AI Health Events

Our goal is to share learning experiences with a broad community, both at Duke and beyond. Our events include the AI Health Seminar Series, the monthly Spark Imaging Seminar Series, and workshops and studios throughout the year. You can sign up for our mailing list to receive emails about upcoming events.

AI Health Spark Seminar Series: Towards solving breast cancer diagnosis with deep learning

Tuesday, June 6, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Krzysztof Geras, PhD; NYU Grossman School of Medicine and NYU Center for Data Science and at Courant Institute for Mathematical Sciences
with host Maciej Mazurowski, PhD; Associate Professor in Radiology, Duke University

Although deep learning has made stunning progress in the last few years, both in terms of engineering and theory, its real-life applications in medicine remain rather limited. One of the fields that has been anticipated to be revolutionized by deep learning for some time, yet proved to be much harder than many expected, is medical imaging. In this talk I will shed some light on my 7-year long journey in developing deep learning methods for medical imaging, in particular, for breast cancer screening. I will explain how we created a deep learning model that can perform a diagnosis with an accuracy comparable to experienced radiologists. To achieve this goal we needed a lot of perseverance, novel neural network architectures and training methods specific to medical imaging. I will also discuss the limitations of our work and what can likely be achieved in the next few years.

This session is a part of the monthly seminar series organized by Spark: AI Health Initiative for Medical Imaging. The seminar will highlight outstanding work in medical imaging at Duke and beyond. The seminar recordings will be publicly available.

The Spark initiative focuses on development, validation, and clinical implementation of artificial intelligence algorithms for broadly understood medical imaging by bringing together the technical and clinical expertise across Duke campus. For more information please contact Dr. Maciej Mazurowski (maciej.mazurowski@duke.edu).

Past Events

Challenges in Bring-Your-Own-Device Design-based Digital Health Studies to Develop Reliable and Generalizable Artificial Intelligence Tools for Healthcare Applications

Thursday, May 10, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Md Mobashir Hasan Shandhi, PhD; American Heart Association Postdoctoral Fellow, Department of Biomedical Engineering, Duke University
with host Andrew Olson, MPP; Duke AI Health

Longitudinal digital health studies combine information from digital devices, such as commercial wearable devices, and patient-reported data, such as surveys, from participants. While the ubiquitous adoption of smartphones and access to the internet supports the development of large-scale and distributed digital health studies, there are challenges in collecting representative data as a result of low adherence to, engagement with, and regularity of performing study tasks such as filling out surveys and charging and wearing devices. These challenges may result in a study population that is not representative of the general population or the population group of interest. Artificial Intelligence tools developed based on a non-representative population have a higher chance to fail to generalize in the real-world deployment of such technologies and may not work for underrepresented and underserved communities. In this seminar, the speaker will share his research group’s experience in conducting longitudinal digital health studies for COVID-19 monitoring, the challenges the researchers faced to collect data from a representative population, and how his team developed a guideline to mitigate demographic imbalance in bring-your-own-device (BYOD) design-based digital health studies. Furthermore, the speaker will also share how his team developed a machine learning method based intelligent allocation method for COVID-19 diagnostic testing in a resource-limited setting (when we have limited diagnostic tests, like the earlier phase of the pandemic and onset of new variants) using wearable and survey data collected during the longitudinal CovIdentify study.

Please join us for this lunchtime virtual seminar. The presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

Data bias is the roadblock to realizing the promise of AI in healthcare

Thursday, May 4, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Leo Anthony Celi, MD, MPH, MSc; Principal Research Scientist Massachusetts Institute of Technology; Associate Professor (Part Time) Harvard Medical School; Instructor, Harvard T.H. Chan School of Public Health; Associate Program Director, Department of Medicine, Beth Israel Deaconess
with host Nicoleta Economou, PhD; Duke AI Health

The application of artificial intelligence in healthcare requires a team science approach. A diverse set of expertise, perspectives and lived experiences are required to understand the various ways bias lurks in the data – from bias introduced by sampling selection (who made it to the database, who didn’t, and what’s the impact on downstream models), variation in the frequency of measurement that is not explained by the disease or patient phenotype (aka “shortcut” features in medical images), technology that performs differently across patient subgroups (e.g. pulse oximetry, wearable sensors optimized around fit individuals), etc. Data bias is the roadblock to realizing the promise of machine learning. Algorithmic bias is not just about evaluating model performance across patient subgroups post hoc. The goal is to ascertain that the model does not learn from features that should not affect decision making. Offering chemotherapy should not depend on whether a patient is on Medicaid or has a private insurance, predicting job performance should not be informed by the gender of the applicant, optimizing treatment for sepsis should be not be confounded by the use of infrared sensing technology. This is much easier said than done because of the discovery that computers can easily learn sensitive attributes that the human eye does not see. Using real world data to evaluate the models makes this extremely challenging. Excellent model accuracy means existing outcome disparities are fully encoded in the algorithms

