AI for healthcare: Prediction disease diagnosis using multimodal data

Abstract

Artificial intelligence boxes (AI) have advanced the system to move up the likelihood from the conventional system of disease diagnosis to early, precise analysis and personal prediction. However, certain complex diseases need to be analyses through a multimodal approach, as the medical diagnoses we use are unimodal in nature (e.g., imaging and lab work). Multimodal data will include medical images (X-ray, MRI, and CT scan), EHR, genetic information, and clinical notes. This will provide us with a holistic approach to the health condition of a patient. This study will be aimed at exploring the capabilities of multimodal AI systems to provide us with this. This will be done by reducing the percentage of misdiagnosis cases, facilitating the early diagnosis of a disease, and providing accurate diagnoses. AI-based healthcare systems will also help to a certain extent by reducing the workload on clinicians, as it can help them interpret the information more efficiently and improve the workflow to enable quicker decision-making. Moreover, personalized healthcare plans can be generated according to the SAND boxes for each individual patient.