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Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease., Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST)., Material and Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI] < 25 kg/m 2 ) and overweight (BMI ≥ 25 kg/m 2 ). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year., Results: There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p = 0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p = 0.04)., Conclusion: SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors., (© 2025 The Author(s). Journal of Internal Medicine published by John Wiley & Sons Ltd on behalf of Association for Publication of The Journal of Internal Medicine.)

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions., (© 2024. The Author(s).)

Background: Many states in the United States have progressed towards legalization of marijuana including decriminalization, medicinal and/or recreational use. We studied the impact of legalization on cannabis-related emergency department visits in states with varying degrees of legalization., Methods: Seventeen healthcare institutions in fifteen states (California, Colorado, Connecticut, Florida, Iowa, Kentucky, Maryland, Massachusetts, Missouri, New Hampshire, Oregon, South Carolina, Tennessee, Texas, Washington) participated. Cannabinoid immunoassay results and cannabis-related International Classification of Diseases (ninth and tenth versions) codes were obtained for emergency department visits over a 3- to 8-year period during various stages of legalization: no state laws, decriminalized, medical approval before dispensaries, medical dispensaries available, recreational approval before dispensaries and recreational dispensaries available. Trends and monthly rates of cannabinoid immunoassay and cannabis-related International Classification of Diseases code positivity were determined during these legalization periods., Results: For most states, there was a significant increase in both cannabinoid immunoassay and International Classification of Diseases code positivity as legalization progressed; however, positivity rates differed. The availability of dispensaries may impact positivity in states with medical and/or recreational approval. In most states with no laws, there was a significant but smaller increase in cannabinoid immunoassay positivity rates., Conclusions: States may experience an increase in cannabis-related emergency department visits with progression toward marijuana legalization. The differences between states, including those in which no impact was seen, are likely multifactorial and include cultural norms, attitudes of local law enforcement, differing patient populations, legalization in surrounding states, availability of dispensaries, various ordering protocols in the emergency department, and the prevalence of non-regulated cannabis products.