Real-time clinical decision support in smart healthcare using a few-shot multimodal explainable natural language processing framework

Natural language processing, or NLP, is becoming increasingly important in smart healthcare systems to facilitate real-time decision assistance. However, performance and confidence are hampered by issues including the lack of interpretability of the model, the limited labelled data for rare diseases, and the separation of modalities (text, speech, and imaging). To provide a precise and understandable diagnosis, we provide FSM-CNLP, a revolutionary Few-Shot Multimodal Clinical NLP system that combines voice transcripts, structured clinical data, and medical imaging. FSM-CNLP uses SHAP-based explanation modules, meta-learning for few-shot adaptation, and transformer-based encoders (BioB- ERT, ViT, Wav2Vec2). Using the MIMIC-III , COVID-CT, and EMRA datasets, we assess FSM-CNLP and find that it performs better than the existing baselines. Our findings show the potential of FSM-CNLP for dependable, real-time clinical applications.