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AI in Clinical Trials: Use Cases and FDA Regulatory Considerations

Updated: Jul 3, 2023

Welcome to our third blog post on RegAffairs Rx! In this edition, we delve into the exciting realm of artificial intelligence (AI) in clinical trials. Artificial Intelligence (AI) has rapidly transformed various industries in recent years, including healthcare. In the pharmaceutical industry, AI is revolutionizing the clinical phase of drug development, making it faster and more efficient. In this article, we will explore the use cases of AI in clinical trials and examine how the FDA regulates this technology.

Patient Recruitment and Selection: Efficient patient recruitment and selection are crucial for the success of clinical trials. AI can analyze large volumes of patient data, such as electronic health records (EHRs), medical records, and genetic information, to identify potential trial participants. By using machine learning algorithms, AI can quickly match eligible patients with the trial's inclusion and exclusion criteria, streamlining the recruitment process.

Companies like Deep 6 AI and Mendel applies natural language processing and machine learning techniques to identify potential candidates for clinical trials. They analyzes structured and unstructured patient data, including electronic health records, to match patients with suitable clinical trials.

Optimizing Clinical Trial Design: AI can analyze historical clinical trial data, scientific literature, and other relevant sources to provide insights that aid in protocol design. By identifying patterns, trends, and relationships within the data, AI can help researchers optimize various aspects of the trial, such as treatment dosages, randomization strategies, sample sizes, and endpoint selection.

Companies like Deep Lens combines AI and advanced imaging technologies to aid in protocol design and patient recruitment for oncology clinical trials. Their platform facilitates rapid identification of eligible patients and provides real-time imaging data analysis.

Generative AI for Synthetic Data: Generative AI techniques can assist in generating synthetic patient data that closely resembles real-world patient characteristics. This synthetic data can help expand the available dataset, improve statistical power, and address issues related to data scarcity or privacy concerns. By simulating a control group using historical data, generative AI enables researchers to compare results between experimental and control groups. primarily focused on patient recruitment, also leverages generative modeling to generate synthetic patient data. This synthetic data can be used to augment real-world data and enhance the understanding of patient populations, optimize trial designs, and improve the efficiency of clinical trials.

Treatment Response Prediction: AI models can provide decision support systems for clinicians involved in clinical trials. By analyzing a wide range of patient data and treatment outcomes, these models can generate insights and predictions regarding treatment response. Clinicians can leverage these predictions to better understand the potential outcomes of different treatment strategies and make evidence-based decisions during the trial.

Owkin develops AI-driven platforms that leverage generative modeling techniques for treatment response prediction. Their models analyze clinical and molecular data to predict patient responses to different treatments. By combining real-world and synthetic data, Owkin aims to enhance treatment outcome predictions and optimize personalized medicine strategies.

Adverse Event Monitoring: AI-powered systems can continuously monitor patient data, including vital signs, lab results, and wearable sensor data, to detect and alert healthcare professionals to potential adverse events. Machine learning algorithms can learn from historical data and recognize patterns that may indicate adverse events, allowing for early intervention and timely treatment modifications.

PhysIQ's AI analytics platform continuously monitors patient data, including vital signs and physiological measurements, to detect deviations from normal patterns and alert healthcare professionals to potential adverse events.

FDA Regulatory Considerations: The FDA plays a crucial role in regulating the use of AI in clinical trials to ensure patient safety and reliable outcomes. The FDA classifies AI algorithms used in clinical trials as Software as a Medical Device (SaMD) and assesses their risk level. Higher-risk AI applications may require pre-market approval through established FDA pathways, such as the 510(k) or Premarket Approval processes. The FDA also provides regulatory guidance for AI-based medical devices and software, emphasizing data integrity, algorithm transparency, validation, and cybersecurity. Developers are expected to undergo rigorous performance evaluation and provide evidence of the algorithm's accuracy, reliability, and clinical relevance. Compliance with FDA regulations is essential for successful regulatory review and approval.


In summary, AI offers numerous use cases in clinical trials, from patient recruitment and trial design optimization to treatment response prediction and adverse event monitoring. The FDA's regulatory oversight ensures that AI technologies in clinical trials meet safety standards, data integrity requirements, and provide reliable outcomes. Developers and clinical trial sponsors must closely follow FDA guidelines and engage in early communication to navigate the regulatory landscape and ensure compliance.

Thank you for being a part of the Reg Affairs Rx community, where knowledge is shared, connections are forged, and regulatory excellence thrives.

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