How Crowd-in-the-Loop Can Optimize AI in Healthcare

As we see the remarkable things that AI can accomplish, one thing that has become clear is that human judgment is needed to effectively harness these tools and prevent unintended consequences. While AI algorithms can sift through vast amounts of data and uncover hidden patterns, they are not immune to biases, errors, and ethical dilemmas that may arise from the data and methods used to train them.

Clinical AI Needs a Human Touch

The rise of artificial intelligence (AI) in healthcare has been nothing short of transformative. From diagnostics to personalized treatment plans, AI promises to revolutionize patient care. However, one of the most pressing challenges facing healthcare today is how to validate AI models and ensure they are safe and effective. To accomplish this, we believe that incorporating a human-in-the-loop (HITL) approach that harnesses human clinical expertise is an essential part of a responsible AI strategy.