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AI-ready Healthcare

AI-ready Healthcare

By Anirban Mukhopadhyay
Podcast is a great clinical culture for Knowledge dissemination, constructive arguments & deep discussions. One more way in which the MICCAI community is different from those who we serve. Introducing "AI-ready Healthcare", my podcast to bridge this gap. I invite fellow researchers from both communities to talk about the translational aspects of AI research into clinical care. Often we converse with my co-host Henry Krumb.
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Jakob Nikolas Kather: Swarm intelligence for Oncology
Jakob Nikolas kather is a professor at Technical University Dresden, leading the department of Clinical Artificial Intelligence at Else Kroener Fresenius Center for Digital Health. As a physician, he specializes in Internal Medicine and gastrointestinal oncology. As a researcher, he focuses on deep learning for immunotherapy biomarkers in cancer. Swarm learning for decentralized artificial intelligence in cancer histopathology
43:59
September 05, 2022
Prateek Prasanna: Augmenting Radiologist's Knowledge into AI
Prateek Prasanna is an assistant professor in the Biomedical Informatics department at Stony Brook University, New York. He directs the Imaging Informatics for Precision Medicine Lab. His research interests lie at the intersection of medical image analysis and machine learning. We talked about the following papers: 1. Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations 2. RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention–guided Disease Classification
55:20
August 30, 2022
Sailesh Conjeti: MLOps for Healthcare AI
Sailesh Conjeti is on a mission to bring AI-based solutions to Healthcare and translating them to clinical use to make a difference. He is the Functional Lead of Data Science at Siemens Healthineers. You can read his blogposts at https://www.saileshconjeti.com/blog.
47:48
August 23, 2022
Ismini Lourentzou: Chest ImaGenome
Ismini Lourentzou is an Assistant Professor of Computer Science at Virginia Tech. Prior to VT, she spent a year as research scientist (Research Staff Member) at IBM Almaden Research Center, working on Machine Learning, Natural Language Processing and Information Retrieval problems. Her research interests are broadly defined at the intersection of Data Science, Big Data, Machine Learning, and Artificial Intelligence.  Chest ImaGenome Dataset for Clinical Reasoning: https://openreview.net/forum?id=H-d5634yVi
30:17
August 15, 2022
Matthias Unberath: Forgotten Humans of Explainable AI
Mathias Unberath is an assistant professor in the Department of Computer Science, and is affiliated with the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. With his group—the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab—he advances healthcare by creating collaborative intelligent systems that support clinical workflows. Through synergistic research on imaging, computer vision, machine learning, and interaction design, he builds human-centered solutions that are embodied in emerging technology such as mixed reality and robotics. Pre-print of the paper we discussed: https://arxiv.org/pdf/2112.12596v1.pdf
40:05
August 09, 2022
Taufique Joarder: Policy Questions of Healthcare AI
Taufique Joarder is a health policy and systems researcher and a university faculty. He has thirteen years of national and international experience and a doctorate in public health with expertise in health policy and systems research, teaching and training as well as extensive publishing. His background also includes higher leadership positions in NGOs/CSOs, faculty positions, policy-relevant engagements in Bangladesh and abroad, and extensive media involvement (as an expert, guest discussant, moderator, and TV anchor). 
48:02
August 02, 2022
Stephen Aylward: The case of Open-source software
Stephen Aylward, Ph.D., is the senior director of strategic initiatives and founder of Kitware’s North Carolina office. He helps drive multiple research and open source software development projects at Kitware. Over the past 25+ years, Stephen has conducted medical image analysis research covering nearly every aspect of health care, including screening, diagnosis, treatment planning, guidance, and outcome assessment for mammography, neurosurgery, partial liver transplantation, retinopathy of prematurity, stroke, traumatic brain injury, pre-clinical cancer studies, and others. He has also been instrumental in the creation of the Insight Toolkit (ITK), major updates to 3D Slicer, and the development of new technologies and the VTK.js library for web-based scientific visualization.
