Evaluation of Advanced Artificial Intelligence Algorithms' Diagnostic Efficacy in Acute Ischemic Stroke: A Comparative Analysis of ChatGPT-4o and Claude 3.5 Sonnet Models - Koyun, Mustafa; Taskent, Ismail (MDPI AG, 2024)
Background/Objectives: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide, with early and accurate diagnosis being critical for timely intervention and improved patient outcomes. This retrospective study aimed to assess the diagnostic performance of two advanced artificial intelligence (AI) models, Chat Generative Pre-trained Transformer (ChatGPT-4o) and Claude 3.5 Sonnet, in identifying AIS from diffusion-weighted imaging (DWI). Methods: The DWI images of a total of 110 cases (AIS group: n=55, healthy controls: n=55) were provided to the AI models via standardized prompts. Their responses were compared to the gold-standard evaluations by radiologists, and performance metrics, including sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy, and inter-model agreement levels, were calculated. Results: Both models exhibited high sensitivity for AIS detection (ChatGPT-4o: 100%, Claude 3.5 Sonnet: 94.5%). However, ChatGPT-4o demonstrated significantly lower specificity (3.6%) compared to Claude 3.5 Sonnet (74.5%). Agreement with radiologists was poor for ChatGPT-4o (κ=0.036) but good for Claude 3.5 Sonnet (κ=0.691). In terms of hemispheric localization accuracy, Claude 3.5 Sonnet (67.2%) outperformed ChatGPT-4o (32.7%). Similarly, for specific AIS localization, Claude 3.5 Sonnet (30.9%) showed greater accuracy than ChatGPT-4o (7.2%), with these differences being statistically significant (p<0.05). Conclusions: This study highlights the superior diagnostic performance of Claude 3.5 Sonnet compared to ChatGPT-4o in identifying AIS from DWI. Despite its advantages, both models demonstrated notable limitations in accuracy, emphasizing the need for further development before achieving full clinical applicability. These findings underline the potential of AI tools in radiological diagnostics while acknowledging their current limitations.
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