Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate...

Area of Science:

• Medical imaging
• Artificial intelligence
• Oncology

Background:

• Prostate magnetic resonance imaging (MRI) is key for detecting prostate cancer but interpretation is challenging.
• Artificial intelligence (AI) shows promise in prostate MRI analysis, but lacks transparency and external validation.
• The 'black box' nature of AI hinders trust, clinical deployment, and regulatory approval.

Purpose of the Study:

• To review interpretability and explainable artificial intelligence (XAI) concepts in prostate MRI.
• To discuss how AI transparency issues impede clinical use.
• To highlight the need for standardized frameworks for AI deployment in prostate cancer detection.

Main Methods:

• Literature review of interpretability and XAI approaches for AI models.
• Analysis of the impact of AI transparency on clinical practice and regulation.
• Discussion of standardized reporting frameworks for AI in medical imaging.

Main Results:

• AI tools for prostate MRI are advancing but require external validation in real-world settings.
• Lack of transparency in AI models ('black box' problem) is a significant barrier to clinical trust and adoption.
• XAI methods and standardized reporting frameworks are essential for safe and effective AI integration.

Conclusions:

• Explainable AI (XAI) is vital for the trustworthy deployment of AI in prostate cancer detection.
• Standardized frameworks are needed to transition from opaque AI models to transparent, informed clinical decision-making.
• Further research and adoption of XAI are necessary to realize the full potential of AI in prostate MRI.