Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)-"Practical Challenges in AI Deployment"

"Practical Challenges in AI Deployment" – Dr. Jason Poff

Pdf Summary

This presentation by Jason A. Poff, MD, Director of Clinical AI at Radiology Partners, addresses practical challenges in deploying artificial intelligence (AI) in radiology practices. It focuses on preparing radiologists to confront common AI limitations and integrating AI tools effectively through education and workflow design.<br /><br />Key challenges include AI’s imperfection, such as false negatives ("The First Case Fail") and false positives, leading to diagnostic misses and overcalls. Human factors like distrust bias, clinical distractions, and IT downtime can exacerbate errors, especially in high-pressure settings like end-of-day or weekend reads ("The Friday Night Miss"). Solutions emphasize operational workflow enhancements to mitigate these issues.<br /><br />The concept of "The AI Collision" warns against deploying multiple AI systems simultaneously, which can cause analysis paralysis, slow clinical care, and compound biases. While combining models for complementary strengths can reduce errors, caution is advised, deploying only when one model clearly addresses gaps of another.<br /><br />The talk also covers "AI was Late to the Party," referring to cases not initially processed by AI, raising questions on handling such unassessed cases. Additionally, differential AI access across multi-system practices may cause discomfort among radiologists and influence case selection bias.<br /><br />Human-in-the-loop reviews using AI can uncover care gaps retrospectively, but must be managed within peer review frameworks, consider legal protections, and include processes for addressing individual patient care gaps.<br /><br />Overall, the presentation outlines six real-world scenarios illustrating AI deployment challenges and highlights three key points: expect and prepare for challenges, mitigate human biases through education, and proactively design workflows tailored to AI integration. The goal is to responsibly leverage AI's potential while addressing its practical and human limitations in radiology.<br /><br />For further details, RSNA.org offers additional resources on AI deployment in radiology.

Keywords

Artificial Intelligence in Radiology

AI Deployment Challenges

False Negatives and Positives

Human Factors in AI Errors

Workflow Design for AI Integration

AI Collision Risks

Multi-AI System Deployment

Human-in-the-Loop Review

Bias Mitigation in AI

Radiologist Education on AI