The path to trustworthy medical AI: the evolving role of explainability.

Key Points

Explainability is crucial for the trustworthiness of AI systems in medicine, as lack of clarity diminishes user confidence.
AI systems face inherent tension between achieving high accuracy and maintaining understandable interpretations of their processes.
The evolution of explainability in AI has shifted from an ethical concern to becoming a potential legal mandate in healthcare.
Understanding the connection between explainability and trust in medical AI is essential for wider acceptance and integration.

Abstract

The integration of artificial intelligence (AI) in medicine has applications across several clinical domains, spanning from disease prevention and diagnosis through treatment and long-term care, as well as remote care. However, many AI systems are inherently characterized by limited explainability, meaning the processes behind their outcomes cannot be clearly understood or communicated to humans, whether developers or end users. This viewpoint explores the importance of AI explainability in medicine by first tracing its evolution from a primarily ethical concern to a legal requirement. It then examines the connection between explainability and the trustworthiness of AI systems. Finally, it considers how explainability is approached from a technical standpoint and its inherent tension with achieving high accuracy.

Mark Helpful

Bookmark

Relay