Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges
Acute respiratory distress syndrome（ARDS）is a highly heterogeneous critical illness with high morbidity and mortality. Early identification and risk assessment are crucial to improving patient prognosis. Current artificial intelligence（AI）technology，especially machine learning（ML）models，have shown s...
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The Editorial Department of Chinese Journal of Clinical Research
2025-08-01
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| Series: | Zhongguo linchuang yanjiu |
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| Online Access: | http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250801 |
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| author | MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan |
| author_facet | MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan |
| author_sort | MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan |
| collection | DOAJ |
| description | Acute respiratory distress syndrome（ARDS）is a highly heterogeneous critical illness with high morbidity and mortality. Early identification and risk assessment are crucial to improving patient prognosis. Current artificial intelligence（AI）technology，especially machine learning（ML）models，have shown significant potential in the early
diagnosis，risk stratification and personalized management of ARDS. Compared with traditional scoring systems，AI models perform well in predicting mortality and optimizing clinical decision ⁃ making，especially through multimodal data fusion，which can significantly improve the prediction accuracy of the models. However，the lack of interpretability，limited clinical applicability and data privacy of AI models are still the main challenges restricting clinical application.Future research should focus on improving model transparency，optimizing clinical integration and solving ethical issues to promote the further development of AI⁃enabled ARDS precision medicine. |
| format | Article |
| id | doaj-art-173636ec28db4b2199a69cd7419a651e |
| institution | Kabale University |
| issn | 1674-8182 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | The Editorial Department of Chinese Journal of Clinical Research |
| record_format | Article |
| series | Zhongguo linchuang yanjiu |
| spelling | doaj-art-173636ec28db4b2199a69cd7419a651e2025-08-25T01:30:57ZzhoThe Editorial Department of Chinese Journal of Clinical ResearchZhongguo linchuang yanjiu1674-81822025-08-013881141114410.13429/j.cnki.cjcr.2025.08.001Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challengesMENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan0*Department of Critical Care Medicine，The First Affiliated Hospital of Harbin Medical University，Harbin，Heilongjiang 150000，ChinaAcute respiratory distress syndrome（ARDS）is a highly heterogeneous critical illness with high morbidity and mortality. Early identification and risk assessment are crucial to improving patient prognosis. Current artificial intelligence（AI）technology，especially machine learning（ML）models，have shown significant potential in the early diagnosis，risk stratification and personalized management of ARDS. Compared with traditional scoring systems，AI models perform well in predicting mortality and optimizing clinical decision ⁃ making，especially through multimodal data fusion，which can significantly improve the prediction accuracy of the models. However，the lack of interpretability，limited clinical applicability and data privacy of AI models are still the main challenges restricting clinical application.Future research should focus on improving model transparency，optimizing clinical integration and solving ethical issues to promote the further development of AI⁃enabled ARDS precision medicine.http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250801acute respiratory distress syndromeartificial intelligencemachine learningrisk predictionprecision medicine |
| spellingShingle | MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges Zhongguo linchuang yanjiu acute respiratory distress syndrome artificial intelligence machine learning risk prediction precision medicine |
| title | Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges |
| title_full | Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges |
| title_fullStr | Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges |
| title_full_unstemmed | Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges |
| title_short | Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges |
| title_sort | artificial intelligence prediction models for acute respiratory distress syndrome progress and challenges |
| topic | acute respiratory distress syndrome artificial intelligence machine learning risk prediction precision medicine |
| url | http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250801 |
| work_keys_str_mv | AT mengxianglinxiongyaxinhancigexinzhaomingyan artificialintelligencepredictionmodelsforacuterespiratorydistresssyndromeprogressandchallenges |