Akshay Chaudhari: LLMs for Clinical Text Summarization

Maciej Mazurowski is an Associate Professor of Radiology, Electrical and Computer Engineering, Biostatistics and Bioinformatics, and Computer Science at Duke University. He is also the Scientific Director of the Duke Center for Artificial Intelligence in Radiology and the Director of Radiology Imaging at Duke AI Health. His interest includes domain adaptation and generalization, anomaly detection, and class imbalance in Medical Deep Learning.

Professor Yiyu Shi, is a professor of Computer Science and Engineering at the University of Notre dam, USA. He is also a visiting scientist at Boston Children’s Hospital.  His research focuses on sustainable computing. The importance of resource awareness in artificial intelligence for healthcare

This episode is about our take on how to review well in MICCAI and related conferences, both for the reviewers and the meta reviewers.

Hitesh Dave is the Digital Pathology and AI Lead of Roche Diagnostics. Dr. Dave is a Global Change Leader, Chartered Scientist and Strategic management consultant. He will talk about how Roche is restructuring itself for the age of digital and computational pathology. The views shared or expressed here at the podcast are from Hitesh's experience only, and not necessarily represent anything from Roche.

Saad Nadeem is an assistant attending computer scientist at Memorial Sloan Kettering Cancer Center with dual appointments in the Departments of Medical Physics and Pathology. He is also an adjunct assistant professor in the Department of Applied Mathematics & Statistics at Stony Brook University. Saad focuses on multimodal and multiscale analysis of biomedical data to improve patient outcomes. DeepLIIF

MICCAI Student Board Mission The MICCAI Student Board (MSB) represents student interests within the MICCAI society, and aims to stimulate the involvement of students within the MICCAI community. The MSB enhances student networking in the community, engages students in the MICCAI society, and advises the MICCAI board on matters of relevance for students. The MSB organises social and professional and student events during the MICCAI period, in collaboration with local organisers, and maintains the MICCAI Educational Initiative.

Islem Rekik is the Director of the Brain And SIgnal Research and Analysis (BASIRA) laboratory (http://basira-lab.com/) and an Associate Professor at Imperial College London (Innovation Hub I-X). Together with BASIRA members, she conducted more than 100 cutting-edge research projects cross-pollinating AI and healthcare —with a sharp focus on brain imaging and neuroscience. She is also a co/chair/organizer of more than 25 international first-class conferences /workshops /competitions (e.g., Affordable AI 2021-22, Predictive AI 2018-2024, Machine Learning in Medical Imaging 2021-24, WILL competition 2021-23). Dr Rekik has been awarded prestigious international research fellowships including the EU Marie-Curie Fellowship in 2019 and the TUBITAK 2232 for Outstanding Experienced Researchers during 2020-2022. In addition to her 150+ high-impact publications, she is a strong advocate of equity, inclusiveness and diversity in research. She is the former president of the Women in MICCAI (WiM), the co-founder of the international RISE Network to Reinforce Inclusiveness & diverSity and Empower minority researchers in Low-Middle Income Countries (LMIC) and a committee member of the AFRICAI network. She is in the organizing committee of MICCAI 2022 (Singapore), 2023 (Vancouver), 2024 (Marrakesh) and 2025 (South Korea). Graph Neural Networks in Network Neuroscience

Akshay Chaudhari is an Assistant Professor in the Integrative Biomedical Imaging Informatics at the Department of Radiology in Stanford University, USA. He leads the Machine Intelligence in Medical Imaging research group and has a primary research interest at the intersection of artificial intelligence and medical imaging. He also serves as the Associate Director of Research and Education at the Stanford AIMI Center. Adapted large language models can outperform medical experts in clinical text summarization

Prof. Rajendra Pratap Gupta is a leading public policy expert with over a decade of experience. Rajendra has worked with the World Economic Forum in the past & has served as an advisor to the Union Health Minister of India. He is focusing on designing digital health policies to make healthcare AI-ready.

Prof. Hamid Tizhoosh explores the applications of artificial intelligence (AI) in medicine, particularly in medical image analysis and cross relations to other patient data such as molecular, laboratory and textual data. His research is currently focused on search and matching in archives of patient data. Foundation Models for Histopathology—Fanfare or Flair Creating an atlas of normal tissue for pruning WSI patching through anomaly detection