Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis

Introduction

Artificial intelligence (AI) has shown great promise in various medical fields, including dermatology [1]. The emergence of large language models (LLMs), such as ChatGPT, has demonstrated their ability to generate human-like responses and assist in clinical decision-making [2]. With the introduction of image analysis capabilities in LLMs [3], their application in dermatological diagnostics has attracted significant interest. Claude 3 Opus, an advanced conversational AI model, has shown promising performance in various natural language processing tasks [4]. This study aims to fill that gap by comparing Claude 3 Opus and ChatGPT with GPT-4. Despite the potential of AI in health care, encompassing diagnosis, treatment, and public health initiatives, these technologies are largely underused in clinical practice [5]. Moreover, the use of AI in health care raises important legal and ethical considerations, particularly for “high-risk” applications such as medical diagnosis [6]. In this context, comparing the diagnostic performance of Claude 3 Opus and ChatGPT can provide valuable insights into their strengths and limitations, guiding the selection and optimization of AI-assisted diagnostic tools in dermatology while taking into account the regulatory landscape.

Methods

We randomly selected 100 dermoscopic images (50 malignant melanomas, 50 benign nevi) from the International Skin Imaging Collaboration (ISIC) archive [7] using a computer-generated randomization process to avoid selection bias. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, which ensures a diverse and representative sample. Images were included if they were dermoscopic images of melanocytic lesions with histopathologically confirmed diagnoses. Each image was presented to Claude 3 Opus and ChatGPT. The models were given the same prompt, instructing them to provide the top 3 differential diagnoses for each image, ranked by likelihood. The exact prompt was “Please provide the top 3 differential diagnoses for this dermoscopic image, ranked by likelihood. Focus on distinguishing between melanoma and benign nevi.” The models’ responses were recorded for analysis (Figure 1A, B). We assessed primary diagnosis accuracy, accuracy of the top 3 differential diagnoses, and malignancy discrimination ability. The McNemar test was used to compare the models' performance.

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Results

For the primary diagnosis, Claude 3 Opus achieved 54.9% sensitivity (95% CI 44.08%-65.37%), 57.14% specificity (95% CI 46.31%-67.46%), and 56% accuracy (95% CI 46.22%-65.42%), while ChatGPT demonstrated 56.86% sensitivity (95% CI 45.99%-67.21%), 38.78% specificity (95% CI 28.77%-49.59%), and 48% accuracy (95% CI 38.37%-57.75%). The McNemar test showed no significant difference between the 2 models (P=.17; Multimedia Appendices 1-2). For the top 3 differential diagnoses, Claude 3 Opus and ChatGPT included the correct diagnosis in 76% (95% CI 66.33%-83.77%) and 78% (95% CI 68.46%-85.45%) of cases, respectively. The McNemar test showed no significant difference (P=.56). In malignancy discrimination, Claude 3 Opus outperformed ChatGPT with 47.06% sensitivity, 81.63% specificity, and 64% accuracy, compared to 45.1%, 42.86%, and 44%, respectively. The McNemar test showed a significant difference (P<.001). Claude 3 Opus had an odds ratio of 3.951 (95% CI 1.685-9.263) in discriminating malignancy, while ChatGPT had an odds ratio of 0.616 (95% CI 0.297-1.278) (Multimedia Appendix 3).

Discussion

Our study demonstrates the potential of LLMs in assisting dermatological diagnosis, while also revealing their current limitations. Claude 3 Opus showed superior performance in discriminating between malignant and benign lesions compared to ChatGPT. However, both models made errors in diagnosing melanoma and nevi. For example, Claude 3 Opus misdiagnosed several melanomas as benign lesions, while ChatGPT had a higher false positive rate, misclassifying many nevi as melanomas. These findings highlight the need for further development and rigorous clinical validation of AI diagnostic tools before their widespread implementation in dermatology practice (Multimedia Appendix 4). Future research should focus on improving the robustness and interpretability of these models through close collaboration between AI researchers, dermatologists, and other health care stakeholders. Moreover, the potential impact of AI in health care extends beyond technical performance, encompassing legal and ethical dimensions. The European Commission has already proposed legislation for “high-risk” AI applications, providing a framework for the safe and responsible use of medical AI [6]. As LLMs and other AI tools continue to advance, it is crucial to proactively address these regulatory aspects to ensure their beneficial integration into clinical practice. In conclusion, while LLMs such as Claude 3 Opus and ChatGPT show promise in assisting dermatological diagnosis, they are not yet capable of replacing human expertise. Continued research, collaborative development, and proactive regulation are essential for realizing the full potential of AI in dermatology while prioritizing patient safety and ethical standards.