Calendar

Sep
21
Wed
2022
IBIIS & AIMI Seminar: Ethical Challenges in the Application of AI to Healthcare @ ZOOM: https://stanford.zoom.us/j/99191454207?pwd=N0ZYWnh1Mks0UEluOVRUZjdWNHZPUT09
Sep 21 @ 1:30 pm – 2:30 pm

David Magnus, PhD
Thomas A Raffin Professor of Medicine and Biomedical Ethics and Professor of Pediatrics, Medicine, and by courtesy of Bioengineering
Director, Stanford Center for Biomedical Ethics
Associate Dean for Research
Stanford University

Title: Ethical Challenges in the Application of AI to Healthcare

Abstract:
This presentation will focus on three issues. First, applying AI to healthcare requires access to large data sets. Data acquisition and data sharing raises a number of challenging ethical issues, including challenges to traditional understandings of informed consent, and importance of diversity and inclusion in data sources. Second, I will briefly discuss the widely discussed issues around justice and equity raised by AI in healthcare. Finally, I will discuss challenges with ethical oversight and governance, particularly in relation to research development of AI. IRB’s are prohibited from considering downstream social consequences and harms to individuals other than research participants when evaluating the harms and risks of research. This gap needs to be filled, particularly as dual uses of AI models are now recognized as a problem.

Bio:
David Magnus, PhD is Thomas A. Raffin Professor of Medicine and Biomedical Ethics and Professor of Pediatrics and Medicine and by Courtesy of Bioengineering at Stanford University, where he is Director of the Stanford Center for Biomedical Ethics and an Associate Dean of Research. Magnus is member of the Ethics Committee for the Stanford Hospital. He is currently the Vice-Chair of the IRB for the NIH Precision Medicine Initiative (“All of Us”). He is the former President of the Association of Bioethics Program Directors, and is the Editor in Chief of the American Journal of Bioethics. He has published articles on a wide range of topics in bioethics, including research ethics, genetics, stem cell research, organ transplantation, end of life, and patient communication. He was a member of the Secretary of Agriculture’s Advisory Committee on Biotechnology in the 21st Century and currently serves on the California Human Stem Cell Research Advisory Committee. He is the principal editor of a collection of essays entitled “Who Owns Life?” (2002) and his publications have appeared in New England Journal of Medicine, Science, Nature Biotechnology, and the British Medical Journal. He has appeared on many radio and television shows including 60 Minutes, Good Morning America, The Today Show, CBS This Morning, FOX news Sunday, and ABC World News and NPR. In addition to his scholarly work, he has published Opinion pieces in the Philadelphia Inquirer, the Chicago Tribune, the San Jose Mercury News, and the New Jersey Star Ledger.

Oct
19
Wed
2022
IBIIS & AIMI Seminar: Learning to Read X-Ray: Applications to Heart Failure Monitoring @ ZOOM: https://stanford.zoom.us/j/93555578704?pwd=eTdhRHM4K0w5WGVmSElSWGkzN3VqQT09
Oct 19 @ 12:00 pm – 1:00 pm

Polina Golland, PhD
Professor of Electrical Engineering and Computer Science
PI in the Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology

Title: Learning to Read X-Ray: Applications to Heart Failure Monitoring

Abstract: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports to construct joint multimodal embedding that serves as a basis for classification. We demonstrate the advantages of this approach in application to assessment of pulmonary edema severity in congestive heart failure that motivated the development of the method.

Nov
16
Wed
2022
IBIIS & AIMI Seminar: Advanced Prostate Cancer Imaging @ Zoom: https://stanford.zoom.us/j/99807942044?pwd=TmJkclNkbVBZOG04KzJaSFRWVXlxZz09
Nov 16 @ 12:00 pm – 1:00 pm

Baris Turkbey, MD, FSAR
Senior Clinician
Section Chief of MRI
Section Chief of Artificial Intelligence
Molecular Imaging Branch
National Cancer Institute, NIH

Title: Advanced Prostate Cancer Imaging

Talk Objectives: 

  • To discuss current status and limitations of localized prostate cancer diagnosis.
  • To discuss use of artificial intelligence in diagnosis of localized prostate cancer.
  • To discuss use of molecular imaging in clinical prostate cancer management.

Bio:
Dr. Turkbey obtained his medical degree from Hacettepe University in Ankara, Turkey in 2003. He completed his residency in Diagnostic and Interventional Radiology at Hacettepe University. He joined Molecular Imaging Branch (MIB), National Cancer Institute, NIH in 2007. His main research areas are imaging of prostate cancer (multiparametric MRI, PET CT), image guided biopsy and treatment techniques (focal therapy, surgery and radiation therapy) for prostate cancer and artificial intelligence. Dr. Turkbey is a member of Prostate Imaging Reporting & Data System (PI-RADS) Steering Committee. He is the Director Magnetic Resonance Imaging section in MIB and the Artificial Intelligence Resource in MIB.

