Black Boxing in Healthcare Artificial Intelligence

Source Domain: www.cureus.com

Artificial intelligence and machine learning are increasingly popular in various fields, particularly medicine, for their efficiency and accuracy. However, they often operate like a “black box” where the underlying processes are unclear.
The “black box” concept leads to two main issues: a lack of transparency and a lack of interpretability. Physicians struggle to understand how AI systems function, which impacts trust and usage.
To improve transparency, Dr. Keith Dreyer suggests standardizing the information in AI catalogs to help doctors make informed decisions about their use.
Poon and Sung propose a stepwise method for making AI systems more transparent, using techniques like Garson’s Algorithm and LIME to explain the AI’s decision-making process.
Some experts, like Eric Topol, argue that AI’s potential benefits outweigh transparency concerns, provided safety is maintained. They caution against halting AI development due to the “black box” nature.
The debate around AI transparency in healthcare urges developers to rethink AI creation methods and encourages healthcare providers and patients to reconsider their expectations and priorities in treatment planning.

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