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* **Data Collection Practices:** This is a big one, guys. The way data is collected is often the root of the problem. Many healthcare datasets are not representative of the diverse populations they serve. This is due to historical disparities in healthcare access, participation in clinical trials, and how medical information is recorded. If the data only reflects a specific demographic group, the AI will learn and perpetuate those biases. For example, clinical trials have historically underrepresented women and minorities, which means that AI models trained on this data might not perform as well on these groups. Another aspect of data collection practices is the use of biased measurement tools or diagnostic criteria. If the tools used to collect data are not validated across different groups, the resulting data will be skewed. It's all about making sure we're collecting the right data, in the right way, from the right people.
