The combination of Artificial Intelligence and multimodal medical data in real clinical scenarios

The integration of Artificial Intelligence (AI) with multimodal medical data is an emerging field in biomedical informatics, aiming to enhance the accuracy and efficiency of clinical decision-making. Today’s clinical settings generate a wealth of heterogeneous data—ranging from medical imaging (CT, MRI, PET, and pathological images), textual information (medical records), to phenomics (high-dimensional “omics” profiles). Despite considerable progress in single-modality analysis, combining and interpreting these diverse data types remains a formidable challenge. Recent breakthroughs in AI, particularly with Large Language Models (LLMs) and Distributed Model, have provided technical possibilities for multimodal medical datasets. However, limitations persist, especially in achieving robust and clinically translatable solutions for diagnosis, prognosis, and treatment planning. Open questions revolve around model interpretability, data standardization, privacy, and the reliability of AI-driven tools in real-world settings. Furthermore, critical areas such as the validation of imaging biomarkers, scalable fusion algorithms, and real-world clinical applications call for deeper investigation to bridge the gap between bench and bedside.This Research Topic aims to advance the understanding and practical application of AI in the fusion of multimodal medical information within real clinical scenarios. By collecting cutting-edge research and insightful perspectives, we seek to uncover novel computational approaches, address persistent barriers, and show innovative clinical solutions that harness the synergy between AI techniques and complex medical data. Special emphasis is placed on demonstrating tangible clinical value, improving interpretability, and promoting reproducibility in diverse healthcare environments.To gather further insights into the opportunities and challenges of AI-driven multimodal information fusion in clinical scenario, we welcome articles that explore, but are not limited to, the following themes:o Novel AI techniques for multimodal medical data fusiono Clinical applications of AI for disease diagnosis, prognosis, and treatment monitoringo AI-based clinical applications for medical information management.o AI-based clinical applications to guide therapeutic decisions.o AI-based research applications to identify novel biomarkers and therapeutic targeto Privacy, security, and ethical frameworks for multimodal data integrationo Barriers and solutions to clinical adoption of AI-driven fusion tools Topic editors: Jiehui Jiang, Ruiqing Ni, Yafu Yin, Xumin Hou Source: https://www.frontiersin.org/research-topics/80238/the-combination-of-artificial-intelligence-and-multimodal-medical-data-in-real-clinical-scenarios