We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language models such as StableLM, Dolly and LLaMA. It exhibits significant versatility in radiological diagnosis, research, and communication. This work serves as a catalyst for future developments in clinical NLP. The successful implementation of Radiology-GPT is indicative of the potential of localizing generative large language models, specifically tailored for distinctive medical specialties, while ensuring adherence to privacy standards such as HIPAA. The prospect of developing individualized, large-scale language models that cater to specific needs of various hospitals presents a promising direction. The fusion of conversational competence and domain-specific knowledge in these models is set to foster future development in healthcare AI. A demo of Radiology-GPT is available at https://huggingface.co/spaces/allen-eric/radiology-gpt.
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 architecture and further trained on a large dataset of radiology reports to generate coherent and clinically useful impressions from radiological findings. Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance compared to other generative language models, with a Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional assessments by radiology experts highlight the model's strengths in understandability, coherence, relevance, conciseness, and clinical utility. The work illustrates the potential of localized language models designed and tuned for specialized domains like radiology. When properly evaluated and deployed, such models can transform fields like radiology by automating rote tasks and enhancing human expertise.
As one of the future's most promising clean energy sources, solar energy is the key to developing renewable energy. The randomness of solar irradiance can affect the efficiency of photovoltaic power generation, which makes photovoltaic power generation planning extremely difficult. The main goal of this study is to accurately predict solar irradiance and establish a prediction model with meteorological characteristics to improve prediction accuracy. This paper proposes a convolutional neural network (CNN) and attention mechanism-based long short-term memory network (A-LSTM) to predict solar irradiance the next day. In addition, the prediction accuracy is further improved by combining similar day analyses. A similar day prediction model is constructed by selecting solar energy data from Andhra Pradesh, India. The experimental results show that the method proposed in this paper can predict solar irradiance more accurately, providing a new idea for photovoltaic power generation planning.
Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal‐systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs‐MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non‐HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. Level of Evidence 3 Technical Efficacy Stage 2
The differential diagnosis of superficial masses in the head and neck is broad and encompasses both benign and malignant soft-tissue tumors. Certain superficial masses of dermal origin do not fall under the World Health Organization classification for soft-tissue tumors but, nonetheless, present similarly and should be considered in the differential. Although many of these superficial masses cannot be definitively diagnosed on imaging alone, recognizing certain imaging patterns and ancillary clinical features may help narrow the differential diagnosis and distinguish benign and malignant lesions. The present article does not aim to provide a comprehensive review of all superficial head and neck masses but rather helps to organize the more common masses by cellular origin and provides an overview of pertinent demographics or risk factors to aid in informed decision-making.Learning Objective: To generate a differential diagnosis of head and neck superficial soft-tissue tumors based on clinical history and imaging features
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