Contrast enhanced ultrasound of liver lesions in patients treated for childhood malignancies.

BACKGROUND: Patients treated for cancer have a higher incidence of focal liver lesions than the general population and there is often concern for a malignant etiology. This can result in patient, caregiver and physician anxiety and is managed by a "wait and watch" approach, or immediate additional imaging, or biopsy, depending on the degree of clinical concern. Because it is a low-cost, easily accessible, radiation and sedation free modality, we investigated the value of contrast enhanced ultrasound (CEUS) to accurately distinguish benign from malignant liver lesions in patients treated for childhood malignancies. METHODS: We performed an IRB approved retrospective study of 68 subjects who were newly diagnosed, on treatment or off treatment for a pediatric malignancy and had liver lesions discovered on CT, MRI or non-contrast ultrasound and subsequently underwent CEUS between September 2013 and September 2021. Two experienced pediatric radiologists and a radiology trainee, blinded to the etiology of the liver lesions, independently reviewed the CEUS examinations and categorized lesions as benign, indeterminate, or malignant. The reference standard was biopsy for 19 lesions and clinical follow-up for 49. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of CEUS were calculated using only the benign and malignant CEUS classifications. Inter-reviewer agreement was assessed by Cohen's kappa statistic. RESULTS: There were 26 males and 42 females, mean age, 14.9 years (range, 1-52 years). Fifty subjects were off therapy, twelve receiving treatment, and six with newly diagnosed cancer. By the reference standard, 59 (87%) lesions were benign and 9 (13%) were malignant. Sensitivities of CEUS for the three reviewers ranged from 83 to 100% (95% CI, 35.9-100%), specificities from 93.1 to 96.0% (95% CI, 83.5-99.6%), PPV 60.0-71.4% (95% CI, 29.0-96.3%), NPV 98.0-100% (95% CI, 89.2-100%) and accuracy from 93.8 to 94.6% (95% CI, 85.1-99.7%). The kappa statistic for agreement between the two experienced radiologists was moderate at 0.58. CONCLUSIONS: CEUS is highly accurate in distinguishing benign from malignant etiologies of liver lesions in patients treated for pediatric malignancies.

From Bench to Bedside With Large Language Models: AJR Expert Panel Narrative Review.

Large language models (LLMs) hold immense potential to revolutionize radiology. However, their integration into practice requires careful consideration. Artificial intelligence (AI) chatbots and general-purpose LLMs have potential pitfalls related to privacy, transparency, and accuracy, limiting their current clinical readiness. Thus, LLM-based tools must be optimized for radiology practice to overcome these limitations. Although research and validation for radiology applications remain in their infancy, commercial products incorporating LLMs are becoming available alongside promises of transforming practice. To help radiologists navigate this landscape, this AJR Expert Panel Narrative Review provides a multidimensional perspective on LLMs, encompassing considerations from bench (development and optimization) to bedside (use in practice). At present, LLMs are not autonomous entities that can replace expert decision-making, and radiologists remain responsible for the content of their reports. Patient-facing tools, particularly medical AI chatbots, require additional guardrails to ensure safety and prevent misuse. Still, if responsibly implemented, LLMs are well-positioned to transform efficiency and quality in radiology. Radiologists must be well-informed and proactively involved in guiding the implementation of LLMs in practice to mitigate risks and maximize benefits to patient care.

ChatGPT and the Future of Journal Reviews: A Feasibility Study.

The increasing volume of research submissions to academic journals poses a significant challenge for traditional peer-review processes. To address this issue, this study explores the potential of employing ChatGPT, an advanced large language model (LLM), developed by OpenAI, as an artificial intelligence (AI) reviewer for academic journals. By leveraging the vast knowledge and natural language processing capabilities of ChatGPT, we hypothesize it may be possible to enhance the efficiency, consistency, and quality of the peer-review process. This research investigated key aspects of integrating ChatGPT into the journal review workflow. We compared the critical analysis of ChatGPT, acting as an AI reviewer, to human reviews for a single published article. Our methodological framework involved subjecting ChatGPT to an intricate examination, wherein its evaluative acumen was juxtaposed against human-authored reviews of a singular published article. As this is a feasibility study, one article was reviewed, which was a case report on scurvy. The entire article was used as an input into ChatGPT and commanded it to "Please perform a review of the following article and give points for revision." Since this was a case report with a limited word count the entire article could fit in one chat box. The output by ChatGPT was then compared with the comments by human reviewers. Key performance metrics, including precision and overall agreement, were judiciously and subjectively measured to portray the efficacy of ChatGPT as an AI reviewer in comparison to its human counterparts. The outcomes of this rigorous analysis unveiled compelling evidence regarding ChatGPT's performance as an AI reviewer. We demonstrated that ChatGPT's critical analyses aligned with those of human reviewers, as evidenced by the inter-rater agreement. Notably, ChatGPT exhibited commendable capability in identifying methodological flaws, articulating insightful feedback on theoretical frameworks, and gauging the overall contribution of the articles to their respective fields. While the integration of ChatGPT showcased immense promise, certain challenges and caveats surfaced. For example, ambiguities might present with complex research articles, leading to nuanced discrepancies between AI and human reviews. Also figures and images cannot be reviewed by ChatGPT. Lengthy articles need to be reviewed in parts by ChatGPT as the entire article will not fit in one chat/response. The benefits consist of reduction in time needed by journals to review the articles submitted to them, as well as an AI assistant to give a different perspective about the research papers other than the human reviewers. In conclusion, this research contributes a groundbreaking foundation for incorporating ChatGPT into the pantheon of journal reviewers. The delineated guidelines distill key insights into operationalizing ChatGPT as a proficient reviewer within academic journal frameworks, paving the way for a more efficient and insightful review process.

