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  • Title: Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification.
    Author: Hindy JR, Souaid T, Kovacs CS.
    Journal: Future Microbiol; 2024; 19(15):1283-1292. PubMed ID: 39069960.
    Abstract:
    Aim: Assessing the visual accuracy of two large language models (LLMs) in microbial classification.Materials & methods: GPT-4o and Gemini 1.5 Pro were evaluated in distinguishing Gram-positive from Gram-negative bacteria and classifying them as cocci or bacilli using 80 Gram stain images from a labeled database.Results: GPT-4o achieved 100% accuracy in identifying simultaneously Gram stain and shape for Clostridium perfringens, Pseudomonas aeruginosa and Staphylococcus aureus. Gemini 1.5 Pro showed more variability for similar bacteria (45, 100 and 95%, respectively). Both LLMs failed to identify both Gram stain and bacterial shape for Neisseria gonorrhoeae. Cumulative accuracy plots indicated that GPT-4o consistently performed equally or better in every identification, except for Neisseria gonorrhoeae's shape.Conclusion: These results suggest that these LLMs in their unprimed state are not ready to be implemented in clinical practice and highlight the need for more research with larger datasets to improve LLMs' effectiveness in clinical microbiology. This study looked at how well large language models (LLMs) could identify different types of bacteria using images, without having any specific training in this area beforehand.We tested two LLMs with image analysis capabilities, GPT-4o and Gemini 1.5 Pro. These models were asked to determine whether bacteria were Gram-positive or Gram-negative and whether they were round (cocci) or rod-shaped (bacilli). We used 80 images of four stained bacteria from a labeled database as a reference for this test.GPT-4o was more accurate in identifying both the Gram stain and shape of the bacteria compared with Gemini 1.5 Pro. GPT-4o had excellent accuracy in correctly classifying the Gram stain and bacterial shape of Clostridium perfringens, Pseudomonas aeruginosa and Staphylococcus aureus. Gemini 1.5 Pro had mixed results for these bacteria. However, both models struggled with Neisseria gonorrhoeae, failing to correctly identify its Gram stain and shape.The study shows that while these LLMs have potential, they are not ready to be implemented in clinical practice. More research and larger datasets are needed to improve their accuracy in clinical microbiology.
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