Multimodal Artificial Intelligence in Medicine
Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data (e.g., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer - Lippincott Williams & Wilkins
2024-11-01
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| Series: | Kidney360 |
| Online Access: | http://journals.lww.com/10.34067/KID.0000000000000556 |
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| _version_ | 1850071800340283392 |
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| author | Conor S. Judge Finn Krewer Martin J. O'Donnell Lisa Kiely Donal Sexton Graham W. Taylor Joshua August Skorburg Bryan Tripp |
| author_facet | Conor S. Judge Finn Krewer Martin J. O'Donnell Lisa Kiely Donal Sexton Graham W. Taylor Joshua August Skorburg Bryan Tripp |
| author_sort | Conor S. Judge |
| collection | DOAJ |
| description | Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data (e.g., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges. |
| format | Article |
| id | doaj-art-ff568271eb374eddaf3bff56fd2aa499 |
| institution | DOAJ |
| issn | 2641-7650 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wolters Kluwer - Lippincott Williams & Wilkins |
| record_format | Article |
| series | Kidney360 |
| spelling | doaj-art-ff568271eb374eddaf3bff56fd2aa4992025-08-20T02:47:13ZengWolters Kluwer - Lippincott Williams & WilkinsKidney3602641-76502024-11-015111771177910.34067/KID.0000000000000556202411000-00024Multimodal Artificial Intelligence in MedicineConor S. Judge0Finn Krewer1Martin J. O'Donnell2Lisa Kiely3Donal Sexton4Graham W. Taylor5Joshua August Skorburg6Bryan Tripp71 HRB-Clinical Research Facility, University of Galway, Galway, Ireland1 HRB-Clinical Research Facility, University of Galway, Galway, Ireland1 HRB-Clinical Research Facility, University of Galway, Galway, Ireland1 HRB-Clinical Research Facility, University of Galway, Galway, Ireland3 Department of Medicine, Trinity College Dublin, Dublin, Ireland4 University of Guelph, Guelph, Ontario, Canada4 University of Guelph, Guelph, Ontario, Canada6 Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, CanadaTraditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data (e.g., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.http://journals.lww.com/10.34067/KID.0000000000000556 |
| spellingShingle | Conor S. Judge Finn Krewer Martin J. O'Donnell Lisa Kiely Donal Sexton Graham W. Taylor Joshua August Skorburg Bryan Tripp Multimodal Artificial Intelligence in Medicine Kidney360 |
| title | Multimodal Artificial Intelligence in Medicine |
| title_full | Multimodal Artificial Intelligence in Medicine |
| title_fullStr | Multimodal Artificial Intelligence in Medicine |
| title_full_unstemmed | Multimodal Artificial Intelligence in Medicine |
| title_short | Multimodal Artificial Intelligence in Medicine |
| title_sort | multimodal artificial intelligence in medicine |
| url | http://journals.lww.com/10.34067/KID.0000000000000556 |
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