Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.


Journal

Cancer discovery
ISSN: 2159-8290
Titre abrégé: Cancer Discov
Pays: United States
ID NLM: 101561693

Informations de publication

Date de publication:
21 Mar 2024
Historique:
received: 12 10 2023
revised: 29 01 2024
accepted: 28 02 2024
medline: 10 4 2024
pubmed: 10 4 2024
entrez: 10 4 2024
Statut: aheadofprint

Résumé

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.

Identifiants

pubmed: 38597966
pii: 741919
doi: 10.1158/2159-8290.CD-23-1199
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

OF1-OF16

Informations de copyright

©2024 American Association for Cancer Research.

Auteurs

William Lotter (W)

Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.
Harvard Medical School, Boston, Massachusetts.

Michael J Hassett (MJ)

Harvard Medical School, Boston, Massachusetts.
Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Nikolaus Schultz (N)

Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.

Kenneth L Kehl (KL)

Harvard Medical School, Boston, Massachusetts.
Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Eliezer M Van Allen (EM)

Harvard Medical School, Boston, Massachusetts.
Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

Ethan Cerami (E)

Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Classifications MeSH