Large language models (LLMs) and artificial intelligence systems possess the transformative potential to revolutionize cancer care. However, their integration into oncology presents both extraordinary opportunities and challenges. Clinically, these tools can extract actionable insights from pathology reports, radiology imaging, and genomic sequencing at previously impossible scales. They also enhance the patient-facing dimension by providing accurate informational support and improving patient-clinical trial matching. In translational research, LLMs accelerate informatics analysis for single-cell transcriptomics, spatial omics, and computational pathology, thereby improving support for precision oncology. However, ethical concerns regarding trust, equity, privacy, transparency, non-maleficence, and accountability call for caution. Implementation challenges include hallucination risks, high computational costs, and the potential to exacerbate existing healthcare disparities. Furthermore, developers must navigate a fragmented regulatory landscape consisting of an evolving patchwork of federal, state, and international rules. Responsible implementation requires appropriate skepticism, rigorous validation, and a commitment to patient welfare to navigate this rapidly evolving landscape.
Christine Adams (2026) studied this question.