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Benchmarking Zero-Shot Generative Pre-Trained Transformer-Based Multimodal Large Language Models for Pressure Injury
Toshiaki Takahashi1,2, Kengo Miyo2, Nao Tamai1
1Department of Adult Nursing, Graduate School of Medicine, Yokohama City University, Yokohama, Kanagawa, Japan.
Objective:
To benchmark zero-shot generative pre-trained transformer (GPT)-based multimodal large language models (MLLMs) for pressure injury (PI) staging from photographs and quantify the effects of prompt strategy, structured outputs, and clinically meaningful label granularity.
Approach:
We performed a retrospective observational benchmark using 1,091 public, de-identified PI photographs labeled Stage I-IV. In the standardized analysis, all 10 model/prompt conditions were evaluated using a standardized, resume-safe application programming interface pipeline with per-image logging. We evaluated exact four-class staging, three-class staging (I/II/III-IV), skin-break screening (I vs. II-IV), and an advanced-intervention threshold (I-II vs. III-IV). Reporting followed Strengthening the Reporting of Observational Studies in Epidemiology and artificial intelligence/machine learning guidance; metrics included accuracy, Wilson 95% confidence intervals, F1 scores, weighted kappa, and threshold sensitivity/specificity.
Results:
All conditions yielded parsed predictions for all 1,091 images. The best accuracy was 93.77% for skin-break screening (GPT-5.2 structured JavaScript Object Notation [JSON]), 90.28% for the advanced-intervention threshold (GPT-5.1 full prompt, low reasoning), 83.78% for three-class staging (GPT-5.1 full prompt, low reasoning), and 65.08% for exact four-class staging (GPT-5.2 structured JSON). The best advanced-intervention condition achieved sensitivity 96.72%, specificity 83.79%, and negative predictive value 96.19%. Stage IV undercalling remained safety-relevant; GPT-5.2-pro had Stage IV recall 8.79% and false-negative rate 91.21%.
Innovation:
This prompt-transparent benchmark shows how outcome granularity, prompting, and structured outputs affect GPT-based PI staging and adds ordinal, threshold-based, and safety metrics.
Conclusion:
GPT-based MLLMs may support clinician-supervised triage and prioritization, but image-only autonomous exact staging is not clinically ready.[Figure: see text].