The Role of AI in Clinical Trials
Medical imaging is increasingly being used in clinical trials. With the introduction of imaging in the clinical trial environment, AI tools and imaging biomarker computation are consequently introduced in order to reduce times in the radiological reading and add objectivity to the evaluation of new treatment response. In order to integrate artificial intelligence (AI) techniques and imaging biomarker (IB) analysis pipelines in clinical trials and improve quality and accuracy in the conclusions of the study, medical image acquisition should be harmonized and standardized across imaging centers. Since all the imaging biomarkers to be extracted from the images rely on the image quality, attention should be given to the design of the image acquisition protocols followed by the theoretical and technical validation of the site. Site’s validation should be performed by the acquisition of dummy run studies to evaluate equipment performance and by a cross-calibration of the different acquisition equipment involved in the trial, since there is a growing trend to integrate quantitative measures in clinical trials beyond lesion diameter. Artificial intelligence and its implementation through machine learning, and specifically deep learning techniques, bring many benefits to medical image processing by allowing to automatize tasks that in the past were performed manually, like organ, tissue, and structure segmentation. Also, the use of this automatic AI tools minimizes the human interaction, reducing the human-induced variability that may bias the results, decreasing the number of patients needed by the increased statistical power, and therefore accelerating the time-to-market of new molecules.
KeywordsImage standardization Image storage Central reading Core laboratory Treatment response Real-time clinical trial Cross-calibration Imaging biomarkers Automatic segmentation
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