Multi-Turn LLM-Based Conversational Agents for Patients with Cancer and Caregivers: A Scoping Review

Large language model (LLM)-based conversational agents are increasingly used in healthcare, yet their capacity to support genuine multi-turn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multi-turn conversational agents designed for cancer patients and informal caregivers remains limited.

This scoping review aimed to map the research landscape of LLM-based multi-turn conversational chatbots developed for cancer patients and informal caregivers, focusing on system design, intervention purposes, evaluation approaches, safety considerations, and transparency of LLM-related components.

This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. Eight databases were searched: PubMed, Embase, Scopus, Web of Science, CINAHL, and PsycINFO were searched for studies published between January 2022 and January 2026, with supplementary searches conducted in IEEE Xplore Digital Library and ACM Digital Library in May 2026. Studies were included if they described LLM-based chatbots designed for cancer patients or informal caregivers that supported multi-turn conversational interaction. Two reviewers independently conducted the study selection and data extraction.

Eight studies met the inclusion criteria. Most studies focused on prototype development, with limited research evaluating clinical outcomes. ChatGPT-based models were the most used LLMs, and retrieval-augmented generation techniques were applied in several studies. Chatbots were primarily designed for emotional support or information provision. Evaluation approaches varied widely, including response quality, psychological outcomes, and user experience. However, no studies evaluated interaction-level characteristics such as conversational continuity or context retention, and only two studies reported any conversational memory mechanism. Reporting on safety risks, mitigation strategies, prompt design, model parameters, and adherence to LLM reporting guidelines was often limited or absent.

This scoping review identified only 8 studies on LLM-based multi-turn conversational chatbots for cancer patients and informal caregivers. The field remains at an early stage characterized by prototype-oriented development, heterogeneous design and evaluation approaches, and inconsistent safety and transparency reporting. Future development should prioritize genuine conversational capability, safety management and transparent reporting.