AI holds immense promise in revolutionizing healthcare, from fast-tracking research and development to providing earlier diagnoses to personalized treatment plans.

However, as AI algorithms increasingly influence medical decision-making, concerns about bias and discrimination have become increasingly apparent. These biases can arise from inherent issues in AI datasets, algorithmic design and implementation, potentially perpetuating disparities in healthcare delivery and outcomes.

This session will delve into the ethical, legal and security implications of healthcare discrimination facilitated by AI, including the erosion of trust, patient harm and legal challenges. The panel will explore how AI’s blind spots can inadvertently reinforce existing biases and how representative datasets can help mitigate these challenges.

The session will cover:

• Biases in AI datasets: Understand how biases in training data can lead to unequal healthcare outcomes and discuss the importance of using diverse and representative datasets
• Algorithmic design, implementation and security: Analyze how design choices and system vulnerabilities can influence the fairness and safety of AI applications
• Mitigation and protection strategies: Discuss effective strategies to mitigate bias in AI healthcare applications, emphasizing the need for ongoing oversight, ethical standards, and robust security measures to ensure equitable and ethical use of AI in healthcare