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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.
William Lotter1,2,3, Michael J Hassett3,4,5, Nikolaus Schultz6,7
1Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.
Cancer Discovery
|April 10, 2024
Summary
Artificial intelligence (AI) in oncology is moving into clinical practice. This review covers AI
Area of Science:
- Oncology
- Artificial Intelligence
- Clinical Translation
Background:
- Artificial intelligence (AI) is rapidly advancing in oncology.
- Applications are maturing from research to clinical integration.
Purpose of the Study:
- To review the current state of AI in oncology, focusing on clinical integration.
- To structure AI applications by cancer type and clinical domain.
Main Methods:
- Review of AI applications across common cancers (detection, diagnosis, treatment).
- Analysis of data modalities including imaging, genomics, and medical records.
Main Results:
- AI applications are being integrated into clinical practice for cancer detection, diagnosis, and treatment.
- Diverse data modalities are utilized in AI oncology tools.
Conclusions:
- AI integration in oncology faces challenges but offers evolving solutions.
- Future directions include further clinical translation and addressing implementation hurdles.
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