Faculty Bibliography

Opportunistic CT screening refers to the extraction of clinically relevant imaging biomarkers from CT examinations routinely performed for unrelated primary indications. This screening does not require additional radiation exposure, imaging time, or patient burden. Advances in automated image analysis and artificial intelligence (AI) have substantially expanded the feasibility of opportunistic CT screening across a range of conditions, including low bone mineral density, hepatic steatosis, sarcopenia, adiposity, and vascular calcifications. These biomarkers are increasingly being recognized as important contributors to fracture risk, cardiometabolic disease, and frailty; yet they often remain undiagnosed in routine clinical practice. While numerous studies have demonstrated technical feasibility and diagnostic performance, widespread clinical adoption of opportunistic CT screening remains limited. Key challenges include those related to workflow integration, standardization of measurement techniques, quality assurance, interpretability of results, and uncertainty regarding downstream clinical management and value. In particular, not all opportunistic imaging biomarkers are equally actionable, and inappropriate or indiscriminate reporting may contribute to overdiagnosis or unnecessary follow-up. The authors summarize common applications of opportunistic CT screening, highlighting typical imaging findings and biomarkers. The authors discuss the evolution of automated and AI-based approaches, with emphasis on practical considerations for deployment, including pre- and postdeployment quality assurance, algorithm monitoring, and practical considerations for clinical integration. Emerging applications also are reviewed, with attention to current limitations. With a focus on clinical integration, this review is intended to inform radiologists, informaticians, and others working in health care systems who seek to translate opportunistic CT screening from proof-of-concept studies into scalable, clinically meaningful practice.

Radiology is undergoing a global transformation across its workforce, workplace, and workflows. Imaging demand and examination complexity continue to increase, while workforce growth has lagged. Although these challenges are global, they manifest differently across regions and practice settings. Workforce shortages, burnout, and a weakening academic mission with less emphasis on research and teaching are systemic problems. In response to growing workloads, digital technologies, including artificial intelligence, have been introduced to increase capacity. Although these tools have the potential to improve efficiency, quality, and education, their effect on radiologist productivity has been mixed. Practice models such as teleradiology and hybrid work offer more flexibility for radiologists and have improved access to imaging services, especially in rural and underserved regions. However, remote work can lead to isolation, as well as lack of collaboration and exchange of ideas, which may contribute to burnout. In addition, declining reimbursement, rising costs, and growing unreimbursed noninterpretive tasks put a strain on the radiology workforce. This review from the International Society for Strategic Studies in Radiology (IS3R) examines international experiences on the challenges affecting the global workforce capacity, imaging demand, workplace, work design, and the work environment for radiology, and offers potential solutions.

Global imaging demand now exceeds capacity due to an aging population, rising comorbidities, expanded indications for imaging, and workforce shortages. These pressures are reshaping national health care policy. At the same time, the competing demands to expand access to imaging services while reducing costs are creating momentum to reimagine imaging delivery through the integration of transformative technologies like artificial intelligence (AI). This consensus statement from the International Society for Strategic Studies in Radiology (IS3R) is a call to action, outlining promising AI solutions that increase workflow efficiency and align radiology with health system and payer priorities. Reflecting expert consensus from the IS3R meeting in Dublin, Ireland, in August 2025, this statement defines strategic directions for radiology amid accelerating digitalization, rapid innovation, and data-driven patient care. This article covers four domains: image interpretation, capacity building, data governance, and systemic digital transformation. This article examines how AI can address rising imaging demand and costs while addressing challenges related to improving data quality, standardization, and interoperability. It also highlights the need to re-engineer workflows and adapt to new care models while maintaining high-quality services. Finally, it presents consensus recommendations outlining how AI can enable multimodal health data integration and advance alignment with value-based care.

