Hazrat Ali
Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis and prevention.
Ali, Hazrat; Shah, Zubair; Alam, Tanvir; Wijayatunga, Priyantha; Elyan, Eyad
Authors
Abstract
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.
Citation
ALI, H., SHAH, Z., ALAM, T., WIJAYATUNGA, P. and ELYAN, E. 2023. Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis and prevention. Frontiers in radiology [online], 3, article number 1349830. Available from: https://doi.org/10.3389/fradi.2023.1349830
| Journal Article Type | Editorial |
|---|---|
| Acceptance Date | Dec 11, 2023 |
| Online Publication Date | Jan 10, 2024 |
| Publication Date | Dec 31, 2023 |
| Deposit Date | May 7, 2024 |
| Publicly Available Date | May 7, 2024 |
| Journal | Frontiers in radiology |
| Electronic ISSN | 2673-8740 |
| Publisher | Frontiers Media |
| Peer Reviewed | Peer Reviewed |
| Volume | 3 |
| Article Number | 1349830 |
| DOI | https://doi.org/10.3389/fradi.2023.1349830 |
| Keywords | Medical imaging; Radiology; Multimodal artificial intelligence; Electronic health records; Vision transformers; Healthcare |
| Public URL | https://rgu-repository.worktribe.com/output/2224546 |
Files
ALI 2023 Recent advances in multimodal (VOR)
(268 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 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).
You might also like
A multimodel-based screening framework for C-19 using deep learning-inspired data fusion.
(2024)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search