Multimodal Artificial Intelligence for Disease Detection and Clinical Decision Support Using Medical Imaging, Electronic Health Records, and Clinical Data

DOI

https://www.doi.org/10.59256/indjcst.20260501C014

Conference

International Conference on Cyber and AI Security

Organised By

Madanapalle Institute of Technology & Science (MITS)

Conference Date

9 & 10 March 2026

Pages

85-91

Article Type

Research Article

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Artificial intelligence systems demonstrate their advanced capabilities in healthcare applications through their recent development of multimodal techniques which enable better diagnostic assessment and clinical decision-making processes. The system employs diverse statistics assets which encompass clinical imaging and electronic fitness statistics and physiological indicators and scientific documentation to create a complete model that correctly portrays the complex nature of scientific conditions. The field has developed several multimodal artificial intelligence systems for medical applications although research studies have not yet established a comprehensive framework that shows their complete operational capabilities. The research review presents an extensive assessment of all machine learning and deep learning multimodal systems which exist today while showing their different methods of data synthesis and their effectiveness in diagnostic assessments. The research study begins with a presentation of accessible datasets to the study team which will analyze the medical data preprocessing methods that establish uniformity across different types of medical information. The research study proceeds to define major fusion techniques which enable the combination of distinct information sources from various modalities and it presents typical model designs which extend from hybrid systems to transformer-based vision-language models and optimization-based educational resources systems. The current research studies show multiple operational problems which need to be solved. Our research shows that using multiple data sources for diagnosis results in better accuracy and system durability and capability to apply findings to new situations than using single data sources. The research presents an integrated framework which shows current research in the field while it creates new research paths to develop multimodal diagnostic system that medical professionals can use with practical understanding and operational efficiency.