Clinical Day

The University of South Dakota (USA)

Abstract: AI and clinical medicine are often portrayed as natural partners, yet their collaboration remains challenging. Drawing on NIH experience and extensive AI research, this talk explores why technically sophisticated models frequently fail in real clinical settings. It highlights mismatches in data assumptions, explainability, accountability, and workflow integration that fuel distrust between clinicians and AI scientists. While much effort focuses on building robust models, clinical relevance is often overlooked. Models trained on narrow or homogeneous datasets struggle to generalize, emphasizing the need for cross-population training and testing to ensure applicability across diverse patient populations. Using examples from medical imaging and precision medicine, the talk shows how algorithmic success alone does not guarantee clinical impact. True progress requires reframing AI as a collaborative tool that augments human expertise — human-in-the-loop machine learning (active learning). The session concludes with pathways toward human-centered, interpretable, and population-aware AI systems. Take-home message: learning, not just limited to training, drives meaningful clinical impact.

Biography: Prof. KC Santosh is the Chair of the Department of Computer Science (since 2020) and founding director of the USD AI Research (since 2015, https://ai-reseach-lab.org)) at the University of South Dakota (USD). He served as Graduate Program Director for seven years (2017–24) and was previously a research fellow at NIH and a PostDoc at INRIA (France). With over $8.7 million in funding (DOD, NSF, and ED, to name a few), he has authored 12 books and 290+ peer-reviewed research articles (such as IEEE TPAMI, IEEE TAI, and IEEE TMI) as well as delivered 90+ keynote talks, including TEDx talk. He serves as an associate editor for multiple prestigious journals such as IEEE Trans on AI, IEEE Trans of Medical Imaging and Int J of Machine Learning &Cybernetics, chaired multiple premier conference events such as IEEE Conference on AI, IEEE Conference on Cognitive Machine Intelligence and IEEE CBMS, and leads multiple review panels such as NSF and Mitacs (Canada). He is a trained leader, having completed leadership programs such as Deans/Chairs 1.0 (CCAS, Spring 2021), Deans/Chairs 2.0 (CCAS, Summer 2024), and the President Executive Leadership Institute (USD, 2021/22). He leads AI+x initiative that primarily includes curriculum innovation, outreach and visibility (USD AI Symposium, IEEE), and interdisciplinary research collaboration (e.g., South Dakota Biomedical Computation Collaborative – supported by a $6.5M award from the U.S. Department of Education). He brings extensive experience in curriculum innovation—particularly in interdisciplinary initiatives and shared governance (e.g., physics, business analytics, psychology, biology, and biomedical engineering, etc.)—as well as in program assessment and evaluation (as PEV), including ABET accreditation during the 2016/17 and 2022/23 cycles. His leadership has driven a 3,000% growth in AI enrollment at USD. His contributions have established USD as a pioneer in AI programs within the state of South Dakota. In fundraising, he secured a $2.0 million endowment promise to further strengthen the reputation of the department. He is a member of the NIST’s AI Standards and Innovation and a U.S. Speaker for AI education. For more info. https://kc-santosh.org/.

Case Western Reserve University (USA)

Abstract: Chemical contrast agents have long been integral to clinical diagnostic imaging. However, growing concerns about their safety, cost, and environmental impact have prompted the need for alternative solutions. In this talk, Dr. Shuo Li will present his pioneering work on AI contrast-enhanced imaging. This transformative approach leverages cutting-edge machine learning techniques to synthesize contrast-enhanced images without chemical agents. This innovative technology reduces patient risk and healthcare costs and opens new frontiers for precision imaging. Dr. Li will showcase recent breakthroughs from his lab, highlight clinical applications across cardiology, oncology, and neurology, and discuss the future potential of AI-driven imaging in reshaping medical diagnostics.

