Avoiding missed opportunities in AI for radiology.

Area of Science:

• Artificial Intelligence (AI) in Medical Imaging
• Deep Learning Applications in Radiology
• Healthcare Informatics and AI

Background:

• The resurgence of Artificial Intelligence (AI), particularly Deep Learning, has led to numerous medical applications, especially in Radiology.
• A comprehensive understanding of AI's clinical and non-clinical applications is crucial for maximizing benefits and mitigating risks in healthcare.
• This review examines the practical application of AI in a clinical radiology setting to identify areas of significant clinical and financial value.

Discussion:

• AI algorithms are effective in detecting specific clinical entities for which they are designed, aiding in the reduction of diagnostic errors.
• AI facilitates the prioritization of positive findings, such as pulmonary embolism and intracranial hemorrhage, enabling timely intervention.
• The incidental detection of conditions like cerebral aneurysms by AI can trigger essential patient-oriented management pathways.

Key Insights:

• AI's ability to detect unsuspected findings, like brain aneurysms, is clinically significant and can initiate crucial patient workups.
• The subsequent clinical management and follow-up for AI-detected incidental findings can generate reimbursement, offsetting AI implementation costs.
• A structured program for screening, management, and follow-up, leveraging AI for incidental aneurysm detection, has been successfully implemented across a multi-hospital system.

Outlook:

• AI in Radiology holds the potential to augment medical expertise and enhance patient care delivery.
• Developing AI tools within a fiscally responsible framework is key to avoiding missed opportunities and ensuring sustainable integration.
• Continued exploration of AI applications can lead to improved diagnostic capabilities and more efficient healthcare operations.