Uses and limitations of artificial intelligence for oncology

Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field. The authors cover emerging use cases of predictive and generative artificial intelligence (AI) in three broad use cases: diagnosis, prognostication, and patient communication. They discuss limitations including lack of explainability, bias, and performance drift and strategies to overcome them.