With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

To investigate the effectiveness of a self-developed intelligent medical record writing assistant in enhancing the efficiency of discharge record writing and improving the quality of discharge records, and to assess physicians' satisfaction with the assistant.

Objectives:To examine the feasibility of using foundation model in computer vision for echocardiographic left ventricular ejection fraction measurement. Methods:Based on the most extensive publicly accessible repository of echocardiographic loops,EchoNet-Dynamic,featuring 10024 recordings from individual patients,a foundation model in computer vision,VideoMAE V2,was fine-tuned,validated,tested using 7460,1288,and 1276 echocardiographic loops,respectively. Results:The mean absolute error between left ventricular ejection fraction measurements of VideoMAE V2 and expert's measurements was 3.94％ (95％CI:3.79％-4.11％).The Pearson's correlation coefficient was 0.91 (95％CI:0.89-0.92).Additionally,VideoMAE V2 demonstrated exceptional accuracy in identifying patients with a left ventricular ejection fraction below 50％,achieving an AUC of 0.96 (95％CI:0.95-0.97). Conclusions:This study validates the feasibility of using foundation model in computer vision for measuring left ventricular ejection fraction in echocardiographic loops and lays the foundation for the development of a generalized multimodal automated interpretation system for echocardiography.

Objective To study the association between serum total IgE level and β2-adrenoreceptor polymorphism (β2-AR) in asthmatic patients. Methods ELISA was used to determine serum total IgE and AS-PCR was used to determine β2-adrenoreceptor polymor- phism in 44 asthmatic patients. Results In β2-AR 16 locus genotype, the distribution frequencies of Arg/Arg, Gly/Gly showed increasing tendency, whereas Arg/Gly showed decreasing tendency, in normal serum total IgE group and increased serum total IgE group. But there was no significant difference between this two groups. In β2- AR 27 locus genotype, the distribution frequency of Gln/Gln genotype accounts for 76.2% and Gln/Glu genotype for 19.0% in increased serum total IgE group, while Gln/Gln genotype accounts for 9.0% and Gln/Glu geno- type for 73.9% in normal serum total IgE group. There was significant difference between two groups ( P<0.01 ). Conclusions Serum total IgE was correlated with β2-AR 27 locus genetic polymorphism, which may contribute to understand the mechanism of asthma in the peo- ple of the Han nationality in GuiZhou.