Supplementary Material for: When jack of all trades is a master of none: Comparing the performance of GPT-4 Omni against specialised neural networks in identifying malignant dermatological lesions from smartphone images and structured clinical data

Introduction: Artificial intelligence (AI) can potentially assist in triaging suspicious skin lesions as malignant or benign. General-purpose multimodal large language models (LLMs), such as GPT-4o, have not been rigorously evaluated for this task. This study assessed GPT-4o’s ability to triage skin lesions and compared its performance to specialised neural networks.

Methods: We evaluated GPT-4o using 1000 random cases from the PAD-UFES-20 dataset with 50 repeated trials. GPT-4o was tested using clinical data-only, image-only, and multimodal inputs. GPT-4o’s performance, consistency, and fairness across different demographic subgroups was evaluated. Its performance metrics were compared against specialised unimodal and multimodal neural networks trained on a separate subset of the PAD-UFES-20 dataset.

Results: GPT-4o exhibited poor diagnostic performance across all modalities, with average balanced accuracies of 0.571, 0.602, and 0.622 for clinical data, image, and multimodal inputs, respectively. Sensitivity was consistently high (>0.95) with the tradeoff of very low specificity. Mean agreement rates were high (>0.90), however Fleiss’ κ indicated only moderate consistency due to a strong bias toward malignant classifications. Fairness evaluations showed poorer discriminative performance in younger patients compared to middle-aged and elderly patients, but no notable differences between different sex and skin tone subgroups. Specialised neural networks significantly outperformed GPT-4o on most pairwise comparisons. Multimodal inputs significantly improved GPT-4o performance over unimodal inputs.

Conclusion: Although GPT-4o consistently triaged skin lesions with high sensitivity, its very low specificity limits clinical utility. Thus, general-purpose LLMs like GPT-4o are currently unsuitable for clinical dermatological diagnostics without significant field-specific developments and validation.