Please join us for this lunchtime virtual seminar. The presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

AI Health Spark Seminar Series: A Transparency- and Trust-Centric Design Approach to AI for Healthcare

Tuesday, May 2, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Alexander Wong, PhD, PEng, SMIEEE, FIET, Professor, Department of Systems Design Engineering, University of Waterloo
with host Maciej Mazurowski, PhD; Associate Professor in Radiology, Duke University

Artificial Intelligence (AI), particularly thanks to the advances of machine learning (ML) and deep learning (DL) in recent years, holds tremendous promise and potential for enhancing clinical decision support in healthcare. From disease diagnosis to prognosis to treatment planning to patient triaging, AI-driven clinical decision support across the entire clinical workflow can great improve the accuracy, consistency, and speed with which clinicians can better serve their patients for greater quality of care. However, widespread adoption of AI in healthcare has remained limited despite these advances, with one of the biggest challenges being trust in such AI systems. Thankfully, significant progress and advances have been made towards transparency- and trust-centric design, and in this seminar I will be discussing recent developments in quantitative explainable AI, trust quantification, improvements in data, and best practices and ethical considerations for trusted AI, as well as example use uses of how this enables the building of trusted AI solutions for healthcare.
 

This session is a part of the monthly seminar series organized by Spark: AI Health Initiative for Medical Imaging. The seminar will highlight outstanding work in medical imaging at Duke and beyond. The seminar recordings will be publicly available.

A Guide to Conducting a Successful Computer Vision Project: Insights from Duke AI Health Fellows

Wednesday, April 26, 2023 | 3:00 – 4:00 PM (Eastern time)

Presented by:

Irene Tanner, MS
Akhil Ambekar, MS
Hyeon Ki Jeong, PhD
Gavin Karr, MS
with host Silvana Lawvere de Moreno, PhD

This seminar provides a practical guide to initiating and executing a computer vision analysis project, with examples from clinical imaging projects in pathology, orthopedics, and dermatology. This topic may be especially pertinent for data scientists, researchers, and practicing clinicians in specialties that use imaging data such as x-rays, whole slide images, and CT scans.

Please join us for this afternoon virtual seminar featuring members of the AI Health Data Science Fellowship Program. The presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

Spring Health Data Science Poster Showcase

Monday, April 24, 2023 | 12:00 – 2:00 PM (Eastern time)
In person at the Trent Semens Center, 6th floor

The Health Data Science poster showcase will be hosted by the Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking.

More than 25 posters will be presented, including Duke participants from: AI Health Fellowship Program; Biomedical Engineering; Clinical and Translational Science Institute (CTSI); Computer Science; Department of Biostatistics and Bioinformatics; Department of Internal Medicine; Department of Neurosurgery; Department of Surgery; Division of Geriatrics, Department of Medicine; Division of Hematology, Department of Medicine; Duke Clinical Research Institute (DCRI); Electrical and Computer Engineering (ECE); Duke Health Technology Solutions (DHTS); Laboratory for Transformative Administrative (LTA); Master of Management in Clinical Informatics (MMCi); OB-GYN; and Trinity College of Arts & Sciences.

Poster awards will include Best Computational Thinking Poster and the Good DEEDS Award (Ethical and Equitable Data Science).

What’s important in the ethics and safety of artificial intelligence in health?

Tuesday, April 25, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

David Carlson, PhD; Assistant Professor of Civil and Environmental Engineering, Assistant Professor in Biostatistics & Bioinformatics, Assistant Professor in the Department of Electrical and Computer Engineering, Assistant Professor of Computer Science
Jessica Sperling, PhD; Director, Office of Evaluation and Applied Research Partnership (Director, AREE, SSRI; Director, Evaluation & Strategic Planning, CTSI)
James Tcheng, MD; Professor of Medicine, Assistant Dean for Academic Appointments, Professor of Family Medicine and Community Health
Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health

The application of artificial intelligence in health settings holds great potential, but there are many considerations and concerns for the safe and responsible integration of these powerful methods. In this one-hour virtual seminar, we’ll discuss these considerations with 3 experts: Dr. Tcheng, who is an interventional cardiologist and clinical informaticist; Dr. Carlson, who is a quantitative and machine learning expert; and Dr. Sperling, who is an expert in evaluation and applied/community-engaged research methods. Through their complementary areas of expertise, we’ll explore perspectives on the current state and future development of ethics and safety in cutting-edge AI methods and applications, with a particular focus on health and clinical settings.

