🤖 AI bias is a complex problem that can arise in healthcare applications. Some of the challenges include:

❇️ Artificial models trained using algorithms that can be biased if said algorithm is designed not acounting for the potential sources of bias or if it's trained on unreliable data.

❇️ AI models trained using data that can also be biased (intentionally or unintentionally), creating predictions or decisions in the same manner and be less accurate.

❇️ Even if the data and algorithms are not biased, human bias can still interject in the use and development of AI models. The people who collect the data, design the algorithms, and interpret the results of AI models may have their own biases.

➡️ There are various potential solutions to address these challenges:

❇️ One solution is different bias-mitigation techniques like data cleaning, algorithm design and human oversight.

❇️ Another is people's awareness and education about AI bias and fairness, helping to ensure that everyone knows about the challenges and how to address them.

🔶 Lastly, AI models trained on data that is as diverse as possible regarding race, gender, ethnicity, age, and other factors, thus helping to reduce the risk.

❇️ These challenges are complex but certainly not insuperable. The objective is to have safe, accurate, non-biased AI models.

🔶 By adressing these and searching for solutions, we can help to ensure that AI is used to improve healthcare for everyone.

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