CARE-LLM-GRAPH: Confidence Aware LLM integrated Multimodal Architecture for clinical Recommendation

Abstract

The growing availability of heterogeneous clinical data has provided new prospects of data-driven clinical decision support, but at the same time, brought about challenges to do with multimodal integration, uncertainty management, and interpretability. Current AI systems in clinical settings are also likely to be based on fixed fusion methods and single-mode thinking, which restricts their resilience and reliability in practice in healthcare settings. In order to overcome these shortcomings, this paper presents a new confidence-aware hybrid design, CARE-LLM-GRAPH, which combines large language models (LLMs) to perform clinical reasoning, multimodal deep learning to analyze medical images, and population-aware graph intelligence to provide cohort-level information. The new framework clearly formulates modality-specific uncertainty and also uses a confidencesensitive adaptive fusion process to combine dynamically the text, visual and graph-based evidence. In addition, the iterative refinement process that is organized by an LLM allows an adaptive reasoning in situations when clinical conditions are ambiguous or incomplete. Most experiments performed out of publicly available multimodal clinical datasets show that CARE-LLM-GRAPH has been demonstrating consistently better discriminative performance, recommendation ranking quality, calibration, and performance under missing data conditions compared to state-of-the-art unimodal and multimodal baselines. The framework also produces interpretable evidence-based explanations, which increase the level of clinical transparency and trust. These findings underscore how CARE-LLMGRAPH can be a valid and explicable clinical decision support framework to serve practical purposes in healthcare environments.