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/ |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
fradi-03-1349830.pdf | Fulltext - Published Version | 176.38 kB | Adobe PDF | View/Open |
This item is protected by original copyright |
A file in this item is licensed under a Creative Commons License
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.