AI and human interactions in prostate cancer diagnosis using MRI

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Healthcare
• Oncology Diagnostics

Background:

• Single-reader prostate MRI interpretations face limitations including inter-reader variability and diagnostic errors.
• These limitations can lead to missed cancers and suboptimal patient management.
• Artificial intelligence (AI) offers potential solutions to enhance prostate MRI analysis.

Purpose of the Study:

• To explore the integration of AI into prostate MRI workflows.
• To review various AI-integrated strategies, from decision support to autonomous analysis.
• To examine the benefits and challenges of AI in prostate MRI interpretation.

Main Methods:

• Review of AI-integrated workflow strategies in prostate MRI.
• Analysis of AI's role as a second reader, for triage, and in autonomous analysis.
• Examination of performance metrics (sensitivity, specificity, predictive values) for different AI applications.

Main Results:

• AI integration can enhance lesion detection sensitivity and reduce false negatives.
• AI workflows can optimize radiologist workload, streamline processes, and reduce reporting times.
• Challenges include automation bias and the need for high accuracy, especially with increased AI autonomy.

Conclusions:

• Collaborative AI-radiologist workflows can improve diagnostic accuracy and efficiency in prostate MRI.
• Strategic AI implementation has the potential to reduce missed cancers and improve patient care.
• Rigorous prospective validation studies are essential to assess AI safety and effectiveness in clinical settings.