Safeguarding Accuracy: Clinical Examples High lighting the Role of Quality Assurance in AI-Driven Radiology

Janus Uhd Nybing, CTO, CO-founder Radiological AI Testcenter RAIT

In this Talk, I will explore the critical importance of quality assurance in the use of AI in radiology.

As AI technologies revolutionize medical imaging, ensuring their accuracy and reliability becomes paramount to patient care. Through clinical examples, I will highlight instances where AI has both succeeded and failed in diagnosing conditions. These examples underscore the potential risks of AI misdiagnosis, which can lead to severe patient harm.

To address these challenges, I will propose a comprehensive solution that includes validation protocols, continuous monitoring, and highlight the need for collaboration between clinicians and AI developers. By implementing robust quality assurance measures, we can harness the full potential of AI in radiology, enhancing diagnostic accuracy and ultimately improving patient outcomes.

This is from the conference "Artificial Intelligence in Healthcare".

Join the discussion about the future of healthcare at our conference, where AI's power to transform patient care through swift, accurate data processing and task automation takes center stage.