The integration of AI algorithms into healthcare has revolutionized patient care, with the potential to improve diagnosis, treatment, and overall health outcomes. However, this transformative technology also introduces the risk of algorithmic bias, which can have detrimental consequences for patients and perpetuate existing health disparities.

What will be discussed:

- The dangers of algorithmic bias in healthcare, i.e. how bias can infiltrate AI algorithms and lead to unfair treatment of marginalized groups.

- How bias can arise from data collection to algorithm implementation, and how it can distort healthcare decisions and lead to disparities in care for diverse patients.

- Illustrate real-world implications of algorithmic bias in healthcare.

What the audience will learn:

- The significance of algorithmic bias in healthcare and its potential to harm patients.

- The dangers of biased AI algorithms, including misdiagnosis, delayed treatment, and poorer health outcomes for marginalized groups.

- The various ways in which bias can arise in healthcare algorithms, from data imbalances to flawed evaluation metrics.

Key Takeaways:

- Develop a comprehensive understanding of algorithmic bias in healthcare.

- Identify the potential sources of bias in AI algorithms.

- Become an advocate for responsible AI development and contribute to a healthier, more equitable future for all.