To address this challenge, IRENE utilizes bidirectional blocks that incorporate intramodal and intermodal attention, generating a comprehensive representation from both medical images and textual clinical information for the detection of pulmonary disease. It achieves superior performance compared to previous methods that employ data early or late fusion, with an average improvement of 9% and 10%, respectively. To expedite multimodal research, Aliper et al. introduces the HAIM framework which facilitates the development and evaluation of AI systems utilizing multiple types of inputs.