2021 Summit of the Mellichamp Mind & Machine Initiative

Overview

The increasing complexity and sheer volume of data available in healthcare has led to a growing interest in deploying AI tools to support clinical decisions, improve patient outcomes, and optimize treatment efficiency. Although a number of reports suggest that AI can perform as well or better than humans at some healthcare tasks, formidable design challenges, as well as ethical and legal questions remain.

The 2021 Mellichamp Mind and Machine Intelligence Annual Summit looks at the state-of-the-art of AI in healthcare and the ways in which AI can be used/misused in this setting. This (virtual) event will be held over three half days, and is organized around these broad topics:

Topics

AI, Doctors, and Patients

Ziad Obermeyer (Keynote)
Distinguished Associate Professor
School of Public Health, UC Berkeley
"What AI Can Teach Us About Human Intelligence"

Tim Bickmore
Professor, Associate Dean for Research
Khoury College of Computer Sciences, Northeastern University
"Death by Siri: The Dangers of Asking AI for Medical Advice"

Katherine Heller
Assistant Professor
Department of Statistical Science, Duke University
"Machine Learning in Real-world Healthcare Settings: How Far We've Come and Where We Are Going"

Matt Beane
Assistant Professor
Technology Management Program, UC Santa Barbara
"Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail"

AI, Radiology, and Decisions

Sharmila Majumdar (Keynote)
Margaret Hart Surbeck Distinguished Professor in Advanced Imaging, Vice Chair for Research
Department of Bioengineering and Therapeutic Sciences, UC San Francisco
"Intelligent Imaging: From Acquisition to Precision Medicine"

Ioannis Sechopoulos
MD, Associate Professor
Radiology and Nuclear Medicine, Radboud University, Nijmegen, NL
"AI for breast cancer screening in Europe: Possibilities and Current Results"

Shamim Nemati
Assistant Professor and Director of Predictive Health Analytics,
Biomedical Informatics, UC San Diego
"Machine Intelligence in Critical Care; Role of Interpretability and Trust"

Kyle Myers
Senior Advisor; Division of Imaging, Diagnostics, and Software Reliability
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA
"AI-Enabled Systems in Medical Imaging: FDA Research in Support of Regulatory Pathways"

AI and COVID-19

Lynn Fitzgibbons (Keynote)
MD, Infectious Disease Specialist
Cottage Health Santa Barbara
"COVID-19 Vaccinations and AI: harnessing the power and avoiding the pitfalls"

Laure Wynants
Assistant Professor
Department of Epidemiology, Maastricht University, Maastricht , NL
"A journey through the disorderly world of diagnostic and prognostic models for covid-19: a living systematic review"

Daniel Chow
Assistant Professor-in-Residence, Radiological Sciences
School of Medicine, UC Irvine
"Deployment of AI-Based Risk Assessment Tools for COVID at UCI - A Single-Center Experience"

Rajesh Ranganath
Assistant Professor
Courant Institute of Mathematical Sciences, New York University
"A Deployed Model for COVID-19"