AI in clinical trials: How AI is optimizing trial design, safety, and drug development

As we move deeper into 2025, the role of AI in clinical trials is set to evolve at an unprecedented pace, becoming more powerful and helpful in enhancing and optimizing clinical trial design. 

AI can significantly accelerate drug development by enhancing various aspects of clinical trial design and helping to predict potential safety and efficacy issues. One way AI can help do this is through optimizing clinical trial design. Since AI algorithms can analyze vast datasets, including electronic health records (EHRs), genomic data, and previous trial outcomes, AI can be used to help identify and select the most suitable patient populations for participation in a clinical trial. In addition, trials can be optimized by utilizing AI to run simulations, looking for potential outcomes of different trial designs, as well as creating adaptive trial designs that modify based on interim results. These optimization techniques can lead to more efficient, lower cost, and ultimately faster clinical trials. 

“By optimizing trial design and predicting safety and efficacy, AI accelerates drug development, reducing costs and timelines while enhancing the likelihood of bringing new treatments to market faster and more safely.” 
Anthony Haywood, Vice President, Clinical Trials Optimization, Inizio Medical 

In addition to optimizing clinical trial conduct and design, AI can be utilized to predict safety and efficacy outcomes. By analyzing preclinical data, historical clinical trial data, and real-world patient outcomes, AI can be used to predict adverse events in clinical trials. AI can also be used to monitor trials for any patterns that might indicate a safety concern, through monitoring of patient data during the trial. In addition, AI can help predict efficacy issues and can be utilized to analyze data from multiple sources (e.g., lab tests, imaging, genetic information) to predict treatment outcomes. This allows researchers to monitor the trial’s progress and adjust quickly to optimize treatment efficacy. AI is also an efficient tool to look for biomarker response to a particular drug and to predict drug-drug interactions. 

Companies need to embrace AI in all stages of development, including R&D, clinical trials, and regulatory approval, to help ensure compliance while maintaining human oversight in critical decision points. Utilizing AI to optimize trial design and help predict safety and efficacy issues can shorten timelines and reduce costs of clinical trials, as well as accelerating time to regulatory approvals. By leveraging data-driven insights, AI enhances both the efficiency and success rate of drug development, bringing new treatments to market more quickly and safely.

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