𝗡𝗲𝘄 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴

𝗡𝗲𝘄 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴

Should you use general AI models or medical AI models?

A recent paper claims general models outperform specialist models in tests. This news caused a debate. The issue is not model power. The issue is how we design tests.

Most medical AI tests use multiple choice questions or short case summaries. General models understand language well. They frame these questions easily. Specialist models train on narrow data. They fail when the test format changes.

General models offer flexibility. They interpret questions from different angles. They handle vague text well. Specialist models rely on specific formats. They lack the ability to generalize because they stay stuck in their training data.

This raises a question. Do these tests measure medical logic? Or do they only measure how well a model memorizes a specific format?

If you build an app to summarize clinical notes, do not start by training a custom model. It costs too much time and money. If general models perform well, focus on your instructions instead. Good prompts and context often work better than building a new model from scratch.

We lack a standard way to measure medical AI. No one agrees on which patient groups or success metrics to use. Until we fix the testing methods, comparing general and specialist models is useless.

Focus on whether a test reflects real clinical work. That matters more than which model wins a benchmark.

Source: https://dev.to/cansubuilds/yeni-ai-modelleri-ve-egitim-48cc

Optional learning community: https://t.me/GyaanSetuAi