Systems that give intelligent help for medical decision-making are becoming more valuable as artificial intelligence and big data technologies are used more widely in medicine. The multimodal AI big data medical intelligent decision-making system combines and uses multiple data kinds such as photos, text, audio, physiological signals, and so on, demonstrating a high potential for enhancing diagnostic and treatment efficiency and accuracy. The purpose of this research is to investigate the design process and application impact of a medical decision support system that combines multimodal data processing with artificial intelligence technologies. The research initially examined the properties and processing techniques of multimodal data, followed by an overview of the current primary intelligent algorithms and their applications in the medical area. A system based on deep learning and natural language processing methods has been presented for integrating multi-source data and making complete suggestions. Clinical experiments have shown the system’s application usefulness in real-world medical settings, including accuracy, reaction time, and user acceptability. The study’s findings show that the approach may help physicians make more accurate diagnoses and give patients with individualized treatment plans, considerably increasing the quality of medical services.
Huanyu Wang (Tue,) studied this question.