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
Bibliographic record
Abstract
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 1,000 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 triage 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 trade-off 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. .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".