43:36
July 26, 2022
Lena Maier-Hein: What does it mean to win a Biomedical Challenge?
Lena Maier-Hein is the head of the Computer Assisted Medical Interventions (CAMI) department at the German Cancer Research Center (DKFZ) in Heidelberg, Germany. Her research focuses on Surgical Data Science and rankings of biomedical challenges.
47:48
April 12, 2022
Russ Taylor: The role of AI in Robotic Surgery
Russ Taylor is the father of robotic surgery. Hi is the John C. Malone Professor in the Department of Computer Science, and the director of the Laboratory for Computational Sensing and Robotics. His research has focused on all aspects of computer-integrated interventional medicine. Broadly, this research has included: Medical robotics Medical imaging & modeling and Complete systems for surgical assistance, image-guided surgery, and "Surgical CAD/CAM". An underlying theme has been the basic insight that information-based technologies can have just as profound an impact on computer-integrated medicine as it has had on computer-integrated manufacturing.
45:06
April 05, 2022
Ilker Hacihaliloglu: Ultrasound for all
Ilker is interested in the extraction of relevant information from three dimensional (3D) medical images by developing state of the art computational algorithms for image guided surgery and therapy applications. The main objective of his research is to study and model medical procedures and introduce advanced computer integrated solutions to improve their quality, efficiency, and safety.
58:44
March 29, 2022
Frank Xu: Baidu's AI, WHO's Digital Health & other stories
Frank (Yanwu) Xu, is an Intelligent Healthcare Scientist (research lead) at Baidu, an Adjunct Professor at Ningbo Institute of Materials Technology & Engineering, the Chinese Academy of Sciences (CAS), and an Adjunct Principal Investigator at Singapore Eye Research Institute. Frank is also serving the World Health Organization (WHO) as a technical advisory group member of Digital Health and an expert group member of Data Principles and Sharing Policies.
56:60
March 22, 2022
Lorenzo Righetto: Publishing MICCAI research into Nature Communications
Lorenzo Righetto is an associate Editor of Nature Communications where he handles manuscripts in the area of digital medicine and computational health. Lorenzo joined Nature Communications in January 2020. Lorenzo is based in the London office.
37:21
March 15, 2022
Karsten Ridder: Communication is key for AI-ready Healthcare
Karsten Ridder is a practicing radiologist from Dortmund, Germany with a special focus on Women's Health Imaging and Cardiovascular Imaging. He received numerous awards for his clinical research and innovation, including German Medical Award for Innovation in 2021. 
53:50
March 08, 2022
Shuo Li: Will MICCAI 2022 be virtual?
Prof. Shuo Li is the Founding Scientific Director of Digital Imaging Group at London, Canada. He is also the general chair of MICCAI 2022 that will happen in Singapore.
49:10
March 01, 2022
Julia Schnabel: MICCAI goes to Africa
Julia Schnabel is the Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging. She is the co-general chair of MICCAI 2024, the first MICCAI in Africa. She often Tweets @ja_schnabel.
49:25
December 07, 2021
Anant Madabhushi: When MICCAI scientist meets Real Clinicians
Anant Madabhushi is the Donnell Institute Professor of Biomedical Engineering at Case Western Reserve University (CWRU) in Cleveland and director of the university's Center for Computational Imaging and Personalized Diagnostics (CCIPD). He is a Research Scientist at the Louis Stokes Cleveland Veterans Administration (VA) Medical Center and has affiliate appointments both at University Hospitals and Cleveland Clinic. He holds secondary appointments in the departments of Urology, Radiology, Pathology, Radiation Oncology, General Medical Sciences, Computer & Data Sciences, and Electrical, Computer and Systems Engineering at CWRU. We talked about his research on translation of AI to clinical oncology. He tweets regularly @anantm.