Potential Use of Chat GPT in Global Warming.

Climate change is a major global challenge that requires the integration of many different scientific disciplines, including atmospheric science, oceanography, and ecology. The complexity and scale of the problem require sophisticated tools and techniques to understand, model, and project future climate conditions. Artificial intelligence and natural language processing technologies, such as ChatGPT, have the potential to play a critical role in advancing our understanding of climate change and improving the accuracy of climate projections. ChatGPT can be used in a variety of ways to aid climate research, including in model parameterization, data analysis and interpretation, scenario generation, and model evaluation. This technology provides researchers and policy-makers with a powerful tool for generating and analyzing different climate scenarios based on a wide range of data inputs, and for improving the accuracy of climate projections. The author acknowledges asking chatGPT questions regarding its uses for Climate Change Research. Some of the uses that it states are possible now and some are potentials for the future. The author has analyzed and edited the replies of chat GPT.

Role of Chat GPT in Public Health.

ChatGPT, a language model developed by OpenAI, has the potential to play a role in public health. With its ability to generate human-like text based on large amounts of data, ChatGPT has the potential to support individuals and communities in making informed decisions about their health (Panch et al. Lancet Digit Health 1:e13-e14, 2019; Baclic et al. Canada Commun Dis Rep 46.6:161, 2020). However, as with any technology, there are limitations and challenges to consider when using ChatGPT in public health. In this overview, we will examine the potential uses of ChatGPT in public health, as well as the advantages and disadvantages of its use.

Prenatal diagnosis of fetal midgut volvulus: a case report.

BACKGROUND: Fetal midgut volvulus is an uncommon yet potentially life-threatening condition. Prenatal diagnosis may pose a challenge, due to the paucity of specific signs and symptoms. Timely prenatal diagnosis of this condition is imperative to prevent fetal mortality and morbidity. CASE PRESENTATION: We present a rare case report of fetal midgut volvulus, malrotation, and intestinal obstruction at 32 weeks of gestation in a 31-year-old multigravida Indian patient who presented with decreased fetal movements. Fetal ultrasound revealed midgut volvulus with proximal bowel obstruction and polyhydramnios. The patient underwent emergency surgery, which revealed intestinal malrotation and confirmed the diagnosis of midgut volvulus. Untwisting of the volvulus was done followed by Ladd's procedure. Follow-up postoperative ultrasound was unremarkable. CONCLUSIONS: Delay in the diagnosis of fetal midgut volvulus leads to poor fetal and maternal outcomes. Hence, it is vital for radiologists, sonologists, and obstetricians to be aware of this condition while performing fetal sonography. Prompt diagnosis and surgical intervention are vital to reduce the morbidity and mortality associated with this condition.

Scurvy in A Malnourished Child: Atypical Imaging Findings.

Scurvy, a disease caused by a severe lack of vitamin C in the diet, is most often associated with 17th-century sailors. Its 21st-century manifestation is a disease of the poor, sick, and those living in remote rural neighborhoods in which fresh, nutritious food is hard to come by. It is caused by a deficiency of Vitamin C and is rare in the United States. We describe the radiographic and MRI findings of a case of scurvy in a child with Noonan syndrome who is a "picky eater". MRI is rarely performed in scurvy as its radiographic findings are generally well known and sufficient for a diagnosis. However, due to the rarity of the disease in the US, MRI features of scurvy have been described in only a few case reports, to date. The rarity of this disease also causes scurvy to be kept lower, if at all in the differential diagnosis list.

Current Status of Radiology Education Online: a Comprehensive Update.

The purpose of this study is to evaluate various online resources available for radiology education. An online search was conducted using PubMed (National Library of Medicine, Bethesda, MD) and Google Scholar for publications discussing the applications of online learning in radiology. The search strategy employed a combination of the following terms: radiology, web-based conferencing, radiology education seminars, radiology education online, radiology education programs, online lectures, radiology residency, radiology degree, Radiology-Integrated Training Initiative (R-ITI) e-learning platform, UTAUT, Moodle, active image-based learning, Video conference platforms (VCPs), education, undergraduate, medical students, teaching, virtual learning, blended learning, e-learning, COVID-19, pandemic, OER, open education resources, online learning, course assets, accessibility, 5G Internet, game-based learning, radiology competition, and virtual world. The literature published was reviewed and consolidated. Data from the literature shows that radiology education online and radiology education seminars are undergoing a revolution due to advancements in computers, online software, and 5G Internet speed. The pace of this development has accelerated even further due to the COVID-19 pandemic and thus forced distance online education. Various technologies are available and are being implemented by residency programs across the world to improve radiology education, making it more interactive and safer in this pandemic. Online learning has become an integral part of education in radiology, with new facets being added to it.

COVID-19 and pulmonary tuberculosis - A diagnostic dilemma.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Meanwhile, pulmonary tuberculosis(TB) is one of the most common infective lung diseases in developing nations. The concurrence of pulmonary TB and COVID-19 can lead to poor prognosis, owing to the pre-existing lung damage caused by TB. Case presentation: We describe the imaging findings in 3 cases of COVID-19 pneumonia with co-existing pulmonary TB on HRCT thorax. The concurrence of COVID-19 and pulmonary TB can be a diagnostic dilemma. Correct diagnosis and prompt management is imperative to reduce mortality and morbidity. Hence it is pertinent for imaging departments to identify and report these distinct entities when presenting in conjunction.