Purpose: Disproportionally high rates of coronavirus disease 2019 (COVID-19) have been noted among communities with limited English proficiency, resulting in an unmet need for improved multilingual care and interpreter services. To enhance multilingual care, the authors created a freely available web application, RadTranslate, that provides multilingual radiology examination instructions. The purpose of this study was to evaluate the implementation of this intervention in radiology. Methods:The device-agnostic web application leverages artificial intelligence text-to-speech technology to provide standardized, human-like spoken examination instructions in the patient's preferred language. Standardized phrases were collected from a consensus group consisting of technologists, radiologists, and ancillary staff members. RadTranslate was piloted in Spanish for chest radiography performed at a COVID-19 triage outpatient center that served a predominantly Spanish-speaking Latino community. Implementation included a tablet displaying the application in the chest radiography room. Imaging appointment duration was measured and compared between pre-and postimplementation groups.Results: In the 63-day test period after launch, there were 1,267 application uses, with technologists voluntarily switching exclusively to RadTranslate for Spanish-speaking patients. The most used phrases were a general explanation of the examination (30% of total), followed by instructions to disrobe and remove any jewelry (12%). There was no significant difference in imaging appointment duration (11 AE 7 and 12 AE 3 min for standard of care versus RadTranslate, respectively), but variability was significantly lower when RadTranslate was used (P ¼ .003).Conclusions: Artificial intelligence-aided multilingual audio instructions were successfully integrated into imaging workflows, reducing strain on medical interpreters and variance in throughput and resulting in more reliable average examination length.

Purpose Physician–scientists have long been considered an endangered species, and their extended training pathway is vulnerable to disruptions. This study investigated the effects of COVID-19-related challenges on the personal lives, career activities, stress levels, and research productivity of physician–scientist trainees and faculty. Method The authors surveyed medical students (MS), graduate students (GS), residents/fellows (R/F), and faculty (F) using a tool distributed to 120 U.S. institutions with MD–PhD programs in April–June 2020. Chi-square and Fisher’s exact tests were used to compare differences between groups. Machine learning was employed to select variables for multivariate logistic regression analyses aimed at identifying factors associated with stress and impaired productivity. Results The analyses included 1,929 respondents (MS: n = 679, 35%; GS: n = 676, 35%; R/F: n = 274, 14%; F: n = 300, 16%). All cohorts reported high levels of social isolation, stress from effects of the pandemic, and negative impacts on productivity. R/F and F respondents were more likely than MS and GS respondents to report financial difficulties due to COVID-19. R/F and F respondents with a dual degree expressed more impaired productivity compared with those without a dual degree. Multivariate regression analyses identified impacted research/scholarly activities, financial difficulties, and social isolation as predictors of stress and impaired productivity for both MS and GS cohorts. For both R/F and F cohorts, impacted personal life and research productivity were associated with stress, while dual-degree status, impacted research/scholarly activities, and impacted personal life were predictors of impaired productivity. More female than male respondents reported increased demands at home. Conclusions This national survey of physician–scientist trainees and faculty found a high incidence of stress and impaired productivity related to the COVID-19 pandemic. Understanding the challenges faced and their consequences may improve efforts to support the physician–scientist workforce in the postpandemic period.

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in ”radiomics”, a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

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The insidious onset and aggressive nature of pancreatic cancer contributes to the poor treatment response and high mortality of this devastating disease. While surgery, chemotherapy and radiation have contributed to improvements in overall survival, roughly 90% of those afflicted by this disease will die within 5 years of diagnosis. The developed ablative locoregional treatment modalities have demonstrated promise in terms of overall survival and quality of life. In this review, we discuss some of the recent studies demonstrating the safety and efficacy of ablative treatments in patients with locally advanced pancreatic cancer.

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Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

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