The false hope of current approaches to explainable artificial intelligence in health care

🤖 Plain-English Summary

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients.

🔑 Key Findings

• It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias.
• In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support.
• We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients.

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