Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis
BackgroundFor therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow.MethodsThis review and meta-analysi...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1224347/full |
| _version_ | 1797666336693813248 |
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| author | Robert Oehring Nikitha Ramasetti Sharlyn Ng Roland Roller Philippe Thomas Axel Winter Max Maurer Simon Moosburner Nathanael Raschzok Can Kamali Johann Pratschke Christian Benzing Felix Krenzien Felix Krenzien |
| author_facet | Robert Oehring Nikitha Ramasetti Sharlyn Ng Roland Roller Philippe Thomas Axel Winter Max Maurer Simon Moosburner Nathanael Raschzok Can Kamali Johann Pratschke Christian Benzing Felix Krenzien Felix Krenzien |
| author_sort | Robert Oehring |
| collection | DOAJ |
| description | BackgroundFor therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow.MethodsThis review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM.ResultsA total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02.ConclusionCDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness. |
| first_indexed | 2024-03-11T19:58:09Z |
| format | Article |
| id | doaj.art-0e7484fd20634621a67e0b69a7a286cb |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-11T19:58:09Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-0e7484fd20634621a67e0b69a7a286cb2023-10-04T13:57:53ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-10-011310.3389/fonc.2023.12243471224347Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysisRobert Oehring0Nikitha Ramasetti1Sharlyn Ng2Roland Roller3Philippe Thomas4Axel Winter5Max Maurer6Simon Moosburner7Nathanael Raschzok8Can Kamali9Johann Pratschke10Christian Benzing11Felix Krenzien12Felix Krenzien13Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanySpeech and Language Technology Lab, German Research Center for Artificial Intelligence (DFKI), Berlin, GermanySpeech and Language Technology Lab, German Research Center for Artificial Intelligence (DFKI), Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyDepartment of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, GermanyBerlin Institute of Health (BIH), Berlin, GermanyBackgroundFor therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow.MethodsThis review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM.ResultsA total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02.ConclusionCDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.https://www.frontiersin.org/articles/10.3389/fonc.2023.1224347/fullartificial intelligencemultidisciplinary team meetingsclinical decision support systemmachine learningconcordance between CDSS and MDS |
| spellingShingle | Robert Oehring Nikitha Ramasetti Sharlyn Ng Roland Roller Philippe Thomas Axel Winter Max Maurer Simon Moosburner Nathanael Raschzok Can Kamali Johann Pratschke Christian Benzing Felix Krenzien Felix Krenzien Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis Frontiers in Oncology artificial intelligence multidisciplinary team meetings clinical decision support system machine learning concordance between CDSS and MDS |
| title | Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis |
| title_full | Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis |
| title_fullStr | Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis |
| title_full_unstemmed | Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis |
| title_short | Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis |
| title_sort | use and accuracy of decision support systems using artificial intelligence for tumor diseases a systematic review and meta analysis |
| topic | artificial intelligence multidisciplinary team meetings clinical decision support system machine learning concordance between CDSS and MDS |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1224347/full |
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