The Most Disruptive Near-Term Use of AI in Cancer Care: Patient Empowerment Through Software Agents

Cancer care often involves making complex medical decisions within a challenging environment: a balkanized medical system of many specialists, information overload and obsolescence, limited time with doctors, and siloed data. New AI tools can enable patients and caregivers to more actively participate in treatment decisions. For example, consider receiving a complex scan report in a patient portal. While today, a patient may face confusion in interpreting a complex diagnostic report, a personalized generative AI agent could help by translating the scan report into language a patient can understand, and even contextualizing it within a patient’s personal health history and clinical evidence/guidelines. By providing an understandable version of the report and the clinical context of their test results, patients and their caregivers can engage more fully in decision-making with their oncology team. As described by Clayton Christensen’s “The Innovator’s Dilemma,” industry incumbents typically do not adopt disruptive technologies for fear of cannibalizing existing revenue streams (such as the case of Kodak and digital photography). This leads incumbents to serve their existing customers with the same value proposition, while ignoring “disruptive innovations” that offer a new value proposition to underserved customers (such as the transistor radio, which enabled teenagers to take their music with them). This theory predicts that institutional health care will focus on adopting AI for incremental operational improvements (e.g., patient scheduling, scan interpretation, claims processing). In this review, we argue that a positive disruptive impact of AI in oncology can come if AI-enabled software agents are used to support patients and caregivers in seeking better outcomes through personalized care. We review existing gaps and challenges that patients face as they go through receiving a cancer diagnosis, selecting a treatment plan, and then living with cancer. We use this process view to map uses of novel AI technologies that can assist patients and caregivers. This allows us to describe how current AI technologies/research and policies need to evolve to support patients and caregivers.