Role of artificial intelligence in medical image analysis.
10.1097/CM9.0000000000003824
- Author:
Lu WANG
1
;
Shimin ZHANG
2
;
Nan XU
3
;
Qianqian HE
3
;
Yuming ZHU
3
;
Zhihui CHANG
4
;
Yanan WU
3
;
Huihan WANG
5
;
Shouliang QI
6
;
Lina ZHANG
7
;
Yu SHI
4
;
Xiujuan QU
8
;
Xin ZHOU
2
;
Jiangdian SONG
3
Author Information
1. Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
2. Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
3. School of Health Management, China Medical University, Shenyang, Liaoning 110122, China.
4. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
5. Hematology Department of Shengjing Hospital, China Medical University, Shenyang, Liaoning 110004, China.
6. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110016, China.
7. Department of Radiology, The Fourth Hospital of China Medical University, Shenyang, Liaoning 110032, China.
8. Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Deep learning;
Large language model;
Medical image
- MeSH:
Artificial Intelligence;
Humans;
Image Processing, Computer-Assisted/methods*;
Neural Networks, Computer;
Deep Learning;
Diagnostic Imaging/methods*
- From:
Chinese Medical Journal
2025;138(22):2879-2894
- CountryChina
- Language:English
-
Abstract:
With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
