Convergence of evolving artificial intelligence and machine learning techniques in precision oncology
Abstract The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01471-y |
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| author | Elena Fountzilas Tillman Pearce Mehmet A. Baysal Abhijit Chakraborty Apostolia M. Tsimberidou |
| author_facet | Elena Fountzilas Tillman Pearce Mehmet A. Baysal Abhijit Chakraborty Apostolia M. Tsimberidou |
| author_sort | Elena Fountzilas |
| collection | DOAJ |
| description | Abstract The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models. |
| format | Article |
| id | doaj-art-550c77eac4d84ddcad971896fed5aeff |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-550c77eac4d84ddcad971896fed5aeff2025-02-02T12:43:36ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111910.1038/s41746-025-01471-yConvergence of evolving artificial intelligence and machine learning techniques in precision oncologyElena Fountzilas0Tillman Pearce1Mehmet A. Baysal2Abhijit Chakraborty3Apostolia M. Tsimberidou4Department of Medical Oncology, St Luke’s ClinicTCellCoDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer CenterDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer CenterDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer CenterAbstract The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.https://doi.org/10.1038/s41746-025-01471-y |
| spellingShingle | Elena Fountzilas Tillman Pearce Mehmet A. Baysal Abhijit Chakraborty Apostolia M. Tsimberidou Convergence of evolving artificial intelligence and machine learning techniques in precision oncology npj Digital Medicine |
| title | Convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| title_full | Convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| title_fullStr | Convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| title_full_unstemmed | Convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| title_short | Convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| title_sort | convergence of evolving artificial intelligence and machine learning techniques in precision oncology |
| url | https://doi.org/10.1038/s41746-025-01471-y |
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