Advertise on podcast: AI-ready Healthcare

https://www.linkedin.com/pulse/179b-year-gap-between-ai-acts-asks-anirban-mukhopadhyay-jda0e/

1. https://www.linkedin.com/pulse/structure-world-model-anirban-mukhopadhyay-njpde/ 2. SWoMo: https://ssharvienkumar.github.io/SWoMo/

Blogpost on broader design space: https://lnkd.in/p/dRU6tVuJNeural Tangent Kernel for Federated Learning: https://lnkd.in/p/dvC56MZhBMVC paper arxiv:https://arxiv.org/abs/2608.12108FedNCA paper:https://link.springer.com/chapter/10.1007/978-3-032-05185-1_17

Keynote of MICCAI26 Workshop CLiMeM

LinkedIn posts on Pathology AI 1. https://www.linkedin.com/pulse/payers-dont-pay-ai-pathology-tests-anirban-mukhopadhyay-sjyse/ 2. https://www.linkedin.com/pulse/pathology-ai-doesnt-pay-slide-reading-pathologist-mukhopadhyay-azvge/ 3. https://www.linkedin.com/pulse/big-pharmas-ai-pipeline-only-fast-preclinical-tox-mukhopadhyay-fanke/

1. Getting reimbursed for Radiology AI Screening: https://www.linkedin.com/pulse/getting-reimbursed-radiology-ai-screening-anirban-mukhopadhyay-6z5ve/ 2. Making Money with Mammography AI: https://www.linkedin.com/pulse/making-money-mammography-ai-anirban-mukhopadhyay-28yre/

1. Getting reimbursed for Radiology AI Screening: https://www.linkedin.com/pulse/getting-reimbursed-radiology-ai-screening-anirban-mukhopadhyay-6z5ve/ 2. Making Money with Mammography AI: https://www.linkedin.com/pulse/making-money-mammography-ai-anirban-mukhopadhyay-28yre/ 3. Payers don't pay for AI pathology. They pay for tests: https://www.linkedin.com/pulse/payers-dont-pay-ai-pathology-tests-anirban-mukhopadhyay-sjyse/

Jeya Maria Jose is a Senior Researcher at Microsoft Research. His research spans Computer Vision, Machine Learning, and NLP with an emphasis on Healthcare. In Microsoft Research, Jose is currently building multi-modal AI systems that harness large-scale real-world data to accelerate biomedical discoveries. GigaTIME Blog

On the 111th episode, our guest is Moritz Fuchs, a researchers from the Bosch Health Campus, Stuttgart Germany. Moritz Fuchs is an expert on Out-of-Distribution quantification in Medical Imaging.

Vamsi Velcheti is the Chief of Hematology and Oncology and Florida Department of Health Chair of Cancer Research at Mayo Clinic Florida, USA. An expert in lung cancer, Vamsi leads a multidisciplinary thoracic oncology program specializing in AI-driven predictive models,  biomarker-driven targeted therapies, immuno-oncology, and early-phase drug development.

Sara Moccia is an Associate Professor in University of Chieti – Pescara, Italy. Sara uses deep learning for the analysis of a wide range of medical images to support clinicians during the actual clinical and surgical procedures. AirCare

Hari Trivedi is an associate professor in Radiology and Imaging Sciences at Emory University, USA. Hari is the co-director of the Health Innovation and Translational Informatics (HITI) lab where he works on machine learning in radiology. Real-world performance evaluation of a commercial deep learning model for intracranial hemorrhage detection AI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment

Pierre Jannin is a legend from the MICCAI society for his research in Image-guided Surgery. Pierre is an INSERM Research Director, located in Rennes France. From 2012, he has been heading the Inserm Research group MediCIS. Pierre is designing and developing computer assisted surgery systems for 30+ years. Towards responsible research in digital technology for health care

Mohammad Yaqub is an Associate Professor in Mohammad Bin Zayed University of AI in Abu Dhabi. Mohammad works on problems in medical image analysis, radiomics and radiogenomics. He investigates continual learning, adversarial attacks and defense, and studies  healthcare challenges using natural language processing. Mohammad is the general chair of MICCAI 2026.

Nassir Navab is a professor of computer-aided medical procedures and augmented reality in TU Munich. His work involves developing technologies to improve the quality of medical intervention and bridges the gap between medicine and computer science. After studying mathematics and physics, computer engineering and systems control, Prof. Navab did his doctorate at INRIA / Paris XI. He then did two years of postdoctoral research at MIT Media Laboratory in Cambridge, USA. Prior to becoming a full professor at TUM in 2003, Prof. Navab was a distinguished member of the technical staff at Siemens Corporate Research (SCR) in Princeton, USA. In 2006, he became a board member of MICCAI, the organizer of the world’s leading conference on medical image computing and computer assisted intervention. He is on the editorial board of many international journals, including IEEE TMI, MedIA and Medical Physics. Prof. Navab has authored hundreds of scientific publications and has filed over 60 international patents.