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Updated: Jun 12, 2025
08:20
In Vitro Model of Human Cutaneous Hypertrophic Scarring using Macromolecular Crowding
Published on: May 1, 2020
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The Potential of Chat-Based Artificial Intelligence Models in Differentiating Between Keloid and Hypertrophic Scars:
Makoto Shiraishi1, Shimpei Miyamoto2, Hakuba Takeishi2
1Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. shiraishi-kyf@umin.ac.jp.
Aesthetic Plastic Surgery
|September 25, 2024
Summary
Large Language Models show potential for scar diagnosis, with GPT-4 outperforming Bing Chat in accuracy. However, current AI diagnostic tools require further development for clinical use.
Area of Science:
- Artificial Intelligence in Dermatology
- Medical Image Analysis
- Natural Language Processing in Healthcare
Background:
- Keloids and hypertrophic scars significantly impact patient quality of life.
- Current diagnostic criteria for scars are complex and lead to underdiagnosis.
- There is a need for more accessible scar diagnostic approaches.
Purpose of the Study:
- To evaluate the diagnostic capabilities of Large Language Models (LLMs) for scar conditions.
- To explore the application of AI chatbots like ChatGPT in scar diagnosis.
- To propose a simplified diagnostic method for scars.
Main Methods:
- Five AI chatbots (ChatGPT-4, Bing Chat modes, Bard) were tested.
- Standardized prompts were used to analyze 30 mock scar images.
- Diagnostic accuracy was assessed by querying each chatbot five times.
Main Results:
- ChatGPT-4 demonstrated significantly higher scar diagnostic accuracy (36.0%) compared to Bing Chat (22.0%).
- GPT-4 showed superior specificity for identifying keloids and hypertrophic scars.
- A statistically significant difference (P = 0.027) was observed between GPT-4 and Bing Chat performance.
Conclusions:
- Current LLMs show promise for scar diagnostics but are not yet suitable for clinical application.
- Further advancements in AI are necessary to meet clinical standards for medical diagnostics.
- AI holds potential for improving the diagnosis of scar conditions.
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