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.

Objective:To investigate the impact of donor human leukocyte antigen (HLA) -Bw4 expression on natural killer (NK) cell reconstitution and transplant outcomes in recipients undergoing haploidentical hematopoietic stem cell transplantation (HSCT) from maternal or related donors without ex vivo T-cell depletion.Methods:This study prospectively enrolled 32 patients who received T-replete haploidentical HSCT from maternal or collateral donors (cohort 1) to evaluate the facilitating effect of donor HLA-Bw4 expression on NK cell reconstitution. Furthermore, a retrospective analysis was conducted on 278 patients who underwent T-replete haploidentical HSCT from maternal or collateral donors (cohort 2) to analyze the impact of donor HLA-Bw4 expression on HSCT outcomes. Thus, a comparison was made between the effects of donor HLA-Bw4 expression on HSCT outcomes in patients receiving or not receiving post-transplant cyclophosphamide (PT-Cy) conditioning.Results:Donors expressing HLA-Bw4 alleles facilitated NK cell reconstitution and functional recovery, which remained unaffected by PT-Cy. Donors with HLA-Bw4 expression were associated with reduced transplant-related mortality (TRM), particularly mortality related to infections. The use of PT-Cy did not impact the ability of donor HLA-Bw4 to decrease TRM.Conclusion:In haploidentical HSCT from maternal or related donors without ex vivo T-cell depletion, the presence of donor HLA-Bw4 expression promotes rapid NK cell reconstitution and functional recovery and is significantly associated with lower TRM, especially infection-related mortality. These findings underscore the clinical significance of donor HLA-Bw4 expression in patients who underwent HSCT. Hence, the consideration of donor HLA-Bw4 in recipient selection and HSCT strategies holds important clinical implications.

Objective To systematically evaluate the efficacy and safety of electroacupuncture(EA)in the treatment of postoperative nausea and vomiting(PONV)after gynecological surgery.Methods PubMed,Cochrane Library,Web of Science,Embase,China national knowledge infrastructure(CNKI),Wanfang database,and China biomedical literature database(CBM)were systematically searched.The re-trieval period was from the establishment of the database to December 2022.Relevant randomized controlled trials on EA for the treatment of PONV in gynecological surgery were collected.RevMan 5.3 software was used for meta-analysis.Results Fourteen randomized controlled trials were accommodated,including 958 patients,477 patients in the EA group and 481 patients in the control group.Compared with the control group,the incidence of PONV was significantly lower in group EA at 0-48 hours postoperatively(RR=0.55,95％CI 0.47 to 0.65,P＜0.001),and the PONV scores were significantly lower in the postopera-tive period within 48 hours in group EA(MD=-0.40 scores,95％CI-0.65 to-0.16 scores,P=0.004),the incidence of postoperative remedial antiemetic were significantly lower(RR=0.28,95％CI 0.16 to 0.51,P＜0.001).Conclusion EA can reduce the incidence of PONV and the incidence of re-medial antiemetic after gynecologic surgery.

Objective To systematically evaluate the effect of transcutaneous electrical acupoint stimulation(TEAS)in the treatment of postoperative nausea and vomiting(PONV)after laparoscopic non-gastrointestinal surgery.Methods Databases such as PubMed,Cochrane library,Web of Science,Embase,CNKI,Wanfang,and Chinese biomedical database(CBM)were searched to find and screen ran-domized controlled trials(RCTs)of TEAS in the prevention and treatment of PONV after laparoscopic non-gastrointestinal surgery.The retrieval time was from the establishment of the database to July 2023.Meta-a-nalysis was performed using RevMan 5.3 software.Results Twenty-two RCTs involving 3 538 patients were included,including 1 799 in the TEAS group and 1 739 in the control group.The results of meta-analysis showed that the total incidence of PONV in the TEAS group was significantly lower than that in the control group 0-24 hours after operation(RR=0.54,95％CI 0.44-0.68,P＜0.001),and the incidence of postoperative remedial antiemetic was significantly reduced(RR=0.54,95％CI 0.38-0.77,P＜0.001).There was no significant difference in the incidence of postoperative acupoint stimulation-related adverse reactions between the two groups(RR=0.62,95％CI 0.15-2.51,P=0.500).Conclusion TEAS has good clinical efficacy and safety in the treatment of PONV after laparoscopic non-gastrointestinal surgery.

Purpose/Significance To explore the changes,challenges,key application scenarios and future development directions of generative artificial intelligence(AI)represented by ChatGPT in the field of hospital management,and to provide references for the ap-plication of AI natural language processing(NLP)technology in the field of hospital management in China.Method/Process Through literature review and analysis,the changes and challenges brought about by the rapid development of generative AI in the field of hospital management are sorted out,its key application scenarios and future development directions in the field of hospital management are empha-sized and explored.Result/Conclusion AI has broad application prospects in the field of hospital management,and it should focus on exploring its practical application scenarios and strategic directions to provide reference and guidance for promoting the high-quality de-velopment of public hospitals.

Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.

Objective To observe the value of spectral CT multi-parameter imaging for preoperative predicting lymph node metastasis(LNM)of gastric cancer.Methods Totally 136 patients with gastric adenocarcinoma were retrospectively enrolled.The patients were further divided into LNM group(n=74)and non-LNM group(n=62)according to postoperative pathological findings of lymph nodes status.Clinical data,conventional CT findings and spectral CT parameters were compared between groups.Factors being significant different between groups were included in multivariate logistic regression analysis to screen independent predictors of gastric cancer LNM.Clinical+conventional CT model(model 1),spectrum CT model(model 2)and combined model(model 3)were constructed based on the above independent predictors,respectively.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for preoperative predicting LNM of gastric cancer.Results CT-N stage,CT-T stage,70,100 and 140 keV CT valuestumor at arterial phase(AP),arterial enhancement fraction(AEF)and normalized iodine concentration at venous phase(NICVP)were all independent predictors of gastric cancer LNM(all P＜0.05).AUC of model 3 was 0.846,higher than that of model 1 and model 2(AUC=0.767,0.774,Z=-0.368,-2.373,both P＜0.05)for preoperative predicting LNM of gastric cancer,while the latter two were not significantly different(Z=-0.152,P=0.879).Conclusion Spectral CT multi-parameter imaging could effectively predict LNM of gastric cancer preoperatively.

