Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation

Artificial intelligence (AI) is increasingly integrated into oncology across clinical care, research workflows, and translational implementation. This chapter provides a practical overview of these domains, emphasizing opportunities and limitations relevant to real-world oncology practice. In clinical care, AI applications include ambient documentation systems, decision support tools, and remote monitoring platforms. These technologies may reduce administrative burden, assist with evidence synthesis and guideline navigation, and enable longitudinal assessment of symptoms and functional status through wearable and patient-reported data. Emerging tools such as large language models also support patient communication through education, translation, and symptom triage. However, across these applications, performance remains dependent on data quality, validation, and appropriate clinical oversight. In research and scholarship, AI is increasingly used to support grant development, study design, literature review, and manuscript preparation. These tools may improve efficiency, enhance clarity, and assist in identifying research gaps or methodological approaches. At the same time, concerns regarding accuracy, hallucinated content, data privacy, and ethical responsibility require careful oversight. The responsibility for hypothesis generation, methodological rigor, and scientific integrity remains with the investigator. From a translational perspective, a persistent gap exists between model development and clinical deployment. Differences in data quality, infrastructure, and patient populations may limit generalizability, while workflow integration, clinician readiness, and bias mitigation remain critical challenges. Collectively, AI has the potential to enhance oncology care and research, but its impact depends on rigorous validation, equitable implementation, and sustained human oversight.