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Avoiding missed opportunities in AI for radiology.

Jonathan Scheiner¹, Leonard Berliner²

¹Department of Radiology, Staten Island University Hospital - Northwell Health, 475 Seaview Avenue, Staten Island, NY, 10305, USA.

International Journal of Computer Assisted Radiology and Surgery

|November 25, 2024

Summary

Artificial Intelligence (AI) in Radiology enhances diagnostic accuracy and prioritizes critical cases like pulmonary embolism. AI-driven detection of incidental findings, such as brain aneurysms, can improve patient care and generate revenue, supporting AI implementation.

Keywords:

Artificial intelligence Economics Return on investment

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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.