LLM-Based Support for Diabetes Diagnosis: Opportunities, Scenarios, and Challenges with GPT-5

2025-09-25

AI Summary

Early diabetes diagnosis remains challenging due to nonspecific symptoms, borderline laboratory values, and physiological complexities in pregnancy. To address this, we developed the first GPT-5–based clinical decision support system aligned with the 2025 American Diabetes Association (ADA) guidelines, designed for multiple clinical scenarios—including symptom recognition, laboratory result interpretation, and gestational diabetes screening. The system concurrently generates structured JSON summaries and interpretable natural language reports, incorporating explicit clinical reasoning chains and patient-friendly explanations. We introduce a data-informed synthetic case evaluation framework, integrating real-world epidemiological features from NHANES and the Pima Indians Diabetes Dataset, and rigorously assess model consistency within a simulated clinical environment. Experimental results demonstrate high concordance with ADA guidelines across five key diagnostic scenarios (Cohen’s κ > 0.92), balancing clinical validity and patient comprehensibility. This work establishes a novel paradigm for explainable, evidence-based large language model deployment in clinical practice, coupled with standardized, real-data–informed evaluation protocols.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Diabetes mellitus is a major global health challenge, affecting over half a billion adults worldwide with prevalence projected to rise. Although the American Diabetes Association (ADA) provides clear diagnostic thresholds, early recognition remains difficult due to vague symptoms, borderline laboratory values, gestational complexity, and the demands of long-term monitoring. Advances in large language models (LLMs) offer opportunities to enhance decision support through structured, interpretable, and patient-friendly outputs. This study evaluates GPT-5, the latest generative pre-trained transformer, using a simulation framework built entirely on synthetic cases aligned with ADA Standards of Care 2025 and inspired by public datasets including NHANES, Pima Indians, EyePACS, and MIMIC-IV. Five representative scenarios were tested: symptom recognition, laboratory interpretation, gestational diabetes screening, remote monitoring, and multimodal complication detection. For each, GPT-5 classified cases, generated clinical rationales, produced patient explanations, and output structured JSON summaries. Results showed strong alignment with ADA-defined criteria, suggesting GPT-5 may function as a dual-purpose tool for clinicians and patients, while underscoring the importance of reproducible evaluation frameworks for responsibly assessing LLMs in healthcare.

Problem

• Improving early diabetes detection through symptom recognition
• Enhancing interpretation of borderline laboratory diagnostic values
• Supporting gestational diabetes screening and complication monitoring

Innovation

• GPT-5 classifies diabetes cases using ADA criteria
• Generates clinical rationales and patient-friendly explanations
• Outputs structured JSON summaries for healthcare monitoring