When AI Meets Real-World Endoscopy: A Match Made in Heaven or Hell?

AI in endoscopy as a student learning from textbooks. For AI, learning from clean, consistent textbook images is like a breeze. Now, picture the endoscopy clinics where these AI systems are used. These clinics don't always have the same image settings. This difference can cause AI to miss important things like polyps or tumors. AI might not spot these diseases correctly when images are too bright or too dark. This can cause some serious issues. A study showed that AI systems had a wide range of accuracy depending on how images were enhanced. The AI that was trained on bright images might miss polyps in dark images. This makes the AI less reliable in real-world settings. But there's good news. Researchers found a way to make AI systems more reliable. They used a technique called image enhancement-based data augmentation. This means they taught the AI to recognize and handle different image enhancements. This made the AI systems more reliable, no matter how the images were altered. It's important to think critically about AI in endoscopy. AI has the potential to revolutionize early disease detection. But it's not enough for AI to work well in perfect lab conditions. They need to be reliable in real-world clinics. Consider this: Are all endoscopy units using the same image enhancement settings? How can we ensure that AI systems are reliable across different clinics? What other factors might affect AI performance in endoscopy? These are important questions to think about. There are some general things to know about AI in endoscopy. It's like a student learning from textbooks. If the textbooks are all the same, the student does well. But if the textbooks are all different, the student might struggle. The same goes for AI - it needs consistent, varied training to perform well in the real world. Think about the future of AI in endoscopy. It's exciting to imagine AI systems that can accurately detect diseases in real-world settings. But it's also important to consider the challenges and questions that come with it. The more we think about these issues, the better we can prepare for the future of AI in endoscopy.