On May 13, the School of Computer Science and Technology at Huazhong University of Science and Technology (HUST), in collaboration with the Centre for Big Data Research in Health (CBDRH) at the University of New South Wales (UNSW), successfully hosted the lectures titled "Scalable Computing Systems Global Forum 2025: Bridging AI and Clinical Practice China-Australia Symposium and HUST-UNSW In-depth Dialogue on AI+Healthcare." These events featured esteemed speakers from UNSW, including Louisa Jorm, founding director of the CBDRH, Oscar Perez Concha, researcher and senior lecturer, and Leibo Liu, researcher and data scientist at the George Institute for Global Health (TGI). They engaged in in-depth discussions with HUST faculty and students on the theme of "AI + Health." The symposium was attended by Xuanhua Shi, Vice Dean of the School, and Peng Liu, Deputy Secretary of the Party Committee, with proceedings moderated by Associate Professor Feng Lu.
The morning session commenced in Room 210 of the East Building No.5 at HUST, where Vice Dean Xuanhua Shi extended warm appreciation for the three UNSW experts. "This forum transcends borders and disciplines," Shi declared, positioning these events as both an academic exchange and a convergence of global perspectives. Highlighting early breakthroughs from the universities' joint seed fund launched in 2024—particularly in intelligent ECG analysis—he envisioned the gathering as a springboard for sustained cross-disciplinary collaboration. Shi further advocated exploring innovative approaches to AI healthcare and data ethics through continued transregional partnerships.
Peng Liu emphasized the School's commitment to interdisciplinary research and international collaboration, particularly in translating AI and health research into clinical practice. He reflected on the School's exploration of intersectional research in "Artificial Intelligence and Medicine," noting the invaluable insights and international perspectives brought by the UNSW experts. Liu anticipated that both institutions would use this lecture as a starting point to expand multi-level cooperation in research, education, and talent development, addressing global challenges in medical data governance and algorithmic clinical adaptation.
Professor Louisa Jorm presented “Beyond the Algorithm: Grounding Health and Medical Al in Clinical Reality.” She stressed the importance for researchers to fully recognize the complexity of the healthcare ecosystem, moving beyond a narrow focus on AI model performance to a deeper understanding of the biological mechanisms of diseases and clinical treatment pathways. Jorm analyzed factors impacting the coverage and accuracy of AI applications in healthcare, including variations in healthcare systems across different countries and disparities in urban and rural development. Through real-world case studies, she warned that neglecting clinical details could undermine model generalization and pose risks to patient safety, asserting that interdisciplinary collaboration is key to achieving safe and effective innovations in AI healthcare.
In "Unlocking Unstructured Health Data: From Challenges to Breakthroughs—Part I", Dr. Oscar Perez Concha introduced how to convert non-instructive data into structured formats, showcasing the latest applications of natural language processing (NLP) in automating clinical record summarization and coding. Dr. Concha emphasized that every line of data in the health AI field represents a living individual. He noted that enhancing the efficiency and accuracy of electronic health records is central to the digital transformation of healthcare. His team is tackling the challenges of processing unstructured data using NLP technology, aiming to streamline clinical workflows.
Dr. Leibo Liu focused on the challenges faced when analyzing unstructured data in "Unlocking Unstructured Health Data: From Challenges to Breakthroughs – Part II." He identified three main issues: balancing patient privacy with data accuracy, the inherent complexity of the data, and difficulties in data integration. Dr. Liu stressed that achieving data sharing while safeguarding patient privacy is essential for unlocking the value of big health data. He pointed out that the combination of NLP and deep learning is paving new pathways for mining unstructured data.
The afternoon session featured an in-depth roundtable discussion held in Room 438 of the South Building No.1. Three distinguished mentors from UNSW engaged with HUST students and faculty on "AI + Health."
During the session, Peng Liu remarked that the morning lectures showcased the dynamic integration of AI technology with healthcare. He highlighted breakthroughs in intelligent diagnostic algorithms for medical imaging and innovations in health data governance, underscoring the importance of interdisciplinary and cross-border collaboration. He hoped that participants would engage in multidimensional dialogues that transcend a purely technical perspective, reflecting on the ethical responsibilities behind each code iteration.
The exchange fostered a vibrant atmosphere, with participants discussing key topics, including the balance between patient privacy and data sharing, challenges in data integration, synthetic data generation, and strategies for addressing data imbalance. In terms of model applications, attendees explored the use of NLP and large language models in medical text analysis, sharing insights on model generalization and external validation. Additionally, discussions covered cutting-edge topics such as the application of blockchain in protecting medical privacy and the development of clinical and text summarization technologies.
This successful event marks a significant advancement in the collaboration between the two institutions in the "AI + Health" domain. The lectures and discussions not only established a platform for knowledge exchange but also sowed the seeds for new international research initiatives. In the future, HUST will continue to promote the construction of open and shared research platforms, cultivating interdisciplinary talent with a global perspective. The university aims to deepen cooperation with UNSW in areas such as intelligent computing systems, health big data analysis, and data ethics, facilitating the transition of "AI + Health" technologies from the laboratory to clinical practice—ultimately contributing to improved public health efficiency and healthcare quality.
