［Abstract］　The integration of brain organoids, microelectrode array, organ-on-a-chip and artificial intelligence(AI) has gradually transformed in vitro neural tissues from static culture models into experimental systems capable of recording, stimulating and providing feedback. Brain-on-a-chip interface(BoCI) refers to a technical platform that takes two-dimensional neuronal networks, brain slices or three-dimensional brain organoids as biological units and microelectrode arrays, optogenetic stimulation, microfluidics and algorithmic models as interfaces to establish bidirectional interactions between in vitro neuronal networks and external tasks. This paper is intended for clinical medical readers and provides an overview of the conceptual boundaries, biological basis, chip interfaces, AI-assisted models, application scenarios, and ethical governance of BoCI. At present, BoCI is more suitable as a preclinical tool for function readout of neuronal networks, validation of disease mechanisms and drug screening, rather than being directly equated with models of consciousness or clinical decision-making systems. The future development of BoCI requires simultaneous advancement in standardized culture, low-damage interfaces, trustworthy AI and patient data governance.]]> 2026/7/31 10:41:33 1, Ma Lingfei¹, Jiang Nan², Gou Shuangquan¹, Liu Shan³, Jiang Cheng¹, Li Chenzhong¹]]> Wang Yifan¹, Ma Lingfei¹, Jiang Nan², Gou Shuangquan¹, Liu Shan³, Jiang Cheng¹, Li Chenzhong¹ 4true ［Abstract］　Large language models(LLMs) are widely used in the medical field, but the problem of hallucinations seriously restricts their safe deployment in clinical practice.This paper systematically reviews the definition, classification, underlying mechanisms and mitigation strategies of the hallucinations of LLMs under medical scenarios. First, a classification system encompassing “factuality” and “faithfulness” is constructed, and a bias transmission mechanism spanning data, architecture, algorithms and interaction is analyzed. Second, clinical scenarios such as medical question-answering, medical record generation and multimodal tasks are integrated to analyze specific manifestations of hallucinations, including factual contradiction and evidence fabrication. On this basis, the mainstream detection and mitigation strategies such as data governance, retrieval-augmented generation, factual alignment and uncertainty quantification are summarized. Finally, a human-AI collaborative governance mechanism is proposed, aiming to provide a reference for advancing the safe and trustworthy deployment of LLMs in clinical practice.]]> 2026/7/31 10:41:33 1,2, Qi Xinglun³, Yang Dagan³]]> Shen Feng^1,2, Qi Xinglun³, Yang Dagan³ 3true ［Abstract］　Objective　To explore the application value of an artificial intelligence(AI)-based differential evolution algorithm-driven patient-based real-time quality control(PBRTQC) intelligent monitoring platform in quality management of 10 clinical chemistry and immunology assays. Methods　Based on the PBRTQC intelligent monitoring platform, the test results of 10 clinical chemistry and immunology assays were automatically collected from the Department of Clinical Laboratory of the People′s Hospital of Guangxi Zhuang Autonomous Region from January 2023 to December 2023. The differential evolution algorithm was adopted to optimize the computational parameters of the exponentially weighted moving average(EWMA) procedure, including batch size, step length, truncated bounds and control limits. The resultant model was validated using data collected from January 2024 to May 2024, and its performance was subsequently evaluated under real-world operating conditions from June 2024 to October 2024. The application value of the PBRTQC intelligent monitoring platform in the quality control of the 10 assays was evaluated by using relevant quality control rules. Results　The optimal procedure-related parameters were selected through the PBRTQC intelligent monitoring platform. In the testing of the 10 assays, the PBRTQC intelligent monitoring platform consistently identified and promptly flagged systematic performance disturbances arising from insufficient reagent volumes, prolonged reagent exposure after the reagent bottles were opened, or carryover contamination from the stirring rods, demonstrating reliable error detection with timely alerting function. Conclusion　The PBRTQC intelligent monitoring platform can monitor the quality risk of detection of 10 clinical chemistry and immunology assays in real time and accurately identify systematic errors.]]