Objective To observe the value of dual-energy CT(DECT)radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma(GAC).Methods Totally 254 patients with GAC were prospectively analyzed and divided into high-grade group(low differentiation GAC,n=88)and low-grade group(middle-high differentiation GAC,n=166)according to pathological results.The patients were also divided into training set(n=203,including 70 high-grade and 133 low-grade GAC)and verification set(n=51,including 18 high-grade and 33 low-grade GAC)at the ratio of 8∶2.Radiomics features were extracted and screened based on venous phase single-level(40,70,100 and 140 keV)DECT,and a multi-energy radiomics model was constructed to predict GAC classification.Univariate analysis and multivariate logistic regression were used to analyze clinical and CT features as well as DECT parameters in training set to construct a clinic-CT model.Then a combined model was constructed through combining clinic-CT model with radiomics model.The predictive efficacy of the models were evaluated,and the calibration degree of the combined model was assessed.Results The area under the curve(AUC)of clinic-CT model,multi-energy radiomics model and combined model was 0.74,0.75 and 0.78 in training set,and 0.73,0.77 and 0.78 in verification set,respectively.Except for AUC of combined model was higher than that of clinic-CT model in training set(P＜0.05),no significant difference of AUC was found among models in training set nor verification set(all P＞0.05).The calibration degree of combined model was good in both training set and verification set(both P＞0.05).Conclusion DECT radiomics combined with clinical and CT features could effectively predict differentiation degree of GAC.

Objective To observe the value of spectral CT parameters for evaluating T staging of advanced gastric cancer(AGC).Methods Totally 155 AGC patients were collected and divided into T2 stage(n=40)and T3/4a stage(n=115)according to postoperative pathology.CT values,water concentration(WC)and iodine concentration(IC)of AGC lesions on 40-140 keV arteriovenous phase single energy level images were measured,and the standardized IC(nIC)and spectral curve slopes k1 and k2 were calculated.Clinical variables and spectral quantitative parameters were compared between groups,and receiver operating characteristic curve was plotted,the area under the curve(AUC)was calculated to evaluate the value of each parameter and model for identifying T2 and T3/4a stage AGC.Results Tumor thickness,proportion of low differentiation degree,CT100kev,CT140kev,and WC values in T3/4a group were all significantly higher than those in T2 group(all P＜0.05).CT140keV of AGC lesions on venous phase images presented the highest discrimination efficacy among single parameters,with AUC of 0.782.AUC of clinical-arterial phase-venous phase model was 0.848,higher than that of clinical model and arterial phase model alone(both P＜0.05)but not significantly different compared with AUC of venous phase model(P＞0.05).Conclusion Spectral CT quantitative parameters,especially venous phase parameters could be used to effectively identify T stage of AGC.Multi-parameter combined models had higher diagnostic value.

Objective:To assess the quality of low-keV monoenergetic images using deep learning image reconstruction (DLIR) algorithm combined with dual energy CT (DECT) in gastric cancer and to compare them with images from the conventional adaptive statistical iterative reconstruction (ASiR-V) algorithm.Methods:In this cross-sectional study, DECT images of 31 gastric cancer patients in the First Affiliated Hospital of Zhengzhou University were prospectively collected from September 2022 to March 2023. The 55 keV monoenergy images were reconstructed using the DLIR algorithm at low-, medium-, and high-intensity levels (DLIR-L, DLIR-M, and DLIR-H) based on arterial phase and venous phase images, respectively. The 70 keV 40% mixing coefficient (ASiR-V40%) images were reconstructed using the ASiR-V algorithm. In the objective evaluation of images, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for both lesions and muscle were calculated across four sets of reconstructed images. In the subjective evaluation of images, scores were assigned to the overall image quality, lesion visibility, and diagnostic confidence for each set of reconstructed images. Comparisons of SNR and CNR between the 4 groups were made by One-way repeated-measures ANOVA or Friedman′s test. Comparisons of scores were made by Friedman′s test. The P value of pairwise comparison was adjusted using Bonferroni correction methods. Results:In the objective evaluations, CNR lesion, SNR lesion and SNR muscle were highest on the 55 keV DLIR-H images in the arterial and venous phases, and showed a gradually increasing trend on the 70 keV ASiR-V40%, 55 keV DLIR-L, DLIR-M, DLIR-H images ( P<0.05). In subjective evaluations, compared to the 70 keV ASiR-V40% images, overall image quality scores were numerically higher for the 55 keV DLIR-H ( P>0.05), similar or slightly worse for the 55 keV DLIR-M, and significantly lower for the 55 keV DLIR-L ( P<0.05). The lesion visibility and diagnostic confidence on the 55 keV DLIR reconstruction images were higher in both arterial and venous phases than 70 keV ASiR-V40% images ( P<0.05). Conclusions:Compared to the conventional 70 keV ASiR-V40% images, the 55 keV DLIR-H images had higher lesion contrast and diagnostic confidence with lower image noise. The 55 keV DLIR-M images had comparable overall image quality to 70 keV ASiR-V40% images, but the former had higher lesion contrast and diagnostic confidence. The 55 keV DLIR-L was unable to improve image quality to the level of 70 keV ASiR-V40%.