50:24
November 30, 2021
Leo Joskowicz: Shaping up AI and MICCAI
Leo Joskowicz is a pioneer of Computer Assisted Intervention and the President of MICCAI society. He is a professor at the School of Engineering and Computer Science at the Hebrew University of Jerusalem. In this episode, we explore his interest in Geometry, Shape and making MICCAI society a home to all scientists working in medical imaging problems, no matter their geographical location.
43:20
November 23, 2021
Dan Stoyanov: Surgical Data Science 101
Dan Stoyanov is a Professor of Robot Vision in the Department of Computer Science at University College London, Director of the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Royal Academy of Engineering Chair in Emerging Technologies and a Fellow of the Institution of Engineering and Technology. Dan is also Chief Scientific Officer at Digital Surgery Ltd and Co-Founder of Odin Medical, both companies specializing in developing AI products for interventional healthcare. You can follow Dan on Twitter @DanStoyanov.
48:01
November 16, 2021
John Mongan: To buy or not to buy radiology AI
John Mongan is the Associate Chair for Translational Informatics, Director of the Center for Intelligent Imaging and an Associate Professor of Clinical Radiology (Abdominal Imaging and Ultrasound section) in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco. His research focuses on AI in medical imaging. In this session, we discussed the business case of Radiology AI and his Checklist for Artificial Intelligence in Medical Imaging (CLAIM).  You can find him in Twitter @MonganMD.
55:57
November 09, 2021
Qi Dou: Federated Learning for radiology
Qi Dou is an Assistant Professor from The Chinese University of Hong Kong. Her research focus is on the interdisciplinary field of medical image analysis, artificial intelligence and robotics. In this episode, we talked about the importance of Federated Learning in medical imaging and in particular, her paper Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. You can find her in Twitter @QiDou_.
50:15
November 02, 2021
Andreas Maier: Known operator learning for medical imaging
Professor Andreas Maier leads the Pattern Recognition Lab of Friedrich-Alexander-Universität of Erlangen-Nürnberg. In this episode, we discussed his research on Known Operator Learning and in particular his paper Learning with known operators reduces maximum error bounds. You can find him in Twitter @maier_ak.
45:58
October 26, 2021
Alex Frangi: Unlocking In-silico clinical trials
Professor Alex Frangi is Diamond Jubilee Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He directs the CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine. In this episode, we discuss computational medicine, the promises of in-silico trials and his new paper In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials.
01:02:43
September 14, 2021
Yuri Tolkach: Silent Failures of Deep Digital Pathology
Dr. Yuri Tolkach, a pathologist and researcher from Uniklinik Köln, is breaking new grounds in digital pathology with probing questions about the usability of deep learning. In this episode, we discussed in great details his recent article on Quality control stress test for deep learning-based diagnostic model in digital pathology.
47:44
September 07, 2021
Marius Linguraru: Making babies fitter - within MICCAI and beyond
Marius Linguraru is a principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children's National Hospital in Washington, D.C. Dr. Linguraru is also professor of Radiology and Pediatrics and secondary professor of Biomedical Engineering at George Washington University. He co-founded PediaMetrix Inc., a company focused on infant well-being by creating solutions to improve the management of conditions of early childhood. Finally Dr. Linguraru is a board member of MICCAI society with a focus on early career development of young researchers. In this episode we discussed in depth about his research on pediatric health and his activities for the MICCAI society.  
45:48
August 31, 2021
Arijit Patra: Big-Pharmas need imaging AI, and they don't know it yet!
Arijit Patra, a senior machine learning scientist from AstraZeneca, discusses how AI can significantly speed-up pre-clinical imaging. He also discussed his PhD thesis on continual learning for fetal ultrasound imaging.
01:01:50
August 24, 2021
Indranil Mallick: Why Indian healthcare needs AI post COVID? Reflections of a Radiation Oncologist
Indranil Mallick, a practicing oncologist from India, asserts the necessity of AI in Radiation Oncology. His reflections of practicing Oncology through the two waves of the pandemic in India is a reminder, how varied the demands are of the AI-readiness of healthcare across the globe.  
