Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Abstract

Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.