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This article discusses the role of human involvement in enhancing the performance of large language models (LLMs) in medical inquiry. The authors highlight the occasional shortcomings of LLMs in providing accurate citation information and accessing real-time data. They recommend prompt engineering as a way to enhance model performance, which involves carefully crafting instructions or queries given to LLMs to elicit specific and desired responses. The article also discusses the importance of verifying LLM outputs and acknowledges the limitations of LLMs in medical diagnoses and personalized advice. The authors conclude that the judicious implementation of the "human-in-the-loop" strategy, with a focus on prompt engineering, can greatly improve LLM capabilities in medical inquiry. [Extracted from the article]

Deep learning has led to significant progress in the fault diagnosis of mechanical systems. These intelligent models often require large amounts of training data to ensure their generalization capabilities. However, the difficulty of obtaining turbine rotor fault data poses a new challenge for intelligent fault diagnosis. In this study, a turbine rotor fault diagnosis method based on the finite element method and transfer learning (FEMATL) is proposed, ensuring that the intelligent model can maintain high diagnostic accuracy in the case of insufficient samples. This method fully exploits the finite element method (FEM) and transfer learning (TL) for small-sample problems. First, FEM is used to generate data samples with fault information, and then the one-dimensional vibration displacement signal is transformed into a two-dimensional time-frequency diagram (TFD) by taking advantage of the deep learning model to recognize the image. Finally, a pre-trained ResNet18 network was used as the input to carry out transfer learning. The feature extraction layer of the network was trained on the ImageNet dataset and a fully connected layer was used to match the specific classification problems. The experimental results show that the method requires only a small amount of training data to achieve high diagnostic accuracy and significantly reduces the training time. [ABSTRACT FROM AUTHOR]