Dec
14
Wed
2022
IBIIS & AIMI Hybrid Seminar: Anthony Gatti, PhD & Liangqiong Qu, PhD @ Clark Center S360
Dec 14 @ 1:00 pm – 2:00 pm

In Person at the Clark Center S360 – Lunch will be provided!
Zoom: https://stanford.zoom.us/j/99496515255?pwd=MHlXbXM2WXJULzZwemk1WjJHNFZOdz09

Anthony Gatti, PhD
Postdoctoral Research Fellow
Department of Radiology
Wu Tsai Human Performance Alliance
Stanford University

Title: Towards Understanding Knee Health Using Automated MRI-Based Statistical Shape Models

Abstract: Knee injuries and pain are prevalent across all ages, with varying causes from “anterior knee pain” in runners to osteoarthritis-related pain. Osteoarthritis pain is a particular problem because structural outcomes assessed on medical images often disagree with symptoms. Most studies trying to understand knee health and pain use simple biomarkers such as mean cartilage thickness. My talk will present an automated pipeline for quantifying the whole knee using statistical shape modeling. I will present a conventional statistical shape model as well as a novel approach that uses generative neural implicit representations. Both modeling approaches allow unsupervised identification of salient anatomic features. I will demonstrate how these features can be used to predict existing radiographic outcomes, patient demographics, and knee pain.

Liangqiong Qu, PhD
Postdoctoral Research Fellow
Department of Biomedical Data Sciences
Stanford University

Title: Distributed Deep Learning in Medical Imaging

Abstract: Distributed deep learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data.
In this talk, we will first investigate the use distributed deep learning to build medical imaging classification models in a real-world collaborative setting.
We then present several strategies to tackle the data heterogeneity challenge and the lack of quality labeled data challenge in distributed deep learning.

Jan
18
Wed
2023
IBIIS & AIMI Zoom Seminar: Biologically Inspired Deep Learning as a New Window into Brain Dysfunction @ Zoom: https://stanford.zoom.us/j/96155849129?pwd=MTVtenF6RWdHMEwwdEZoV3NhM0svUT09
Jan 18 @ 12:00 pm – 1:00 pm

Archana Venkataraman, PhD
Associate Professor of Electrical and Computer Engineering
Boston University

Title: Biologically Inspired Deep Learning as a New Window into Brain Dysfunction

Abstract: Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unparalleled success of these models has, in many cases, been fueled by an explosion of data. Millions of labeled images, thousands of annotated ICU admissions, and hundreds of hours of transcribed speech are common standards in the literature. Clinical neuroscience is a notable holdout to this trend. It is a field of unavoidably small datasets, massive patient variability, and complex (largely unknown) phenomena. My lab tackles these challenges across a spectrum of projects, from answering foundational neuroscientific questions to translational applications of neuroimaging data to exploratory directions for probing neural circuitry. One of our key strategies is to integrate a priori information about the brain and biology into the model design.

This talk will highlight two ongoing projects that epitomize this strategy. First, I will showcase an end-to-end deep learning framework that fuses neuroimaging, genetic, and phenotypic data, while maintaining interpretability of the extracted biomarkers. We use a learnable dropout layer to extract a sparse subset of predictive imaging features and a biologically informed deep network architecture for whole-genome analysis. Specifically, the network uses hierarchical graph convolution that mimic the organization of a well-established gene ontology to track the convergence of genetic risk across biological pathways. Second, I will present a deep-generative hybrid model for epileptic seizure detection from scalp EEG. The latent variables in this model capture the spatiotemporal spread of a seizure; they are complemented by a nonparametric likelihood based on convolutional neural networks. I will also highlight our current end-to-end extensions of this work focused on seizure onset localization. Finally, I will conclude with exciting future directions for our work across the foundational, translational, and exploratory axes.

Feb
15
Wed
2023
IBIIS & AIMI Seminar: Computational Pathology – Towards Precision Medicine @ Zoom: https://stanford.zoom.us/j/91585038349?pwd=L1ZuRkZibG1iSmdtR0RtakhVdi9HZz09
Feb 15 @ 9:30 am – 10:30 am

Andrew Janowczyk, PhD
Assistant Professor
Department of Biomedical Engineering
Emory University

Title: Computational Pathology: Towards Precision Medicine

Abstract:
Roughly 40% of the population will be diagnosed with some form of cancer in their lifetime. In a large majority of these cases, a definitive cancer diagnosis is only possible via histopathologic confirmation on a tissue slide. With the increasing popularity of the digitization of pathology slides, a wealth of new untapped data is now regularly being created.

Computational analysis of these routinely captured H&E slides is facilitating the creation of diagnostic tools for tasks such as disease identification and grading. Further, by identifying patterns of disease presentation across large cohorts of retrospectively analyzed patients, new insights for predicting prognosis and therapy response are possible [1,2]. Such biomarkers, derived from inexpensive histology slides, stand to improve the standard of care for all patient populations, especially where expensive genomic testing may not be readily available. Moreover, since numerous other diseases and disorders, such as oncoming clinical heart failure [3], are similarly diagnosed via pathology slides, those patients also stand to benefit from these same technological advances in the digital pathology space.

