Market Trends of Generative AI In Clinical Trials Industry
Clinical Trial Design Segment Expected to Witness Significant Growth Over the Forecast Period
Machine learning, a subset of AI, is transforming clinical research, and these areas include clinical trial design, digital health technologies, and real-world data analytics. Due to its ability to enhance strategic planning and execution phases, the clinical trial design segment is poised to drive the growth of generative AI in the clinical trials market. Furthermore, there is a growing reliance on AI-driven methodologies to streamline research protocols and refine health outcome precision.
Clinical trials are pivotal in pharmaceutical development, serving as the benchmark for validating new drugs' safety and efficacy. Generative AI (Gen AI) is a transformative technology poised to revolutionize clinical trials by refining trial designs and optimizing patient recruitment. Numerous studies have delved into generative AI's potential in refining trial designs and enhancing patient recruitment.
For instance, a May 2024 article in Clinical Research highlighted that generative AI leverages electronic health records (EHRs), social media activities, and diverse data sources to identify and recruit optimal clinical trial candidates. Moreover, AI algorithms outpace traditional methods in matching patient profiles with trial criteria, leading to heightened recruitment rates and expedited enrollment.
According to the aforementioned source, Gen AI models possess the capability to simulate diverse trial designs, identifying the most effective protocols. By scrutinizing historical trial data and forecasting potential outcomes, AI empowers researchers to design trials with a higher likelihood of success. This encompasses decisions on optimal sample sizes, treatment regimens, and endpoints. Such capabilities not only foster more efficient and adaptive clinical trials but are also expected to bolster the anticipated growth of generative AI in clinical trial designs over the forecast period.
Furthermore, the launches of new generative AI platforms for advancing and maintaining clinical trial information are anticipated to bolster the segment’s growth over the forecast period. For instance, Clarivate PLC launched a new enhanced search platform leveraging generative artificial intelligence (GenAI). The new Clarivate offering enables drug discovery, pre-clinical, clinical, regulatory affairs, and portfolio strategy teams to interact with multiple complex datasets using natural language to obtain immediate and in-depth insights.
Therefore, the rising adoption of generative AI in designing clinical trials is expected to augment the growth of the segment over the forecast period.
North America Expected to Hold a Significant Market Share Over the Forecast Period
North America is poised for substantial growth during the forecast period. This surge can be attributed to several factors, such as the increasing demand for expedited and efficient clinical trial processes to introduce new therapies, advancements in sophisticated AI algorithms bolstered by enhanced computational power, and a rising collaboration between AI companies, research institutions, and pharmaceutical companies in the region.
The rise of generative AI (GenAI) in the clinical trial arena stands out as the primary catalyst for this market growth. For instance, an April 2024 article in Applied Clinical Trials highlighted that GenAI is poised to steer the clinical development model toward a more data-driven and patient-centric direction. This shift is set to revolutionize the current clinical development approach, especially given the surge in readily available real-world data and the escalating resource demands of clinical trials, all in response to the rapid pace of novel drug discoveries driven by GenAI.
In addition, GenAI algorithms for decision-making would create higher values if substantiated in validations and become operational across all functions and stages of clinical developments ranging from drug discovery and pre-clinical study to clinical trials. Thus, the current clinical development model is shifting toward a GenAI-augmented proactive approach, supported by real-world data for real-time evidence.
The integration of GenAI in clinical trials has been found to shorten the decision lag time, and clinical trials would be more insightful in clinical contexts as the full spectrum of relevant variables would be fully embedded in the GenAI algorithm for greater efficiency and performance improvements, accelerating clinical developments from pre-clinical to clinical stages with substantial cost-saving. Hence, such developments are expected to boost market growth over the forecast period.
Furthermore, the increasing introduction of advanced generative AI tools aimed at expediting clinical trials is poised to boost their adoption, subsequently driving market growth during the forecast period. For instance, in October 2023, H1 unveiled GenosAI, a new generative AI tool integrated into its clinical trial intelligence platform, Trial Landscape. This tool is designed to analyze and address a wide range of complex inquiries. Trial Landscape empowers sponsors to identify new sites, identify centers of excellence, and select investigators with diverse patient backgrounds. It also aids in assessing site and principal investigator recruitment strategies and evaluating competition across various trials.
Moreover, the emergence of innovative generative AI models for drug development is set to fuel market growth in the coming years. For instance, in March 2024, a collaboration between Stanford Medicine and McMaster University developed SyntheMol, an AI model that generates synthesis recipes for chemists. This model targets the creation of six novel drugs designed to combat resistant strains of Acinetobacter baumannii in laboratory settings. Such advancements are likely to motivate companies to further innovate in generative AI models for drug discovery, thereby accelerating market growth.
Therefore, with technological advancements, new product launches by key players, and the development of novel GenAI models by researchers, the market is expected to grow over the forecast period.
