Comprehensive cancer rehabilitation programs that incorporate evidence-based physical activity (PA) and exercise are currently recommended as a standard component of cancer care. However, reach and access to cancer rehabilitation is fragmented due to patient-, healthcare provider-, and organizational-level barriers. Artificial intelligence (AI), including both generative AI (e.g. chatbots that use large language models) and predictive AI techniques (e.g. forecasting future outcomes), holds potential to scale cancer rehabilitation at a relatively low cost, while filling critical gaps in care. The purpose of this narrative review is to introduce the concept of AI-supported cancer rehabilitation and synthesize emerging evidence focused on PA and structured exercise interventions. We found that existing research on the role of AI to support cancer rehabilitation is in its early stages. To-date, AI has been used to support cancer rehabilitation to: 1) screen and identify patients in need of rehabilitation; 2) predict exercise training responses and outcomes; 3) enhance patient engagement and behavior change (e.g., through feedback, coaching, or conversational agents); and 4) support precision exercise prescription. Early AI-supported interventions have demonstrated modest improvements in PA levels, although evidence remains limited. We outline priority research questions and summarize key challenges relating to the ethics, equity, and implementation of AI-tools to support cancer rehabilitation. By leveraging multidisciplinary collaboration and patient-engagement, ethically and effectively designed AI-supported cancer rehabilitation tools have the potential to overcome barriers to cancer rehabilitation access and delivery, while remaining trustworthy and meaningful to end-users.
Bland et al. (Sat,) studied this question.