THE INTEGRATION OF RADIOLOGICAL FINDINGS WITH ELECTRONIC HEALTH RECORDS AND CLINICAL DECISION SUPPORT SYSTEMS

Abstract

Deep learning techniques have the potential to significantly improve healthcare, especially in areas where medical imaging is used for diagnosis, prognosis, and treatment choices. The most advanced deep learning models available today for radiological applications only take into account pixel-value data; they do not take into account data that provides clinical context. In actuality, however, doctors are able to interpret imaging results in the proper clinical context thanks to relevant and accurate non-imaging data based on the clinical history and laboratory data. These results in increased diagnostic accuracy, informed clinical decision making, and better patient outcomes. Medical imaging pixel-based models need to be able to process contextual data from electronic health records (EHR) in addition to pixel data in order to accomplish a comparable aim utilizing deep learning. In this study, we thoroughly analyze the medical data fusion literature produced between 2012 and 2020 and discuss various data fusion strategies that can be used to merge medical imaging with EHR. We performed a thorough search for original research publications using deep learning to fuse multimodality data on PubMed and Scopus. We analyzed 985 studies in all, and we took data out of 17 publications. We present current information, highlight significant findings, and offer implementation advice through this systematic review, which can be used as a reference by researchers who are interested in using multimodal fusion in medical imaging.

Keywords: Deep learning techniques, electronic health records (EHR), medical imaging, review, clinical decision.