Version 1
: Received: 14 August 2024 / Approved: 15 August 2024 / Online: 20 August 2024 (03:56:15 CEST)
Version 2
: Received: 21 August 2024 / Approved: 21 August 2024 / Online: 22 August 2024 (12:16:55 CEST)
How to cite:
Diao, S.; Huang, D.; Jiang, G. The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging. Preprints2024, 2024081168. https://doi.org/10.20944/preprints202408.1168.v2
Diao, S.; Huang, D.; Jiang, G. The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging. Preprints 2024, 2024081168. https://doi.org/10.20944/preprints202408.1168.v2
Diao, S.; Huang, D.; Jiang, G. The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging. Preprints2024, 2024081168. https://doi.org/10.20944/preprints202408.1168.v2
APA Style
Diao, S., Huang, D., & Jiang, G. (2024). The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging. Preprints. https://doi.org/10.20944/preprints202408.1168.v2
Chicago/Turabian Style
Diao, S., Danyi Huang and Gaozhe Jiang. 2024 "The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging" Preprints. https://doi.org/10.20944/preprints202408.1168.v2
Abstract
This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.
Keywords
radiomics; personalized medicine; clinical decision support system (CDSS); radio-imaging guided health care (RLHC)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.