AI in Oncology: Current Clinical Integration Across Detection, Diagnosis, and Treatment
This Cancer Discovery review structures AI applications by cancer type and clinical domain across the four most common cancers, spanning imaging, genomics and medical record data modalities. The authors focus specifically on clinical translation maturity rather than algorithm development, identifying which applications are moving beyond research into direct patient care. The review provides a practical landscape assessment for diagnostic laboratories evaluating AI tool adoption in oncology workflows.
The original study
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.
- Authors
- Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E
- Journal
- Cancer discovery
- Type
- Journal Article, Review, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
- PMID
- 38597966
Original abstract
UNLABELLED: 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. SIGNIFICANCE: 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.