Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs
Abstract Plain pelvic radiographs (PXR) remain crucial for initial trauma assessment, yet interpretation challenges persist. While artificial intelligence (AI) shows promise, its practical impact across specialties remains unexplored. We conducted a retrospective image-based, multi-reader multi-case...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01923-5 |
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| author | Yu-San Tee Jen-Fu Huang Yu-Ting Huang Chi-Po Hsu Huan-Wu Chen Chi-Hsun Hsieh Chih-Yuan Fu Chi-Tung Cheng Chien-Hung Liao |
| author_facet | Yu-San Tee Jen-Fu Huang Yu-Ting Huang Chi-Po Hsu Huan-Wu Chen Chi-Hsun Hsieh Chih-Yuan Fu Chi-Tung Cheng Chien-Hung Liao |
| author_sort | Yu-San Tee |
| collection | DOAJ |
| description | Abstract Plain pelvic radiographs (PXR) remain crucial for initial trauma assessment, yet interpretation challenges persist. While artificial intelligence (AI) shows promise, its practical impact across specialties remains unexplored. We conducted a retrospective image-based, multi-reader multi-case (MRMC) study using a standardized, prospectively planned evaluation protocol. A total of 26 physicians (8 radiologists, 10 emergency physicians, 8 trauma surgeons) interpreted 150 PXRs in three sequential sessions: without AI, with AI-alert, and with AI-visual guidance. AI assistance improved overall diagnostic accuracy from 0.870 to 0.940 (p < 0.001) and reduced interpretation time from 22.70 to 9.58 s (p < 0.001). Non-radiologists showed substantial improvements, with emergency physicians demonstrating increases in specificity (26.2%, p = 0.006) and positive predictive value (41.5%, p = 0.006). Trauma surgeons with AI-visual guidance achieved comparable accuracy to unaided radiologists (0.940 vs. 0.920, p = 0.556). Tailored AI assistance effectively bridges the performance gap between radiologists and non-radiologists while reducing reading time. These findings suggest AI integration could enhance clinical workflow efficiency across specialties in trauma care settings. |
| format | Article |
| id | doaj-art-f4ad4f4b4a0e49f5a098cc21b04f6fb8 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-f4ad4f4b4a0e49f5a098cc21b04f6fb82025-08-20T04:03:11ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111010.1038/s41746-025-01923-5Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographsYu-San Tee0Jen-Fu Huang1Yu-Ting Huang2Chi-Po Hsu3Huan-Wu Chen4Chi-Hsun Hsieh5Chih-Yuan Fu6Chi-Tung Cheng7Chien-Hung Liao8Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDivision of Emergency and Critical Care Radiology, Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung UniversityAbstract Plain pelvic radiographs (PXR) remain crucial for initial trauma assessment, yet interpretation challenges persist. While artificial intelligence (AI) shows promise, its practical impact across specialties remains unexplored. We conducted a retrospective image-based, multi-reader multi-case (MRMC) study using a standardized, prospectively planned evaluation protocol. A total of 26 physicians (8 radiologists, 10 emergency physicians, 8 trauma surgeons) interpreted 150 PXRs in three sequential sessions: without AI, with AI-alert, and with AI-visual guidance. AI assistance improved overall diagnostic accuracy from 0.870 to 0.940 (p < 0.001) and reduced interpretation time from 22.70 to 9.58 s (p < 0.001). Non-radiologists showed substantial improvements, with emergency physicians demonstrating increases in specificity (26.2%, p = 0.006) and positive predictive value (41.5%, p = 0.006). Trauma surgeons with AI-visual guidance achieved comparable accuracy to unaided radiologists (0.940 vs. 0.920, p = 0.556). Tailored AI assistance effectively bridges the performance gap between radiologists and non-radiologists while reducing reading time. These findings suggest AI integration could enhance clinical workflow efficiency across specialties in trauma care settings.https://doi.org/10.1038/s41746-025-01923-5 |
| spellingShingle | Yu-San Tee Jen-Fu Huang Yu-Ting Huang Chi-Po Hsu Huan-Wu Chen Chi-Hsun Hsieh Chih-Yuan Fu Chi-Tung Cheng Chien-Hung Liao Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs npj Digital Medicine |
| title | Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs |
| title_full | Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs |
| title_fullStr | Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs |
| title_full_unstemmed | Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs |
| title_short | Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs |
| title_sort | comparative analysis of ai support levels in clinical interpretation of traumatic pelvic radiographs |
| url | https://doi.org/10.1038/s41746-025-01923-5 |
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