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Research Article: Evaluating LLMs in non-metastatic melanoma care: a comparative analysis

Date Published: 2026-09-07

Abstract:
Malignant melanoma is an aggressive skin cancer with rising incidence. Accurate early staging and standardized treatment are crucial for prognosis. This study evaluates seven large language models (LLMs)-including GPT-5.2, Gemini-3.1, and medically enhanced models-in assisting non-metastatic melanoma management amid clinical complexities. Employing a prospective, simulated expert-blinded design, 59 virtual cases across TNM stages, ages, and comorbidities were assessed. Multiple senior oncologists independently evaluated model outputs using a 6-point Likert scale for staging accuracy, treatment rationality, and protocol standardization. GPT-5.2 (5.56?±?1.12) and Gemini-3.1 (5.25?±?1.3) achieved the highest staging accuracy, while AntAngelMed performed worst (2.93?±?1.67). Performance declined significantly in complex Stage III cases. GPT-5.2 and Gemini-3.1 also led in treatment rationality, showing stability, whereas model performances converged in early stages but diverged in advanced ones. Gemini-3.1 excelled in protocol standardization (5.17?±?0.57), though some models posed risks like insufficient surgical margin recommendations. Leading LLMs demonstrate potential for high-quality melanoma management but exhibit inconsistent performance influenced by architecture and case complexity, with reduced reliability in advanced stages. Future tools require risk-stratified guidelines and real-world validation to improve patient outcomes.

Introduction:
Malignant melanoma is an aggressive skin cancer with rising incidence. Accurate early staging and standardized treatment are crucial for prognosis. This study evaluates seven large language models (LLMs)-including GPT-5.2, Gemini-3.1, and medically enhanced models-in assisting non-metastatic melanoma management amid clinical complexities.

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