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Dr. Mohammed Imdad is an Associate Professor in the Department of Medical Surgical Nursing at St. Francis Hospital and College of Nursing, Ajmer, Rajasthan, India. His academic and professional background is associated with medical-surgical nursing and healthcare education. His contribution examines the integration of clinical information and multimodal AI techniques for healthcare applications.
The amalgamation of medical imagery, clinical documentation, and physiological sensor information using multimodal AI signifies a hopeful strategy for enhancing medical diagnosis, patient surveillance, and clinical decision-making assistance. Traditional medical AI systems often evaluate distinct data modalities in isolation, hindering their capacity to integrate the synergistic information found in visual, textual, and physiological formats. This chapter examines the essential principles, computational frameworks, and integration techniques employed to merge medical imaging, clinical language, and sensor-derived data. The chapter culminates by offering a conceptual framework for the amalgamation of medical images, clinical text, and sensor data to facilitate precise, interpretable, and clinically pertinent decision-making with suitable human oversight.