Artificial intelligence applications in pediatric oncology diagnosis
Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundament...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Open Exploration Publishing Inc.
2023-02-01
|
| Series: | Exploration of Targeted Anti-tumor Therapy |
| Subjects: | |
| Online Access: | https://www.explorationpub.com/Journals/etat/Article/1002127 |
| _version_ | 1811161978631094272 |
|---|---|
| author | Yuhan Yang Yimao Zhang Yuan Li |
| author_facet | Yuhan Yang Yimao Zhang Yuan Li |
| author_sort | Yuhan Yang |
| collection | DOAJ |
| description | Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature. |
| first_indexed | 2024-04-10T06:22:38Z |
| format | Article |
| id | doaj.art-479ce67f4324431a964db9ff678002e7 |
| institution | Directory Open Access Journal |
| issn | 2692-3114 |
| language | English |
| last_indexed | 2024-04-10T06:22:38Z |
| publishDate | 2023-02-01 |
| publisher | Open Exploration Publishing Inc. |
| record_format | Article |
| series | Exploration of Targeted Anti-tumor Therapy |
| spelling | doaj.art-479ce67f4324431a964db9ff678002e72023-03-02T02:13:15ZengOpen Exploration Publishing Inc.Exploration of Targeted Anti-tumor Therapy2692-31142023-02-014115716910.37349/etat.2023.00127Artificial intelligence applications in pediatric oncology diagnosisYuhan Yang0https://orcid.org/0000-0002-4405-5711Yimao Zhang1Yuan Li2Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, ChinaDepartment of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, ChinaLaboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, ChinaArtificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.https://www.explorationpub.com/Journals/etat/Article/1002127pediatric oncologyartificial intelligencecancer diagnosismachine learningdeep learning |
| spellingShingle | Yuhan Yang Yimao Zhang Yuan Li Artificial intelligence applications in pediatric oncology diagnosis Exploration of Targeted Anti-tumor Therapy pediatric oncology artificial intelligence cancer diagnosis machine learning deep learning |
| title | Artificial intelligence applications in pediatric oncology diagnosis |
| title_full | Artificial intelligence applications in pediatric oncology diagnosis |
| title_fullStr | Artificial intelligence applications in pediatric oncology diagnosis |
| title_full_unstemmed | Artificial intelligence applications in pediatric oncology diagnosis |
| title_short | Artificial intelligence applications in pediatric oncology diagnosis |
| title_sort | artificial intelligence applications in pediatric oncology diagnosis |
| topic | pediatric oncology artificial intelligence cancer diagnosis machine learning deep learning |
| url | https://www.explorationpub.com/Journals/etat/Article/1002127 |
| work_keys_str_mv | AT yuhanyang artificialintelligenceapplicationsinpediatriconcologydiagnosis AT yimaozhang artificialintelligenceapplicationsinpediatriconcologydiagnosis AT yuanli artificialintelligenceapplicationsinpediatriconcologydiagnosis |