43:10
August 17, 2021
Michal Rosen-Zvi: Underwhelming maturity of Radiology AI in COVID-19 imaging
Michal Rosen-Zvi, director of IBM Research's healthcare informatics, talks about her perspective on the usefulness of radiology AI during the pandemic. In particular, we discussed her recently published article On the role of artificial intelligence in medical imaging of COVID-19. With multiple articles describing similar concerns, this is a timely episode about a very relevant topic. Further reading: 1. Hundreds of AI tools have been built to catch covid. None of them helped. 2. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
47:21
August 10, 2021
Daniel Pinto dos Santos: Get your radiology report structured, have some ECLAIRS!
We chatted with the radiologist Dr. Daniel Pinto dos Santos about his research in deep learning for radiology. We focused on two of his recent articles. You can read both open-access article right here: 1.  Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs 2. To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)    
51:26
August 03, 2021
Terry Peters: Beware of the nasty surprises from healthcare AI
Prof. Terry Peters tackles problems related to Image-guided interventions with a special focus on intra-operative navigation. While majority of MICCAI society is whole-heartedly embracing AI, Prof. Peters, a MICCAI fellow, voices his skepticism in some of the research directions. He cautions about proper standardization and interpretability of healthcare AI. Very important lessons for the majority of young members within MICCAI society.  
41:47
June 15, 2021
Ilkay Oksuz: AI gives a thumbs-up to Cardiac MRI
Ilkay Oksuz is an Assistant Professor in Computer Engineering Department of Istanbul Technical University. He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab. He talks about his research on machine learning for medical image quality assessment with a particular focus towards Cardiac Magnetic Resonance Imaging.
49:39
June 08, 2021
Saif Afat: Does global radiology need AI?
Dr. Saif Afat is a radiologist, originally from Iraq, trained and actively practicing in the University Hospital Tuebingen, Germany. He will introduce the radiology at the global context beyond developed world and the role that AI can play in global Radiology.  
44:15
June 01, 2021
Andreas Bucher: Why a German Radiologist should care about AI?
Dr. Andreas Bucher is a radiologist practicing at the University Hospital Frankfurt. He is also one of the spokesperson of the project RACOON and the secretary of 102nd German Radiology Congress. In this episode Dr. Bucher shares his view about AI from a radiologist's perspective who is practicing in Germany. 
41:36
May 25, 2021
Bernhard Dorweiler: Intelligent training of Vascular Surgeons
Professor Dorweiler is the director of the Clinic for Vascular Surgery in the Heart Center of the Cologne University Hospital. He is interested in the digital advancement of surgeries with a special focus on 3D printing patient specific anatomies for surgical planning and teaching. In this episode, he talks about his vision of how AI can help in this endeavor.  
46:56
May 18, 2021
Sandy Engelhardt: Heart bits
Sandy Engelhardt is an assistant professor in the University hospital of Heidelberg, Germany. She leads the research group of AI in Cardiovascular Medicine. In this podcast, she talks about her research of using AI for complex Cardiac Surgery as well as the AdaptOR challenge (in association with the DGM4MICCAI workshop) we are co-organizing for MICCAI 2021. 
50:10
May 11, 2021
Tianming Liu: Cross-talk between Neuroimaging and Neural Networks
Tianming Liu is a Distinguished Research Professor and a Full Professor of Computer Science at University of Georgia, USA. He has also been the member of MICCAI board and co-general chair of MICCAI 2019. He talks about how the cross pollination of ideas between separate sub-fields of neuroimaging and neural nets (AI) can make both fields better. 
55:35
May 05, 2021
Intro AI-ready Healthcare
This short recording is to introduce the purpose of AI-ready Healthcare as well as the host. 
01:08
March 31, 2021