Using Multimodal Data and AI to study Post-Traumatic Epilepsy

Thursday, April 20, 2023 | 4:00 – 5:00 PM (Eastern time)

Presented by:

Dominique Duncan, PhD, USC Stevens Neuroimaging and Informatics Institute, University of Southern California
with host Nicoleta J Economou, PhD; Duke AI Health

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-site, international collaboration that aims to investigate the multifactorial processes underlying the development of epilepsy after traumatic brain injury (TBI). The study involves a parallel investigation in both humans and animal models and includes the collection of multimodal data such as MRI, EEG, and blood samples. The development of epilepsy after TBI is a complex phenomenon that spans multiple modalities, and a comprehensive understanding of its underlying biological mechanisms is essential for the development of effective treatments. To this end, we have established a centralized data archive that standardizes data and provides tools for searching, viewing, annotating, and analyzing them. This archive includes data generated from multicenter preclinical trials, clinical sites, and various laboratories in different formats. We use machine learning (ML) and artificial intelligence (AI) techniques to analyze large-scale, multimodal datasets and identify complex patterns that may not be apparent through traditional statistical methods. By integrating these cutting-edge analytical approaches with our data archive, we aim to accelerate progress towards the development of effective treatments for post-traumatic epilepsy. In addition to this EpiBioS4Rx archive, we have also developed the Data Archive for the BRAIN Initiative (DABI) and the COVID-19 Data Archive (COVID-ARC) to facilitate research in related areas and foster collaboration among researchers worldwide.

Design and implementation of a novel platform for COVID-19 contact tracing and testing

Wednesday, April 12, 2023 | 4:00 – 5:00 PM (Eastern time)

Presented by:

Andrew Olson, MPP; Associate Director, Policy Strategy and Solutions for Health Data Science, Duke AI Health
with host Hyeon Ki Jeong, PhD

In July, 2020, just a few months into the COVID-19 pandemic, the CDC funded the Snowball Study to test an approach for more efficiently identifying and contacting people in Durham, NC who may be at risk of infection. The study deployed respondent-driven sampling methods, in which individuals infected with COVID-19 serve as seed cases and recruit their own social contacts for testing and enrollment in the study. To operationalize this approach, we built a cloud-based platform that integrates with the Duke Health electronic health record for case ascertainment and with the Research Electronic Data Capture (REDCap) application for electronic consent and collection of a social contact survey. The platform generates unique coupons that participants validate to enroll in the study and that are used to track all of the links between seeds and multiple waves of peer participants. The Snowball study tested novel methods of contact tracing as well as conducting clinical research and in this seminar we will describe the design of the platform and share results from the study. This presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

Governance practices for fairness and equity: anticipating, identifying, and mitigating algorithmic bias

Thursday, April 6, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Sophia Bessias, MPH, MSA; Evaluation Lead, Algorithm-Based Clinical Decision Support (ABCDS) Oversight, and Michael Cary, PhD, RN; Duke AI Health Equity Scholar

With the explosion of interest in healthcare applications of AI and machine learning, health systems are increasingly implementing clinical algorithms intended to benefit patient care and reduce costs. Integration with clinical care also brings risks, including the potential for biased algorithms to automate and entrench existing health disparities. In this 1-hour seminar, we will share examples of bias in healthcare algorithms, discuss ways to assess bias potential across the modeling lifecycle, and offer strategies for mitigation. We will also consider how bias assessment fits into the algorithmic governance framework at Duke Health.

Please join us for this lunchtime virtual seminar. The presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

CHAI Blueprint for Trustworthy AI Webinar

Wednesday, April 5, 2023 | 12:00-1:00 PM (Eastern time)

The use of artificial intelligence (AI) in healthcare offers enormous potential for accelerating clinical research and improving quality and delivery. But concerns with some AI-based algorithms have raised questions about their safety, efficacy, and equity.

As part of the Coalition for Health AI (CHAI), Duke AI Health has been helping to build and refine a Blueprint for Trustworthy AI in partnership with member organizations. This blueprint explores the parameters for the guidance, the guardrails, the best practices, and the governance to help ensure ethical AI use in healthcare.