Biography: Dr. Li is a global leader in conducting multi-disciplinary research to enable artificial intelligence (AI) in healthcare. He is a Leonard Case, Jr. endowed professor at Case Western Reserve University (USA). Before that, he was an associate professor at Western University (Canada) and a scientist at the Lawson Health Research Institute. He was a scientist at GE Healthcare (2006-2015). He is a committee member in multiple highly influential conferences and societies. He is most notable for serving on the prestigious board of directors in the MICCAI society (2015-2024), where he is also the general chair for the MICCAI 2022 conference, which is the most influential AI-in-imaging conference. He has over 300 publications, acted as the editor for six Springer books, and serves as an associate editor for several prestigious journals in the field. Throughout his career, he has received several awards from GE, various institutes, and international organizations. He is a Fellow of SPIE, AAIA, IET, AIMBE, and IAMBE, and a member of the National Academy of Artificial Intelligence (NAAI).

Zhejiang University

Abstract: Pathology is the “gold standard” for cancer diagnosis. However, pathological diagnosis in clinical practice is highly subjective and heavily reliant on the expertise of pathologists, leading to issues such as missed diagnoses and misdiagnoses. The long training cycle for pathologists, the significant workforce shortage, and the uneven distribution of medical resources further exacerbate the challenges in cancer diagnosis and treatment. AI offers a powerful solution to this dilemma by significantly improving the efficiency, accuracy, and objectivity of cancer diagnosis. Driven by clinical needs in pathology, we have conducted a series of studies on AI-assisted diagnosis and therapy, following the key stages of cancer screening, diagnosis, and prognosis. This work is further integrated with the discovery of diagnostic and prognostic biomarkers to help unravel the mechanisms of cancer initiation and progression. This talk will present our recent advances, focusing on large models for pathological diagnosis and prognostic analysis, and discuss their translation into real-world clinical scenarios.

Biography: Dr. Zunlei Feng is an Associate Professor and Ph.D. Supervisor at Zhejiang University. His primary research interests encompass Artificial Intelligence, AI in Medicine, Computational Pathology for Intelligent Diagnosis and Therapy, and Multi-Omics Data Integration. In the past five years, he has authored over 100 research papers, with more than 60 published in top-tier international journals and conference proceedings. His work has been recognized with awards including the Best Paper Award at the IEEE VCIP conference. Dr. Feng has actively served the academic community as a Co-Chair for the 16th and 17th ICGIP International Conferences. Furthermore, he has led teams to achieve more than ten champion and runner-up awards in international AI competitions. The intelligent pathology diagnostic systems developed under his leadership have been successfully translated into clinical practice and deployed in multiple medical institutions.

The University of Hong Kong

Abstract: Computational pathology is transforming diagnostic practice by leveraging artificial intelligence to extract clinically relevant insights from Whole Slide Images (WSIs). The integration of multimodal AI offers new opportunities for building interpretable, accurate, and scalable diagnostic tools. In this talk, I will present our recent advances that demonstrate how incorporating domain knowledge and biological context can significantly enhance histopathological analysis. We will first introduce a knowledge-guided framework that integrates expert-derived knowledge into AI models, enabling more generalizable and clinically meaningful predictions across diverse cancer tasks. We then showcases a strategy for inferring cellular-level phenotypes directly from histology images, providing a cost-effective alternative to spatial transcriptomics for characterizing the tumor microenvironment and predicting patient outcomes. Together, these works reflect a shift toward more human-aligned, knowledge grounded AI systems for computational pathology.

Biography: Dr. Lequan Yu is an Assistant Professor at School of Computing and Data Science, The University of Hong Kong, and a former postdoctoral fellow at Stanford University. He received his Ph.D. and B.Eng. from The Chinese University of Hong Kong and Zhejiang University, respectively. His research focuses on medical AI, multimodal learning, and precision oncology. He has published over 100 papers, including Nature Communications, npj Digital Medicine, Cell Gemonics, and TPAMI, with 22,000+ Google Scholar citations. Dr. Yu was ranked by Clarivate Analytics in the top 1% of the citation list in 2023-2025 and won the MICCAI 2023&2024 Young Scientist Publication Impact Award Runner-Up. He serves as the Associated Editor of npj Digital Medicine, the Guest Associated Editor of TMI, and the Area Chair of NeurIPS, ICLR, CVPR, AAAI, and MICCAI.