory framework to boost the adoption of AI-based solutions for clinical trial studies. In addition, governments of developing nations are spreading awareness amongst stakeholders to drive new drug discoveries and accelerate patient recruitment and enhance patient engagement & monitoring using AI-powered solutions.

AI-based clinical trial solutions for patient matching can help enhance patient recruitment by reducing patient population heterogeneity by harmonizing large patient health information data from a wide array of platforms & sources such as electronic medical records (EMRs), omics data, and medical imaging. These solutions enable choosing the most appropriate patient population groups with the highest likelihood of responding to the trial and a higher probability of providing a measurable & quantifiable clinical endpoint. AI-based systems and ssolutions can be deployed in analyzing patient health information records & clinical trial eligibility criterion and matching them with recruiting clinical trial studies. For instance, AI-powered clinical trial solutions used in patient matching have increased patient recruitment in lung cancer trial studies by 58.4%. AI-based solutions integrate natural language processing (NLP) algorithms which improve the match rate between clinical trials and patient enrollment.

The therapeutic application segment has been further classified into oncology, cardiovascular diseases, neurological diseases, metabolic diseases, infectious diseases, and others.The oncology application segment portrays the highest growth potential owing to the growing expenditure towards the pre-clinical and clinical development of oncology therapy products. AI-driven solutions offer an exceptional capability to accelerate the process of development of oncology drugs with a higher success rate. The end-user segment is bifurcated into pharmaceutical companies, academia, and others. Pharmaceutical companies are rapidly adopting AI-based solutions in designing and planning their clinical trial studies. Growing awareness levels and increasing funding for adopting these advanced technologies are driving the growth.

Nice entrepreneurial startups offering patient-matching AI-based solutions to life science organizations for their clinical studies are directly contributing to the market growth. Some of the renowned market participants are Unlearn.AI, Inc.; Deep6.ai; Deep Lens AI; AmeriSourceBergen Corporation; and Antidote Technologies, Inc. For instance, a France-based startup, Owkin focuses on making clinical trial studies more collaborative by promoting collaboration amongst multiple stakeholders and integrating patient health information data arising from multiple platforms. The company uses federated learning approaches to train its algorithms. Similarly, Deep Lens AI, was founded in 2017 with the ultimate focus of delivering advanced patient matching solutions and laid the foundation of the Virtual Imaging for Pathology Education and Research (VIPER) solution. For instance, in April 2021, Deep Lens AI and Oregon Oncology Specialists entered a strategic collaboration to deploy Deep Lens AI-powered clinical trial solutions to identify eligible patients for clinical trial studies.

Key Players

  • Unlearn.AI, Inc.
  • Antidote Technologies, Inc.
  • Deep6.ai
  • Mendel.ai
  • Aris Global
  • Deep Lens AI
  • AmeriSourceBergen Corporation
  • Koneksa
  • Microsoft Corporation
  • GNS Healthcare

Market Segmentation

By Therapeutic Application Outlook

  • Oncology
  • Cardiovascular Diseases
  • Neurological Diseases or Conditions
  • Metabolic Diseases
  • Infectious Diseases
  • Others

By End-Use Outlook

  • Pharmaceutical Companies
  • Academia
  • Others

By Regional Outlook

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • MEA

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