On April 5, 2023, from 12:00-1:00 ET, we will hold a webinar to discuss the Blueprint, how it aligns with other AI frameworks, and our next steps to evolve the Blueprint and encourage wide adoption.

Speakers include:

Keynote: Alan E. Mislove, Assistant Director for Data and Democracy, White House Office of Science and Technology Policy
Laura Adams, National Academy of Medicine
Eric Horvitz, Chief Scientific Officer, Microsoft
CHAI Steering Committee Members

We hope you will join us to learn how you can apply this Blueprint to your organization and help institutionalize the responsible use of AI in healthcare.

To learn more about CHAI, visit our website: www.coalitionforhealthai.org

AI Health Spark Seminar Series: Driving AI Innovation with Synthetic Data in Longitudinal Imaging, Continual Leaning, and Federated Learning for Healthcare

Tuesday, April 4, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by: Xiaoxiao Li, PhD; Assistant Professor at the University of British Columbia, Assistant Professor Adjunct at Yale University, and Faculty Member at Vector Institute with host Maciej Mazurowski, PhD; Associate Professor in Radiology, Duke University

Register here: https://duke.zoom.us/webinar/register/WN_2AGtDoheSYChmTf3v4F3tg

Description: In this presentation, we explore the power of synthetic data in advancing healthcare AI applications, particularly focusing on longitudinal imaging, continual learning, and federated learning. The talk delves into two data synthesis methods: the diffusion model, a fine-grained approach, and datasets distillation, a coarse approach. First, I will begin by introducing our novel diffusion model-based method for longitudinal image synthesis, which has shown great promise in understanding brain aging through the generation of spatial-temporal data. This innovative technique allows for the efficient analysis of brain development and degeneration over time. Next, I will discuss the use of data distillation to enhance the performance of continual learning and federated learning. These learning paradigms address critical challenges in modern healthcare, such as data privacy constraints. Data distillation helps mitigate the catastrophic forgetting issue in continual learning and tackles the heterogeneity issue in federated learning, enabling more effective and adaptive AI solutions in healthcare. Through the use of synthetic data and cutting-edge AI methodologies, this presentation demonstrates the potential to advance healthcare by providing improved understanding, and diagnosis for patients, fostering collaboration, and ultimately improving patient outcomes.

What are informatics and data science, and why do they matter in health?

Tuesday, March 28, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

W. Edward Hammond, PhD; Professor in Family Medicine and Community Health, Research Professor in the School of Nursing, Professor of Biostatistics and Bioinformatics
Warren Kibbe, PhD; Professor in Biostatistics & Bioinformatics
Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health

The disciplines of informatics and data science are critical to the use of technology in clinical research and healthcare. In this one-hour virtual seminar, we’ll discuss the foundations of these fields with 2 experts: Dr. Hammond, who is one of the great pioneers in medical informatics and a founding member of HL7®; and Dr. Kibbe, who is chief for Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics and Chief Data Officer for the Duke Cancer Institute. Our conversation will include what the COVID-19 global pandemic has taught us about challenges and opportunities in data infrastructure, a debate on the future and direction of the HL7® FHIR® standard, and a broad-ranging perspective into the evolution and future of informatics and data science.

Trailblazing a path to simpler, more transparent and equitable risk algorithms

Thursday, March 23, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Michael Pencina, PhD; Professor of Biostatistics and Bioinformatics; Director of Duke AI Health; Vice Dean of Data Science, Duke University School of Medicine
Chuan Hong, PhD; Assistant Professor of Biostatistics & Bioinformatics
with host Marcella Dalrymple, MPH, MBA; Duke AI Health

Join us for a live virtual seminar with Dr. Pencina and Dr. Hong, who will discuss the need for a careful evaluation of risk prediction algorithms to avoid bias and propagation of health inequities. The problem is illustrated in the most recent publication by AI Health and collaborators, “Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups,” demonstrating how currently proposed risk prediction algorithms perform markedly worse in Black individuals and are not improved when employing advanced machine learning techniques. Our expert presenters will discuss the important implications of these findings, as well as an overview of the methods, implementation, and opportunities for improvement in risk prediction. Register now to connect with Duke AI Health, and join us as we work to redefine algorithmic standards for clinical practice.

