Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review
Traumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-1729/15/3/424 |
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| author | Seun Orenuga Philip Jordache Daniel Mirzai Tyler Monteros Ernesto Gonzalez Ahmed Madkoor Rahim Hirani Raj K. Tiwari Mill Etienne |
| author_facet | Seun Orenuga Philip Jordache Daniel Mirzai Tyler Monteros Ernesto Gonzalez Ahmed Madkoor Rahim Hirani Raj K. Tiwari Mill Etienne |
| author_sort | Seun Orenuga |
| collection | DOAJ |
| description | Traumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI’s implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI’s transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients. |
| format | Article |
| id | doaj-art-7c03c0c203674b6aabd953d09f08c595 |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-7c03c0c203674b6aabd953d09f08c5952025-08-20T03:43:27ZengMDPI AGLife2075-17292025-03-0115342410.3390/life15030424Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A ReviewSeun Orenuga0Philip Jordache1Daniel Mirzai2Tyler Monteros3Ernesto Gonzalez4Ahmed Madkoor5Rahim Hirani6Raj K. Tiwari7Mill Etienne8School of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USADepartment of Psychiatry, Mayo Clinic, Phoenix, AZ 85054, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USATraumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI’s implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI’s transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients.https://www.mdpi.com/2075-1729/15/3/424traumatic brain injuryartificial intelligencerehabilitation |
| spellingShingle | Seun Orenuga Philip Jordache Daniel Mirzai Tyler Monteros Ernesto Gonzalez Ahmed Madkoor Rahim Hirani Raj K. Tiwari Mill Etienne Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review Life traumatic brain injury artificial intelligence rehabilitation |
| title | Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review |
| title_full | Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review |
| title_fullStr | Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review |
| title_full_unstemmed | Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review |
| title_short | Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review |
| title_sort | traumatic brain injury and artificial intelligence shaping the future of neurorehabilitation a review |
| topic | traumatic brain injury artificial intelligence rehabilitation |
| url | https://www.mdpi.com/2075-1729/15/3/424 |
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