Multimodal Artificial Intelligence in Healthcare

Dear Colleagues,

Artificial Intelligence (AI) is rapidly transforming healthcare. AI has applications in disease diagnosis, prognosis, and healthcare data analytics. AI can aid physicians and doctors through efficient workload management and in reducing data analysis time. AI can make better-informed decisions through incorporating multimodal healthcare data. However, the majority of AI methods rely on use of single-modality data. Many recent studies have demonstrated that the use of multimodal data tends to enhance the predictive performance of AI models in medical imaging, e.g., through leverage of diverse features in different-modality data. Hence, multimodal AI models, along with early/late data/inference fusion approaches, can utilize complex features from data efficiently, thus resulting in better decisions. Exploring new methods to combine different data types often leads to new protocols and strategies for multimodal data collection, cleaning, pre-processing, and integration.

This Special Issue aims to cover recent advancements in multimodal AI for healthcare applications. Its focus is on new methods and applications of AI in healthcare that incorporate multiple modalities of data such as images, text, electronic healthcare records, etc. This research topic will benefit both the AI and clinical researchers looking for new developments in multimodal AI methods in the medical data domain.

Dr. Hazrat Ali
Dr. Kashif Ahmad
Guest Editors

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