This talk will discuss our research aimed towards reaching the goal of precision medicine, wherein patients receive optimized treatment based on historical evidence. The talk discusses how the applications of deep learning in this domain are significantly improving the efficiency and robustness of these models [4]. Numerous challenges remain, though, especially in the context of quality control and annotation gathering. This talk further introduces the audience to open-source tools being developed and deployed to meet these pressing needs, including quality control (histoqc.com [5]), annotation (quickannotator.com), labeling (patchsorter.com), validation (cohortfinder.com).

Mar
15
Wed
2023
IBIIS & AIMI Seminar: What Makes a ‘Good’ Decision? An Empirical Bioethics Study of Using AI at the Bedside @ https://stanford.zoom.us/j/96612401401?pwd=WFNJb2Q4dStoVDE5a25BYTBkMjN4QT09
Mar 15 @ 12:00 pm – 1:00 pm

Melissa McCradden, PhD
John and Melinda Thompson Director of Artificial Intelligence in Medicine
Integration Lead, AI in Medicine Initiative
Bioethicist, The Hospital for Sick Children (SickKids)
Associate Scientist, Genetics & Genome Biology
Assistant Professor, Dalla Lana School of Public Health

Title: What Makes a ‘Good’ Decision? An Empirical Bioethics Study of Using AI at the Bedside

Abstract: This presentation will identify the gap between AI accuracy and making good clinical decisions. I will present a study where we develop an ethical framework for clinical decision-making that can help clinicians meet medicolegal and ethical standards when using AI that does not rely on explainability, nor perfect accuracy of the model.

Apr
19
Wed
2023
IBIIS & AIMI Hybrid Seminar: Designing Machine Learning Processes For Equitable Health Systems @ ZOOM: https://stanford.zoom.us/j/97119304595?pwd=clBRVW45NXdWZE9ZUk8ySzQ0OEtYQT09
Apr 19 @ 12:00 pm – 1:00 pm

Marzyeh Ghassemi, PhD
Assistant Professor, Department of Electrical Engineering and Computer Science
Institute for Medical Engineering & Science
Massachusetts Institute of Technology (MIT)
Canadian CIFAR AI Chair at Vector Institute

Title: Designing Machine Learning Processes For Equitable Health Systems

Abstract
Dr. Marzyeh Ghassemi focuses on creating and applying machine learning to understand and improve health in ways that are robust, private and fair. Dr. Ghassemi will talk about her work trying to train models that do not learn biased rules or recommendations that harm minorities or minoritized populations. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain.

Apr
26
Wed
2023
IBIIS & AIMI Seminar: Advancing Health at the Speed of AI @ LKSC 120 and remote via Zoom
Apr 26 @ 2:30 pm – 3:30 pm
Hoifung Poon

Hoifung Poon, PhD
General Manager at Health Futures of Microsoft Research
Affiliated Professor at the University of Washington Medical School.

Title: Advancing Health at the Speed of AI

Abstract: The dream of precision health is to develop a data-driven, continuous learning system where new health information is instantly incorporated to optimize care delivery and accelerate biomedical discovery. In reality, however, the health ecosystem is plagued by overwhelming unstructured data and unscalable manual processing. Self-supervised AI such as large language models (LLMs) can supercharge structuring of biomedical data and accelerate transformation towards precision health. In this talk, I’ll present our research progress on biomedical AI for precision health, spanning biomedical LLMs, multi-modal learning, and causal discovery. This enables us to extract knowledge from tens of millions of publications, structure real-world data for millions of cancer patients, and apply the extracted knowledge and real-world evidence to advancing precision oncology in deep partnerships with real-world stakeholders.

May
17
Wed
2023
IBIIS & AIMI Seminar: Radiomics and Radiogenomics: The Role of Imaging, Machine Learning, and AI, as a Biomarker for Cancer Prognostication and Therapy Response Evaluation @ Clark Center S360 - Zoom Details on IBIIS website
May 17 @ 12:00 pm – 1:00 pm

Despina Kontos, PhD
Matthew J. Wilson Professor of Research Radiology II
Associate Vice-Chair for Research, Department of Radiology
Perelman School of Medicine
University of Pennsylvania

Title: Radiomics and Radiogenomics: The Role of Imaging, Machine Learning, and AI, as a Biomarker for Cancer Prognostication and Therapy Response Evaluation

Abstract: Cancer is a heterogeneous disease, with known inter-tumor and intra-tumor heterogeneity in solid tumors. Established histopathologic prognostic biomarkers generally acquired from a tumor biopsy may be limited by sampling variation. Radiomics is an emerging field with the potential to leverage the whole tumor via non-invasive sampling afforded by medical imaging to extract high throughput, quantitative features for personalized tumor characterization. Identifying imaging phenotypes via radiomics analysis and understanding their relationship with prognostic markers and patient outcomes can allow for a non-invasive assessment of tumor heterogeneity. Recent studies have shown that intrinsic radiomic phenotypes of tumor heterogeneity for cancer may have independent prognostic value when predicting disease aggressiveness and recurrence. The independent prognostic value of imaging heterogeneity phenotypes suggests that radiogenomic phenotypes can provide a non-invasive characterization of tumor heterogeneity to augment genomic assays in precision prognosis and treatment.