AI-Augmented Clinical Decision Support Systems Using Multimodal Patient Data

Renato Espinola

Authors

Renato Espinola Clinical Data Scientist, Brazil. Author

Keywords:

Clinical Decision Support, Multimodal Learning, Healthcare AI, EHR Analytics, Medical Imaging, Deep Learning

Abstract

The increasing complexity of patient data, drawn from diverse modalities such as electronic health records (EHRs), imaging, genomics, and biosensors, presents new challenges for clinical decision-making. This paper proposes an AI-augmented Clinical Decision Support System (CDSS) that integrates multimodal patient data using advanced deep learning techniques to enhance diagnostic accuracy and treatment recommendations. The system utilizes a fusion architecture combining natural language processing (NLP), medical imaging analysis, and structured clinical data analytics to support physicians in real-time. Experimental results demonstrate improved diagnostic performance, interpretability, and workflow efficiency compared to unimodal or rule-based systems.

References

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AI-Augmented Clinical Decision Support Systems Using Multimodal Patient Data

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