Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation
Abstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and its integration with medicine and radiology, with a particular focus on advancements...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-01-01
|
| Series: | Interdisciplinary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/INMD.20240063 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586400530169856 |
|---|---|
| author | Runqiu Huang Xiaolin Meng Xiaoxuan Zhang Zhendong Luo Lu Cao Qianjin Feng Guolin Ma Di Dong Yang Wang |
| author_facet | Runqiu Huang Xiaolin Meng Xiaoxuan Zhang Zhendong Luo Lu Cao Qianjin Feng Guolin Ma Di Dong Yang Wang |
| author_sort | Runqiu Huang |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and its integration with medicine and radiology, with a particular focus on advancements addressing major diseases that significantly threaten human health. Temporally, the advent of foundational model architectures, combined with the underlying drivers of AI development, is accelerating the progress of AI interventions and their practical applications. Spatially, the discussion explores the potential of evolving AI methodologies to strengthen interdisciplinary applications within medicine, emphasizing the integration of AI with the four critical points of the imaging process, as well as its application in disease management, including the emergence of commercial AI products. Additionally, the current utilization of deep learning is reviewed, and future advancements through multimodal foundation models and Generative Pre‐trained Transformer are anticipated. |
| format | Article |
| id | doaj-art-386b126d0fed4b5dae0d70c2a70973a4 |
| institution | Kabale University |
| issn | 2832-6245 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Interdisciplinary Medicine |
| spelling | doaj-art-386b126d0fed4b5dae0d70c2a70973a42025-01-25T17:57:32ZengWiley-VCHInterdisciplinary Medicine2832-62452025-01-0131n/an/a10.1002/INMD.20240063Artificial intelligence‐driven change redefining radiology through interdisciplinary innovationRunqiu Huang0Xiaolin Meng1Xiaoxuan Zhang2Zhendong Luo3Lu Cao4Qianjin Feng5Guolin Ma6Di Dong7Yang Wang8Department of Radiology Zhujiang Hospital of Southern Medical University Guangzhou ChinaSchool of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai ChinaSchool of Biomedical Engineering Southern Medical University Guangzhou ChinaDepartment of Radiology University of Hong Kong‐Shenzhen Hospital Shenzhen ChinaEngineering School Santa Clara University Santa Clara California USASchool of Biomedical Engineering Southern Medical University Guangzhou ChinaThe Department of Radiology China‐Japan Friendship Hospital Beijing ChinaSchool of Artificial Intelligence University of Chinese Academy of Sciences Beijing ChinaDepartment of Radiology Zhujiang Hospital of Southern Medical University Guangzhou ChinaAbstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and its integration with medicine and radiology, with a particular focus on advancements addressing major diseases that significantly threaten human health. Temporally, the advent of foundational model architectures, combined with the underlying drivers of AI development, is accelerating the progress of AI interventions and their practical applications. Spatially, the discussion explores the potential of evolving AI methodologies to strengthen interdisciplinary applications within medicine, emphasizing the integration of AI with the four critical points of the imaging process, as well as its application in disease management, including the emergence of commercial AI products. Additionally, the current utilization of deep learning is reviewed, and future advancements through multimodal foundation models and Generative Pre‐trained Transformer are anticipated.https://doi.org/10.1002/INMD.20240063artificial intelligencedeep learninginterdisciplinary applicationradiology |
| spellingShingle | Runqiu Huang Xiaolin Meng Xiaoxuan Zhang Zhendong Luo Lu Cao Qianjin Feng Guolin Ma Di Dong Yang Wang Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation Interdisciplinary Medicine artificial intelligence deep learning interdisciplinary application radiology |
| title | Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation |
| title_full | Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation |
| title_fullStr | Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation |
| title_full_unstemmed | Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation |
| title_short | Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation |
| title_sort | artificial intelligence driven change redefining radiology through interdisciplinary innovation |
| topic | artificial intelligence deep learning interdisciplinary application radiology |
| url | https://doi.org/10.1002/INMD.20240063 |
| work_keys_str_mv | AT runqiuhuang artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT xiaolinmeng artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT xiaoxuanzhang artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT zhendongluo artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT lucao artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT qianjinfeng artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT guolinma artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT didong artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation AT yangwang artificialintelligencedrivenchangeredefiningradiologythroughinterdisciplinaryinnovation |