Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36696
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Unrefereed
Title: Editorial: Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis, and prevention
Author(s): Ali, Hazrat
Shah, Zubair
Alam, Tanvir
Wijayatunga, Priyantha
Elyan, Eyad
Contact Email: ali.hazrat@stir.ac.uk
Keywords: electronic health records
healthcare
medical imaging
radiology
multimodal artificial intelligence
vision transformers
Issue Date: 10-Jan-2024
Date Deposited: 13-Dec-2024
Citation: Ali H, Shah Z, Alam T, Wijayatunga P & Elyan E (2024) Editorial: Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis, and prevention. <i>Frontiers in Radiology</i>, 3. https://doi.org/10.3389/fradi.2023.1349830
Abstract: First paragraph: Artificial Intelligence (AI) has gained huge attention in computer-aided decision-making in the healthcare domain. Many novel AI methods have been developed for disease diagnosis and prognosis which may support in the prevention of disease. Most diseases can be cured early and managed better if timely diagnosis is made. The AI models can aid clinical diagnosis; thus, they make the processes more efficient by reducing the workload of physicians, nurses, radiologists, and others. However, the majority of AI methods rely on the use of single-modality data. For example, brain tumor detection uses brain MRI, skin lesion detection uses skin pathology images, and lung cancer detection uses lung CT or x-ray imaging (1). Single-modality AI models lack the much-needed integration of complex features available from different modality data, such as electronic health records (EHR), unstructured clinical notes, and different medical imaging modalities– otherwise form the backbone of clinical decision-making.
DOI Link: 10.3389/fradi.2023.1349830
Rights: © 2024 Ali, Shah, Alam, Wijayatunga and Elyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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