Artificial intelligence and machine learning are rapidly transforming medical imaging in clinical trials by improving the speed, accuracy, consistency, and efficiency of image analysis. While AI is already widely used in clinical radiology, its role in clinical research is expanding through applications such as automated image segmentation, biomarker quantification, patient eligibility assessment, and disease progression tracking. Medpace Core Labs highlights how AI can reduce variability between reviewers, accelerate patient enrollment, lower trial costs, and streamline workflows while still operating under expert physician oversight. Current applications include automated analysis of liver, kidney, and spleen volumes, pathology scoring, brain imaging assessments, and tumor detection using RECIST criteria. As regulatory frameworks evolve and training datasets improve, AI is expected to play an increasingly important role in delivering faster, more reliable imaging endpoints and advancing the efficiency of clinical trials.

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