The Power of Multimodality: Comparative Analysis of Multimodal Large Language Models, Unimodal ChatGPT-5.0, and Human Clinical Experts on Wound Care Certification Examination (Preprint)

BACKGROUND Background: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal versus unimodal artificial intelligence capabilities against human expertise. Objective: To compare the performance of MLLMs, unimodal ChatGPT-5.0, and human clinical experts on a standardized wound care certification examination. OBJECTIVE Objective: To compare the performance of MLLMs, unimodal ChatGPT-5.0, and human clinical experts on a standardized wound care certification examination. METHODS Methods: This cross-sectional comparative study evaluated three participant groups on a 25-question wound care certification examination spanning four clinical domains (Diagnosis, Treatment, Complication Management, Wound Subtype Knowledge). Participants included three MLLMs (Med-PaLM 2, LLaVA-Med, BioGPT), one unimodal LLM (ChatGPT-5.0), and four human clinical experts (General Surgeon, Wound Care Nurse, two Internal Medicine Physicians). Statistical analyses included one-way ANOVA with Tukey's post-hoc tests and domain-specific Kruskal-Wallis comparisons RESULTS Results: Human experts achieved the highest accuracy (86.0%±9.1%), followed by MLLMs (78.7%±12.2%), while ChatGPT-5.0 achieved 64.0%, failing the 70% certification threshold. Significant overall group differences were observed (F(2,5)=8.42, p=0.018, η²=0.74). MLLMs significantly outperformed ChatGPT-5.0 (difference=14.7 percentage points, p=0.032, Cohen's d=1.38), with the multimodal advantage most pronounced in visually-dependent domains: Diagnosis (81% vs 43%, p=0.008) and Complication Management (72% vs 50%, p=0.034). No multimodal advantage was observed for text-based Wound Subtype Knowledge (both 67%). Med-PaLM 2 achieved 92% accuracy, matching the Wound Care Nurse, while the General Surgeon achieved the highest overall performance (96%). CONCLUSIONS Conclusions: MLLMs demonstrate significant performance advantages over unimodal AI in wound care assessment, particularly for visually-dependent clinical tasks. While human experts with specialized wound care experience maintain overall superiority, top-performing MLLMs approach expert-level accuracy, supporting their potential role as clinical decision-support tools