Clinical application of multidisciplinary teams in tumor therapy

Multidisciplinary team （MDT） model is a diagnostic and treatment model characterized by interdisciplinarity, integration, centralism, individualization, and precision and is becoming more common in the management of complex malignancies. MDT emphasizes team spirit and a personalized treatment strategy according to the actual condition of each patient. A cooperative and effective multidisciplinary team is an important guarantee for delivering high-quality services to patients. Under the guidance of a medical humanistic concept, MDT provides reasonable, effective, convenient, and a full range of excellent quality medical service to patients. The MDT maximizes patient benefits, and it is the developmental direction for large-scale general hospitals. At the same time, the MDT is also an important measure to strengthen the core competitiveness of hospitals. Here, we introduce the clinical application of the model in tumor therapy as well as the current state and development in our hospital.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.