Background: Understanding of the diagnosis and treatment of adults with community-acquired pneumonia (CAP) has evolved thanks to new evidence, experience, and emerging technologies. This document updates evidence-based clinical practice guidelines on four key questions for the diagnosis and management of adult patients with CAP. Methods: A multidisciplinary panel integrated systematic reviews of comparative evidence with other relevant research and clinical experience, then applied Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology to produce recommendations using the Evidence to Decision Framework. Results: The panel formulated clinical recommendations that address questions related to CAP, including lung ultrasound for diagnosis, empiric antibacterial therapy if a test for a respiratory virus is positive, antibiotic duration, and the use of systemic corticosteroids. Conclusions: The panel formulated and provided the rationale for recommendations on selected diagnostic and treatment strategies for adult patients with CAP.

The coronavirus disease 2019 (COVID-19) has been identified in over 110 million people with no studies comparing pre-infection pulmonary function to post-infection. This study's aim was to compare pre-infection and post-infection pulmonary function tests (PFT) in COVID-19 infected patients to better delineate between preexisting abnormalities and effects of the virus. This was a retrospective multi-center cohort study. Patients were identified based on having COVID-19 and a pre- and post-infection PFT within one year of infection during the time period of March 1, 2020 to November 10, 2020. There was a total of 80 patients, with an even split in gender; the majority were white (n = 70, 87·5%) and never smokers (n = 42, 52·5%). The majority had mild to moderate COVID-19 disease (n = 60, 75·1%) with 25 (31·2%) requiring hospitalization. There was no difference between the pre- and post-PFT data, specifically with the forced vital capacity (FVC) (p = 0·52), forced expiratory volume in 1 s (FEV1)(p = 0·96), FEV1/FVC(p = 0·66), total lung capacity (TLC) (p = 0·21), and diffusion capacity (DLCO)(p = 0·88). There was no difference in the PFT when analyzed by hospitalization and disease severity. After adjusting for potential confounders, interstitial lung disease (ILD) was independently associated with a decreased FEV1 (-2·6 [95% CI, -6·7 to - 1·6] vs. -10·3 [95% CI, -17·7 to -2·9]; p = 0·03) and an increasing age (p = 0·01) and cystic fibrosis (-1·1 [95% CI, -4·5 to- 2·4] vs. -36·5 [95% CI, -52·1 to -21·0]; p < 0·01) were associated with decreasing FVC when comparing pre and post infection PFT. Only increasing age was independently associated with a reduction in TLC (p = 0·01) and DLCO (p = 0·02) before and after infection. This study showed that there is no difference in pulmonary function as measured by PFT before and after COVID-19 infection in non-critically ill classified patients. There could be a relationship with certain underlying lung diseases (interstitial lung disease and cystic fibrosis) and decreased lung function following infection. This information should aid clinicians in their interpretation of pulmonary function tests obtained following COVID-19 infection. No funding was obtained for this study.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.

Purpose: Despite its growing popularity and clinical utility among hospital-based physicians, there are no formal competency requirements nor training standards for United States based Internal Medicine Residencies for learning point-of-care ultrasonography (POCUS). The purpose of this investigation was to study the impact and effectiveness of a novel POCUS curriculum for an Internal Medicine (IM) residency program. Patients and methods: This was a Single-Group Educational Quasi-Experiment involving Categorical and Preliminary Internal Medicine Residents in Post-Graduate Years 1 through 3 at a single United States academic tertiary center. The study period was from January 1, 2017, through June 30, 2017, during which time the residents participated in monthly modules including didactics and hands-on ultrasound scanning skills with live models. Participants completed a comprehensive knowledge examination at the beginning and end of the six-month period. Participants were also tested regarding hands-on image acquisition and interpretation immediately before and after the hands-on skills labs. The primary outcome measure was performance improvement in a comprehensive medical knowledge assessment. Results: In total, 42 residents consented for participation. The residents’ monthly rotations were adjusted in order to accommodate the new educational process. Among 29 participants with complete data sets for analysis, the mean (SD) comprehensive knowledge examination score improved from 60.9% before curriculum to 70.2% after curriculum completion (P<0.001). Subgroup analysis determined that improvement in medical knowledge required attending at least 2 out of the 6 (33%) educational sessions. Attendance at hands-on skills labs correlated significantly with improvement; didactics alone did not. Conclusion: A longitudinal POCUS curriculum consisting of both didactic sessions and hands-on skills labs improves knowledge, image acquisition, and interpretation skills of residents. Having this curriculum span at least 6 months provides learners the opportunity to attend multiple classes which strengthens learning through repetition while also providing learners flexibility in schedule.

Pulmonary arterial hypertension (PAH) is a devastating disease with significant morbidity and mortality. There are many psychosocial and financial implications of this disease; however, little is known how this affects the treatment of PAH patients. A questionnaire-based prospective cohort study was performed on 106 PAH patients from a Pulmonary Hypertension Center and the Pulmonary Hypertension Association national conference in 2018. The demographic, treatment, psychosocial, employment, financial impact on treatment data was obtained. The majority of patients had cardiopulmonary symptoms despite treatment. The symptoms affected their social and work lives, with about one in three applying for disability because of their PAH. The majority of PAH patients had insurance coverage, but still noted a significant financial burden of the disease, with nearly a half who needed financial assistance to pay for their PAH medications. Thirty (28.3%; 95% CI, 20.6–37.5%) patients mentioned they changed their medication regimen, with some skipping doses outright (28 [26.4%; 95% CI, 19–35.6%]) in order to save money. PAH continues to cause significant psychosocial and financial burden on patients despite advances in medications. This impact ranged from dissatisfaction with quality of life, to unemployment, to altering their medication regimen to save money.