Integrated diagnostics is an emerging multidisciplinary model of care that integrates traditionally siloed domains, including pathology, radiology, genomics, and laboratory medicine, into a coordinated framework designed to improve diagnostic accuracy, treatment selection, and clinical outcomes. In this position statement, the EFLM Integrated Diagnostics Committee proposes a concise and operational definition of integrated diagnostics and outlines the relevance for healthcare systems. Integrated diagnostics has the potential to advance precision medicine through improved data integration, more coordinated interpretation, and enhanced clinical decision-making. Drawing on multidisciplinary expert consensus, we recommend adoption of a standardized conceptual framework to support research, clinical implementation, interoperability, and policy development across healthcare systems with implementation adapted to local needs, scale, and digital maturity.

Opportunistic screening leverages existing imaging examinations performed for unrelated routine clinical indications to systematically extract quantitative biomarkers. Artificial intelligence tools have made deployment at scale increasingly feasible. However, the pathway from a validated algorithm to a functioning clinical program remains poorly defined, and prospective implementation at scale is uncommon. Successful deployment requires coordinated engagement from radiologists, information technology and operational teams, and clinical care teams, each facing distinct decisions that determine whether a program functions reliably and delivers patient benefit. This article presents a practical framework for opportunistic screening implementation organized around these three stakeholder groups. We apply this framework to opportunistic CT osteoporosis screening, drawing on our experience developing such a program at a large academic medical center. The framework presented is intended to be broadly applicable across opportunistic screening applications as the field moves from algorithmic validation toward clinical translation.

With the rapid growth of the use of computed tomography, advances in artificial intelligence enable opportunistic screening, the systematic extraction of clinically meaningful biomarkers from imaging scans performed for other indications. Modeling studies demonstrate that opportunistic screening can be highly cost effective by enabling early intervention and preventing complications such as osteoporotic fractures. Musculoskeletal radiologists are uniquely positioned to contribute to this paradigm shift because routine examinations frequently include vertebrae, skeletal muscle, adipose tissue, and vasculature, all structures that provide quantitative data on bone mineral density, sarcopenia, adiposity, and cardiovascular risk. However, widespread implementation faces challenges, such as the need for prospective outcomes data, normative reference standards, workflow integration, and clear pathways for clinical follow-up. This review examines the rationale, technical foundations, key applications, and challenges for opportunistic screening in musculoskeletal radiology.

Background Osteoporosis is underdiagnosed and undertreated, prompting the exploration of opportunistic screening using CT and artificial intelligence. Purpose To develop a reproducible convolutional neural network to automatically identify a three-dimensional (3D) region of interest (ROI) in trabecular bone, develop a correction method to normalize attenuation values across different CT protocols and scanner models, and establish thresholds for diagnosing osteoporosis in a large diverse population. Materials and Methods In this retrospective study, a deep learning-based method was developed to automatically quantify trabecular attenuation of the thoracic and lumbar spine on CT images with use of a 3D ROI. A statistical method was developed to adjust for different tube voltages and scanner models. Normative values and diagnostic thresholds for trabecular attenuation of the spine for osteoporosis were established based on the reported prevalence of osteoporosis by the World Health Organization. Differences between groups were assessed using the Student t test. Results A total of 538 946 CT examinations from 283 499 patients (mean age, 65 years ± 15 [SD]; 145 021 [51.2%] female; 157 457 [55.5%] White patients) were analyzed, representing 43 scanner models and six different tube voltages. The attenuation values at 80 kVp and 120 kVp differed by 23%, and different scanner models resulted in differences in values of less than 10%. The automated ROI placement of 1496 vertebrae was validated by manual radiologist review and demonstrated greater than 99% agreement. Trabecular attenuation was greater in young women (age <50 years) than in young men (P < .001) and decreased with age, with a steeper decline in postmenopausal women. In patients older than 50 years, trabecular attenuation was greater in male than in female patients (P < .001). Trabecular attenuation was highest in Black patients, followed by Asian patients, and lowest in White patients (P < .001). Conclusion Deep learning-based automated opportunistic osteoporosis screening can identify patients with low bone mineral density using CT scans obtained for clinical purposes with use of different scanners and protocols. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Feuerriegel and Sutter in this issue.