Artificial Intelligence for the Future Radiology Diagnostic Service
Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-01-01
|
| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2020.614258/full |
| _version_ | 1829514571200593920 |
|---|---|
| author | Seong K. Mun Kenneth H. Wong Shih-Chung B. Lo Yanni Li Shijir Bayarsaikhan |
| author_facet | Seong K. Mun Kenneth H. Wong Shih-Chung B. Lo Yanni Li Shijir Bayarsaikhan |
| author_sort | Seong K. Mun |
| collection | DOAJ |
| description | Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service. |
| first_indexed | 2024-12-16T13:19:59Z |
| format | Article |
| id | doaj.art-1773a75469114373aa20dec347f1605e |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-16T13:19:59Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-1773a75469114373aa20dec347f1605e2022-12-21T22:30:22ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-01-01710.3389/fmolb.2020.614258614258Artificial Intelligence for the Future Radiology Diagnostic ServiceSeong K. MunKenneth H. WongShih-Chung B. LoYanni LiShijir BayarsaikhanRadiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.https://www.frontiersin.org/articles/10.3389/fmolb.2020.614258/fullartificial intelligenceradiologyCNNproductivityintegrated diagnosticsworkflow |
| spellingShingle | Seong K. Mun Kenneth H. Wong Shih-Chung B. Lo Yanni Li Shijir Bayarsaikhan Artificial Intelligence for the Future Radiology Diagnostic Service Frontiers in Molecular Biosciences artificial intelligence radiology CNN productivity integrated diagnostics workflow |
| title | Artificial Intelligence for the Future Radiology Diagnostic Service |
| title_full | Artificial Intelligence for the Future Radiology Diagnostic Service |
| title_fullStr | Artificial Intelligence for the Future Radiology Diagnostic Service |
| title_full_unstemmed | Artificial Intelligence for the Future Radiology Diagnostic Service |
| title_short | Artificial Intelligence for the Future Radiology Diagnostic Service |
| title_sort | artificial intelligence for the future radiology diagnostic service |
| topic | artificial intelligence radiology CNN productivity integrated diagnostics workflow |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2020.614258/full |
| work_keys_str_mv | AT seongkmun artificialintelligenceforthefutureradiologydiagnosticservice AT kennethhwong artificialintelligenceforthefutureradiologydiagnosticservice AT shihchungblo artificialintelligenceforthefutureradiologydiagnosticservice AT yannili artificialintelligenceforthefutureradiologydiagnosticservice AT shijirbayarsaikhan artificialintelligenceforthefutureradiologydiagnosticservice |