Prof. Liang Wang
Guangdong Provincial People’s Hospital/ Southern Medical University, China

Prof. Liang Wang currently holds tenured full professor and distinguished medical researcher positions at Guangdong Provincial People’s Hospital, a top-tier tertiary hospital at the national level, which is affiliated with the Southern Medical University and the South China University of Technology in China. He also serves as chief scientist and assistant director at the Laboratory Medicine Department of the hospital. Prof. Wang also holds adjunct research fellow positions at the University of Queensland and the University of Western Australia, and an adjunct professor position at Edith Cowan University. His research centers on advancing disease diagnosis via interdisciplinary methods such as intelligent medicine, digital health, and molecular biology. Prof. Wang has edited seven books and published over 130 peer-reviewed articles in esteemed journals such as The Lancet Microbe, npj Digital Medicine, and ISME J. Prof. Wang frequently presents at prestigious conferences like EuroCarb (Poland, 2025), ICID (South Africa, 2024), CHRO (Australia, 2024) and VAAM (Germany, 2023). He also serves as an editorial board member for multiple international journals, including Journal of Translational Medicine (Associate Editor, Computational Modelling and Epidemiology) and BMC Microbiology (Editor of Distinction Awards, 2025). He is the recipient of the American Chemical Society’s Rising Star Award in measurement science and the Australia-China Helicobacter Research Fellowship.

Speech Title: "Application of Artificial Intelligence in Medical Laboratories: Opportunities and Challenges"

Abstract: Intelligent medicine driven by artificial intelligence (AI) is transforming medical laboratories by enhancing diagnostic accuracy, efficiency, and scalability. AI-driven tools enable rapid analysis of complex datasets, such as genomic profiles and pathology images, improving early disease detection and personalized treatment strategies. Automation of routine tasks reduces human error and frees laboratory personnel for higher-value work. Predictive analytics also optimizes resource allocation and laboratory workflows. However, challenges persist, including data privacy concerns, integration with existing systems, and the need for standardized protocols to ensure reliability. Ethical considerations, such as bias in AI algorithms and equitable access, remain critical. Additionally, regulatory hurdles and the high cost of implementation can limit adoption, particularly in resource-constrained settings. Addressing these challenges through interdisciplinary collaboration and robust policy frameworks will unlock the full potential of artificial intelligence and revolutionize laboratory medicine.