Neuroimage Analysis in Autism: from Model-Based Estimation to Data-driven Learning

Tuesday March 7th, 2023 | 12:00-1:00 PM (Eastern time)

Presented by:

James S. Duncan, Ph.D., Ebenezer K. Hunt Professor and Department Chair of Biomedical Engineering, Professor of Radiology & Biomedical Imaging Professor of Electrical Engineering, Professor of Statistics & Data Science; Yale University
with host Maciej Mazurowski, PhD; Associate Professor in Radiology, Duke University

Functional magnetic resonance imaging (fMRI) has been shown to be helpful for the study of autism spectrum disorders (ASD). This talk will describe the evolution of efforts in this area within our group that carry promise for producing objective biomarkers for ASD, as well as predicting patient response to a behavioral therapy known as Pivotal Response Treatment (PRT), using task-based fMRI. Such biomarkers would provide an important step for better understanding the underlying pathophysiology of ASD that could help with objective and personalized diagnosis, provide new targets for development of new treatments, and provide a way to monitor patient progress. Initially a robust, group-wise unified Bayesian framework to detect both hyper and hypo-active communities from connectivity maps will be described. Next, more recent work will be presented that has focused on deriving ASD biomarkers from individual subject’s time-series data, based on the classification of individual subjects (into ASD or typical control) and identifying spatially-specific key regions using convolutional neural networks and ablation analysis of regions. In addition, a strategy based on recurrent neural networks (using long-short-term memories or LSTMs) will be presented that predicts patient response to PRT behavioral therapy from baseline imaging while incorporating subject-specific phenotypic information for network initialization. Finally, early work on the use of effective connectivity based on whole brain dynamic causal modeling will be discussed as an alternative or an adjunct to functional connectivity for classification and biomarker analysis.

This session is a part of the monthly seminar series organized by Spark: AI Health Initiative for Medical Imaging. The seminar series highlights outstanding work in medical imaging at Duke and beyond.

What are machine learning and artificial intelligence, and why do they matter in health?

Tuesday, February 28, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

Matthew Engelhard, MD, PhD; Assistant Professor of Biostatistics and Bioinformatics
Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health

The transformation in artificial intelligence (AI) and machine learning (ML) is impacting clinical medicine with potential for benefit but also harm. In this one-hour virtual seminar, we’ll introduce AI and ML, highlight capabilities of current methods for images, text, and other big data in clinical settings, and discuss considerations in how to use these powerful methods responsibly. This session will be a high-level introduction accessible to people with no prior knowledge of artificial intelligence.

Algorithm-Based Clinical Decision Support (ABCDS) Oversight: A framework for the governance and evaluation of algorithms to be deployed at Duke Health

Tuesday, February 14, 2023 12:00-1:00 PM (Eastern time)

Presented by:

Armando Bedoya, MD, MMCi; Duke Health Technology Solutions
Nicoleta J Economou, PhD; Duke AI Health

In 2021, Duke Health launched the Algorithm-Based Clinical Decision Support (ABCDS) Oversight program, a collaborative effort between the Duke University School of Medicine and the Duke University Health System to ensure high-quality care, patient safety, and ownership are maintained for algorithms and related tools. In this session, Drs. Economou & Bedoya will introduce you to the ABCDS Oversight framework. This session is open to members outside of Duke.

Duke Electronic Health Records Study Design Workshop (EHR-SDW)

December 5-9, 2022

To learn more: https://aihealth.duke.edu/ehr-sdw-2022/

The Duke Electronic Health Records Study Design Workshop (EHR-SDW) will be offered in December as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on.

Duke Machine Learning Summer School

June 6–10, 2022

To learn more: https://aihealth.duke.edu/mlss2022/

The curriculum in the Duke Machine Learning Summer School (MLSS) is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLSS will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.

This is the 11th Duke Machine Learning School presented since 2017. This series has reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Last year’s machine summer learning summer school attracted 170 participants from around the world, representing 43 universities, institutes, and corporations.

AI Health Data Studio Seminars

Spring 2022

To learn more: https://aihealth.duke.edu/2022springdatastudios/

The AI Health Data Studio Seminars multi-part educational offering are designed for campus-based researchers at Duke who are interested in working with medical data but are unsure where to begin. The multi-part Data Studio seminar series will begin with an overview presented by AI Health Senior Informaticist Ursula Rogers, who has 25 years of experience in data management and software development. Additional individual sessions will feature data experts from across the Duke enterprise. Hosted by Ms. Rogers, Chief AI Health Scientist Ricardo Henao, PhD, and Associate Director of Informatics Shelley Rusincovitch, MMCi, the series builds on the successful AI Health Proposal Studios and extends structured opportunities for investigators to engage with Duke’s top data science expertise and thought leadership.