Faculty Bibliography

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.

Expansion of bone marrow (BM) adipocytes has been linked to nutritional pressures, suggesting that BM is a dynamic compartment that responds to fluctuations in systemic nutritional availability to regulate osteogenesis and hematopoiesis. Here, we investigated BM metabolism in response to acute overnutrition (high calorie diet; HCD) and calorie deprivation (fasting). Participants underwent a 10-day HCD followed by a two-week interval of an ad libitum diet and then underwent 10 days of fasting. BM adipocytes and sera were collected before and after each dietary intervention for each participant. Using comprehensive and integrated analyses, we characterized nutritional influences on BM adiposity. BM adipocytes after HCD showed an upregulation of FOXP3, the transcription factor that controls the development of Tregs, which are critical in reducing inflammatory immune responses. After fasting, BM adipocytes had an upregulation of inflammatory genes (CP, CFH, VCAN, and IGFBP3). Proteomic analysis after HCD showed that BM serum had an upregulation of proteins related to an inflammatory/complement pathway. After fasting, in the BM serum, there was a significant downregulation of inflammatory/complement pathway proteins. Despite both interventions causing BM adipose tissue expansion, the mechanism for adipogenesis appears to be dependent on nutrient availability. After HCD, lipid-mediated signaling and lipid storage, and lipid droplet biogenesis were significantly downregulated. In contrast, after fasting, lipid-mediated signaling and lipid storage, and lipid droplet biogenesis were significantly upregulated. Overall, our results demonstrate key differences in inflammatory response and lipid metabolism between HCD and fasting, despite a nearly identical BM adipose phenotype. Further analyses are needed to understand the effects nutritional pressures have on BM adipogenesis and immune responses.

BACKGROUND:Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events. METHODS:A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both non-contrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization). RESULTS:Our cohort included 3599 patients (median age 60 years, 62% male, 74% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r=0.56, P<0.001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683 to 0.718]; AUC for PREVENT plus AAC 0.782 [0.767 to 0.797]; P<0.001). There were 324 MACE, of which 246 were MCE. Following adjustment for the 10-year cardiovascular disease PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P<0.001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P<0.001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE. CONCLUSIONS:Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.