No abstract

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and “learning” patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

Purpose: Social determinants of health, including race and insurance status, contribute to patient outcomes. In academic health systems, care is provided by a mix of trainees and faculty members. The optimal staffing ratio of trainees to faculty members (T/F) in radiology is unknown but may be related to the complexity of patients requiring care. Hospital characteristics, patient demographics, and radiology report findings may serve as markers of risk for poor outcomes because of patient complexity.Methods: Descriptive characteristics of each hospital in an urban five-hospital academic health system, including payer distribution and race, were collected. Radiology department T/F ratios were calculated. A natural language processing model was used to classify multimodal report findings into nonacute, acute, and critical, with report acuity calculated as the fraction of acute and critical findings. Patient race, payer type, T/F ratio, and report acuity score for hospital 1, a safety net hospital, were compared with these factors for hospitals 2 to 5. Results:The fraction of patients at hospital 1 who are Black (79%) and have Medicaid insurance (28%) is significantly higher than at hospitals 2 to 5 (P < .0001), with the exception of hospital 3 (80.1% black). The T/F ratio of 1.37 at hospital 1 as well as its report acuity (28.9%) were significantly higher (P < .0001 for both).Conclusions: T/F ratio and report acuity are highest at hospital 1, which serves the most at-risk patient population. This suggests a potential overreliance on trainees at a site whose patients may require the greatest expertise to optimize care.