Large Language Models for Diabetes Care Planning, Patient Education and Patient Safety

Abstract

An exploration into the application of large language models in diabetes care as a support tool for clinical decision-making is warranted due to the model’s ability to integrate clinical guidelines, synthesize large volumes of patient data, construct clinical notes, assist in clinical teaching, and provide a rationale for complex management decisions. Diabetes, in particular, presents a complex clinical scenario, as the formulation of management decisions must consider multiple factors, including, but not limited to, multiple targets and risk factors, monitoring data, medication safety, and the patient’s behavioral and psychosocial needs, burdens, and nutritional preferences. This narrative review describes the possibilities of clinician-facing diabetes decision-support systems utilizing large language models and proposes a patient safety architecture and evaluation framework for their clinical assessment. The emphasis is on the safe implementation of these systems, not in an autonomous care setting. Based on the current literature, the systems will most likely assist with preparing clinical visits, reviewing clinical medications, preparing patient summaries for continuous glucose monitoring, planning clinical procedures, and coordinating communication within the clinical team. Despite the above benefits, these systems remain limited, and the constraints of large language models may have serious consequences in the field of diabetes, considering the potential risks of treatment-related adverse events, including hypoglycemia, diabetic ketoacidosis, and other complications based on inequitable provision of care. The safest and most viable near-term solution for large language models in clinical practice is a controlled, evidence-based, and clinician-supervised model that supports the retrieval of clinical guidelines, indicates appropriate levels of uncertainty, identifies gaps in the data, and assumes professional accountability. The focus of future studies should extend beyond validation studies and accuracy benchmarks to evaluating the impact on clinical workflows, equity, patient care and safety, and to monitoring these systems once deployed. Large language models have the potential to support diabetes care; however, they must act to enhance the clinical voice and judgment rather than diminish it.