AI in Clinical Trials Market Size

AI in Clinical Trials Market Size

AI in Clinical Trials Market size was valued at USD 1.3 billion in 2023 and is estimated to register a CAGR of over 14% between 2024 and 2032. AI technology can analyze vast datasets from biological research, clinical studies, and medical records more quickly and accurately than traditional methods. It reduces the time required for drug discovery and development by identifying potential drug candidates and predicting their effectiveness early in the process.

AI can sift through Electronic Health Records (EHRs) and other data sources to identify potential candidates who meet the specific criteria for a trial. This targeted approach increases recruitment efficiency. For instance, in April 2024, Tempus announced its AI-based platform, which identified eligible candidates for cancer trials 50% faster than traditional methods. This capability enhances the recruitment process, reducing the time to reach trial endpoints.
 

Running clinical trials is an expensive endeavor. AI can help reduce these costs by automating various aspects of the trial process, such as monitoring, data management, and even regulatory compliance. AI's ability to analyze genetic and molecular data allows for the development of personalized treatment plans tailored to individual patients' needs. For instance, in June 2024, Novartis used AI to design personalized treatment regimens for patients in its breast cancer trials. The AI models helped tailor treatments based on genetic profiles, leading to higher response rates and better patient outcomes.
 

The market faces several pitfalls and challenges that can impede its growth. AI algorithms require large volumes of high-quality, well-annotated data to function effectively. However, clinical trial data can be fragmented, inconsistent, and incomplete, leading to potential biases and inaccuracies in AI models. Integrating AI systems with existing clinical trial infrastructure, such as EHRs and clinical data management systems, can be technically challenging and resource intensive. Furthermore, AI models can inadvertently perpetuate existing biases present in the training data. In clinical trials, this can lead to inaccurate results and unequal treatment outcomes across different demographic groups.