Comprehensive Summary
Goudrar et al present a review of evaluating existing research on large language data model (LLM) chatbots for patient education about kidney disease. The authors selected 17 relevant studies and found that most LLM‑based answers exceeded the recommended 6-8th grade reading levels, showed variable quality, and often lacked strong understandability or actionability. But, effective prompting could potentially help meet those targets. Although some studies reported accuracy comparable to traditional sources of information, differences from clinical guidelines were noted and patient perception, while generally favorable, was mixed. The authors conclude that LLM chatbots hold promise for educating KSD patients and reducing physician workload, but significant shortcomings currently exist that must be addressed before deployment in practice.
Outcomes and Implications
The implications for the medical community are that, while LLM chatbots could potentially enhance access, consistency of information, and complement physician counselling for patients with KDS, they are not yet ready for independent use in guiding patients. The limitations in readability, guideline‑adherence, understandability and actionability mean that there is a risk of misinformation, incomplete comprehension or inappropriate patient decision‑making if these tools are used without guidance. For healthcare providers, integrating LLM chatbots may provide an extra support resource, but it should be paired with human review, tailored prompting, and ideally the development of specialty‑specific chatbot tools trained on validated urology content. In the future, improving these tools could reduce workload, support shared decision‑making, and standardize educational quality for KSD patients. However it must be implemented with caution to safeguard patient outcomes.