Abstract
Background
Claude Opus 4 is a large language model (LLM) that features improved reasoning capabilities
and broader contextual understanding compared to earlier versions. Despite the growing
use of LLM systems for seeking medical information, structured and simulation-based
evaluations of Claude Opus 4's capabilities in diabetes management remain limited,
particularly across domains such as patient education, clinical reasoning, and emotional
support.
Objectives
This study aimed to conduct a baseline evaluation of Claude Opus 4's performance across
key domains of diabetes care (i.e., patient education, clinical reasoning, and emotional
support), and to identify preliminary insights that can inform future, evidence-based
integration strategies.
Methods
A three-step evaluation was conducted: (1) 30 diabetes management questions assessed
using expert endocrinologist evaluation, (2) five fictional diabetes cases evaluated
for clinical decision-making, and (3) emotional support responses assessed for appropriateness
and empathy. Three expert endocrinologists graded responses according to American
Diabetes Association guidelines.
Results
Claude Opus 4 achieved 80% accuracy in general diabetes knowledge, with high response
reproducibility (96.7%), indicating baseline rather than clinically adequate performance.
Clinical case evaluations showed moderate utility (mean expert rating = 4.4/7), while
emotional-support assessments yielded high scores for empathy (6.2/7) and appropriateness
(6.0/7). These findings suggest that although the model demonstrates promising informational
and emotional-support capabilities, its current performance remains insufficient for
autonomous clinical use and should be viewed as preliminary evidence to guide future,
patient-inclusive validation studies.
Conclusion
Although Claude Opus 4 demonstrates preliminary findings suggesting potential applications
in diabetes care, education, and emotional support, this baseline assessment using
fictional cases underscores the need for real-world validation with clinical data
to determine true clinical utility and patient-centered impact. This simulation-based
evaluation also offers practical lessons learned for researchers designing future
LLM assessments, highlighting the need for mixed expert–patient panels, contextual
validation, and person-centered metrics beyond numerical accuracy.
Keywords
artificial intelligence - evaluation studies - diabetes management - health communication
- patient education - clinical decision support - health information technology evaluation
- medical informatics
