Part 3/10:
The discussion underscores several challenges inherent in integrating AI into healthcare:
Data Privacy and Security: Healthcare data is among the most sensitive personal information. Breaches or misuse can have severe repercussions.
Bias and Fairness: AI models can inadvertently perpetuate biases, especially when trained on unrepresentative datasets. Examples include racial or gender disparities in diagnostics or risk predictions. Wowski references instances where AI models misdiagnosed women or racially biased algorithms, often rooted in training data deficiencies.
Ethical Use: Balancing technological advancements with ethical principles, especially regarding consent and bias mitigation, remains complex.