According to a new study from Research and Markets, the global market for AI-based clinical trial solutions for patient matching is expected to reach USD$1,969 million by 2030. This growth is fueled by sustained and increased investment in AI-based technologies to deliver higher efficiency and improve operational processes in clinical trials, as well as an increased use of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and deep learning to support a more efficient clinical trial process. Traditional R&D processes are known to be time-consuming, costly, and require significant investment. Because of the high development costs, pharmaceutical and biotech companies are increasingly incorporating artificial intelligence (AI) into their research and development efforts in order to reduce costs, improve data quality, and shorten trial lengths.
According to Deloitte, “Artificial intelligence (AI)-enabled data collection and management can be a game changer for life sciences companies in the drug development process.”
These are the problems and solutions that companies like Clintex are exploring through the use of the Clinical Trial Intelligence Tool (CTi).
Clintex is at the forefront of AI-based clinical trial solutions and we believe that Artificial Intelligence (AI) and machine learning techniques have great potential to improve clinical trial efficiencies and expedite the delivery of novel treatments and therapies more quickly and cost-effectively. This is expected to drive significant future growth in the global market. Growing awareness of the benefits of AI-based clinical trials is expected to drive overall market revenue growth over the coming decade. Furthermore, direct uses of AI in clinical trials, such as the use of wearable technology in conjunction with AI techniques, can improve patient adherence to the protocol, which is a major cause of delays and inefficiencies in clinical trials. Clintex has long highlighted this issue and believes that the use of AI-based technologies can help address it.
The aforementioned report also highlights that the number of strategic alliances in the deployment of Artificial Intelligence (AI) in clinical trials will increase over the coming decade. This will reduce time and expenditure during clinical development phases and is expected to result in profitable market growth for companies like Clintex, as we deploy CTi technology into live clinical trial environments. Clintex CTi is designed to support the entire clinical trial process, from study planning and design to data collection and analysis. The tool uses AI-based algorithms to optimize clinical trial processes and improve efficiency. It helps researchers to identify suitable patients for clinical trials, predict potential safety issues, and optimize trial design. By leveraging AI-based technologies, CTi is expected to significantly reduce clinical trial costs, improve the quality of clinical trial data, and shorten the time required to complete clinical trials. The use of CTi is expected to benefit not only pharmaceutical and biotech companies but also research organizations and patients participating in clinical trials.
Clintex’ CTI tool will be implemented in the upcoming CANPAIN trial being conducted by LVL Health, pending confirmation of the go-live date. The CANPAIN trial is currently in the set-up phase, and Clintex will be working with LVL to integrate CTi into their clinical trial process according to the terms of the signed NDA agreement. The implementation of CTi is expected to improve clinical trial efficiencies and help expedite the delivery of novel treatments and therapies. Clintex looks forward to the opportunity to collaborate with LVL on this important trial and contribute to the advancement of medical research in this novel field.
Companies like Clintex are leading the way in the development and deployment of AI-based solutions, and the continued adoption of these technologies is expected to lead to more efficient and effective clinical trials, benefiting patients and society as a whole.