报告摘要：

    Artificial Intelligence (AI) and Machine Learning (ML) are redefining medical decision support by turning complex health data into actionable insights. This presentation will showcase applications in medical image analysis and predictive modeling, including COVID-19 diagnosis, Alzheimer’s disease progression, hip therapy outcome prediction, influenza-like illness (ILI) forecasting, and digital twins for COPD management. We will explore how deep learning and longitudinal modeling enable earlier detection, personalized risk assessment, and optimized treatment strategies. Key challenges—such as data quality, interpretability, and clinical integration—will be discussed alongside strategies for ensuring trust and equity. By bridging predictive analytics with precision medicine, AI can accelerate the shift toward safer, more effective, and patient-centered healthcare.

专家简介：

    Prof. Hichem Sahli’s research team has made substantial contributions to AI-driven methodologies at the intersection of signal processing, computer vision, probabilistic modeling, and artificial intelligence. With a strong track record in interdisciplinary research, the team has developed innovative approaches in affective computing, multimodal emotion recognition, and computational medical diagnostics.

Drawing on deep expertise in audiovisual signal processing and machine learning, the team designs practical frameworks for real-world healthcare and societal challenges. Their work spans AI-enabled healthcare innovation, from personalized disease monitoring and precision medicine to digital mental health and infectious disease forecasting. By integrating multimodal sensor data with advanced learning architectures, they develop predictive models that support early diagnosis, risk assessment, and tailored treatment planning, particularly for neurodegenerative and affective disorders.

The team actively contributes to collaborative research efforts, helping to advance clinical decision support systems that assist healthcare professionals in optimizing triage, patient admissions, and care pathways. Their contributions to medical imaging, enable enhanced diagnostics through robust, interpretable AI-guided assessments.

Beyond healthcare, the team applies probabilistic modeling and signal analysis to affective computing, with research in speech, gait, and facial expression analysis supporting mental health applications and human-computer interaction.

Overall, the team’s work reflects a sustained commitment to advancing AI-driven healthcare and computational intelligence through careful, collaborative, and application-oriented research. Over his career, Prof. Sahli has supervised 31 PhD dissertations and 78 master's theses, many of which have led to high-impact publications and academic careers. His Joint International Research Group with Northwestern Polytechnical University, China (NPU) on Audio-Visual Signal Processing (AVSP) has been at the forefront of both foundational and translational research in health informatics, radar imaging, and behavioural analysis, with a particular emphasis on explainable AI for neurological and cognitive health.