Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. It has had a dramatic impact on society and world economies in only a few months. COVID-19 presents numerous challenges to all aspects of healthcare, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications to healthcare. Machine learning is a subset of AI that employs deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than humans. In this manuscript, we explore the potential for a simple and widely available test as a chest x-ray (CXR) to be utilized with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. Finally, we developed and described a publicly available website to demonstrate how this technology can be made readily available in the future.

We report the results of a retrospective five-year study within a veteran population aimed at correlating abnormal thyroid fine-needle aspiration (FNA) diagnosis with associated molecular testing to the histology of the surgical resection. A retrospective analysis of abnormal thyroid FNAs with associated molecular testing and surgical outcome was conducted from January 1, 2015 to December 31, 2020. Aspirates were classified using the Bethesda system for reporting thyroid cytopathology, including atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), follicular neoplasm/suspicious for follicular neoplasm (FN/SFN), suspicious for malignancy (SM), and malignant. Pertinent data, including patient demographics, imaging, and ancillary testing were reviewed. A thyroid cancer mutation panel assessing the most common mutations and rearrangements associated with neoplasia was utilized. The results of molecular testing were directly compared and correlated with final cytological and histological diagnosis. A total of 1850 thyroid aspirates were performed, 200 of which were given an abnormal cytologic diagnosis. Thirty-six samples were submitted for molecular testing and subsequent surgical follow-up. Four were called malignant on cytology. 32 were placed in an indeterminate category (89%). Within indeterminate cases: 53% exhibited positive molecular mutations (n=17), 34% no mutation detected (n=11), and 13% insufficient quantity for testing (n=4). Upon surgical resection in the mutation-positive group: 18% had no malignancy (n=3), and the remaining 82% were positive for malignancy (n=14). Mutations in the histologically malignant group included: 57% BRAF (n=8), 21% NRAS (n=3), 7% HRAS (n=1), 7% KRAS (n=1), and 7% PAX8/PPAR gamma (n=1). In indeterminate cases with no mutation detected, 10 cases were found to be benign, and one case of malignancy was diagnosed. The probability of indeterminate diagnosis in combination with no mutation yielded a 91% chance of benign entity and 9% chance of malignancy. We demonstrated 93% sensitivity and 91% negative predictive value (NPV) for the risk of malignancy in indeterminate cytology specimens with ancillary molecular testing. There was 77% specificity and 82% positive predictive value (PPV) for our data set. In indeterminate samples, the detection of a mutation was highly predictive of malignancy and a strong indicating factor for surgery with a high sensitivity and NPV. Molecular testing refined or established the diagnosis in 89% of the cases. Our results indicate that molecular testing of thyroid nodules enhances the accuracy of FNA cytology and the subsequent surgical outcome.

Artificial intelligence (AI) is rapidly transforming healthcare, yet its integration into medical education remains inconsistent and lacks standardization across training levels. This article synthesizes current research on AI applications within medical education, highlighting significant gaps between the growing clinical role of AI and its representation in undergraduate and postgraduate curricula. The article identifies disparities in AI awareness and utilization, with postgraduate trainees demonstrating greater familiarity than undergraduates, and notes that most existing educational efforts are concentrated in specialty training and continuing education, particularly in fields such as radiology, pathology, surgery, cardiology, and dentistry. While medical trainees generally express positive attitudes toward acquiring AI competencies, barriers such as the absence of standardized frameworks and AI taxonomy, limited faculty expertise, curricular constraints, and ethical considerations impede broader adoption. Drawing on examples of pioneering programs and a systematic analysis of curricular approaches, we propose a novel, tiered framework for comprehensive AI integration across the medical education continuum. This framework emphasizes universal AI literacy, critical evaluation skills, ethical awareness, and experiential learning at the undergraduate level, with extensions for specialty-specific training and advanced technical or leadership tracks. Recommendations include phased implementation strategies, faculty development initiatives, including “teach the teacher”, and competency-based assessment methods. The article concludes that adequate preparation of future physicians requires a shift from isolated AI initiatives to coordinated, longitudinal integration efforts supported by collaboration among educational institutions, professional societies, and technology experts. Future research should focus on evaluating educational outcomes, developing robust assessment tools for AI competencies, and examining the long-term clinical impact of AI training.

US Veterans are disproportionately affected by type 2 diabetes (T2D) compared with civilians. Previously, we demonstrated that serum GAS5 levels are lower in Veterans with T2D compared with non-diabetics and established GAS5 as a genetic biomarker for T2D, validated by others. Dried blood spots (DBS) are advantageous because collection is minimally invasive, and shipment can be at room temperature. Measurement of lncRNAs in DBS is not yet reported and hence we sought to measure lncRNA GAS5 levels in DBS collected from Veteran participants. Results demonstrate that older age was independently associated with lower GAS5 levels among Veterans. Comparing Veterans across war eras, in this cohort, Vietnam War (VW) Veterans had higher incidences of diabetes compared to Gulf War (GW) or Other (non-VW/GW) wars. VW Veterans with A1c values greater than 6.5 had lower GAS5 levels, independent of BMI. Among the VW Veterans, a very small number of Veterans with self-reported Agent Orange (AO) exposure had lower GAS5 levels. Based on this observation, a mouse model with acute TCDD exposure was evaluated for Gas5 levels. The results demonstrate a significant negative correlation between TCDD dose and Gas5 levels measured in blood and DBS in the mouse model. Overall, the study demonstrates that lncRNA GAS5 measured in DBS and blood may be used in biomarker studies in diabetic patients. These results support the use of DBS in longitudinal studies of chronic diseases in active-duty service members or Veterans with toxic exposures during military service.