Risks and Mitigation of Bias in Medical AI

Authored by associate Zach Harned and published by the Journal of Robotics, Artificial Intelligence, and Law, this article delves into the transformative potential of AI in healthcare. It highlights the improvements in medical diagnosis and treatment, leading to more efficient and cost-effective care. Despite these benefits, the article emphasizes the importance of addressing risks, particularly bias, to prevent misdiagnosis and discriminatory outcomes. Real-world instances and research findings are explored, shedding light on associated legal repercussions such as liability under antidiscrimination laws and contractual agreements between AI vendors and healthcare providers.

Read the full article here


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