> 2026/7/31 10:41:33 1, Su Hangjiu¹, Huang Xiuli¹, Yang Wenhui¹, Huang Xiong², Xiao Yu¹, Liang Li¹, Chen Jin¹, Yuan Yulin^1,2*, Ning Leping¹]]> Luo Changliang¹, Su Hangjiu¹, Huang Xiuli¹, Yang Wenhui¹, Huang Xiong², Xiao Yu¹, Liang Li¹, Chen Jin¹, Yuan Yulin^1,2*, Ning Leping¹ 2true ［Abstract］　Objective　To analyze the pathogenic distribution characteristics of nosocomial infections in patients with gynecological malignant tumors and to construct a predictive model for infections caused by the predominant causative agent, Escherichia coli, thereby providing a basis for clinical anti-infection treatment and risk assessment. Methods　A retrospective analysis was conducted on the clinical data of 146 patients who developed infections within 1 week after gynecological tumor surgery and during hospitalization for chemotherapy in Anhui Provincial Cancer Hospital from May 2020 to January 2022. The distribution characteristics of the pathogenic microorganisms causing infections, the drug resistance of the predominant causative agent, Escherichia coli, and the influencing factors of the infections were analyzed, and a predictive model was constructed. Results　A total of 180 strains of pathogenic microorganisms were isolated, with mid-stream urine samples accounting for the highest proportion(65.56%, 118/180). Gram-negative bacilli accounted for 80.56%(145/180) of all the isolates, and Escherichia coli accounted for 70.34%(102/145) of the Gram-negative bacilli and 56.67%(102/180) of the total pathogenic microorganisms. Antimicrobial susceptibility testing results showed that Escherichia coli exhibited resistance rates to ceftriaxone, ciprofloxacin, trimethoprim/sulfamethoxazole and levofloxacin exceeding 55%, while the resistance rates to imipenem, ertapenem, piperacillin/tazobactam, nitrofurantoin and amikacin were less than 10%. Across all the sample types, univariate logistic regression analysis revealed that Escherichia coli infection was only significantly associated with mid-stream urine samples(P=0.004). Therefore, the subsequent analyses were restricted to the mid-stream urine samples. In the mid-stream urine samples, multivariate logistic regression analysis showed that Escherichia coli infection might be associated with tumor type, white blood cell count(WBC), lipoprotein a(Lpa), urinary nitrite(UNIT), and bacterial count in urine(P<0.1). Based on the above 5 variables, several machine learning methods were adopted to construct assessment models for Escherichia coli infection. The results showed that the decision tree and logistic regression models demonstrated stable performance. For the decision tree model, area under the curve(AUC) was 0.838 in the training set and 0.818 in the test set. For the logistic regression model, AUC was 0.775 in the training set and 0.737 in the test set. Furthermore, the calibration curves and decision curve analysis(DCA) in both models demonstrated excellent performance. Conclusion　Nosocomial infections in patients with gynecological malignant tumors are predominantly caused by Gram-negative bacilli in the urinary tract, with Escherichia coli being the dominant pathogen. The decision tree and logistic regression models built on tumor type, WBC, Lpa, UNIT and bacterial count in urine show excellent performance in identifying Escherichia coli infection in mid-stream urine samples. The two models facilitate early identification of high risk patients and guide targeted antimicrobial therapy, which are of great value in reducing antibiotic resistance and improving anti-infection outcomes.]]> 2026/7/31 0:00:00 1,2, Peng Wenju³, Xu Xinxin⁴, Kan Jinsong^1,2, Zhang Jiayun^1,2, Chen Jiming⁵]]> Shan Wulin^1,2, Peng Wenju³, Xu Xinxin⁴, Kan Jinsong^1,2, Zhang Jiayun^1,2, Chen Jiming⁵ 1true