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Kevin Wu: Research Talk 11/8

Kevin Wu

Wednesday, November 8th, 2023

2:00 pm – 3:00 pm PST 

CCSR 0235

Zoom Link: https://stanford.zoom.us/j/95984592133?pwd=MDRXcyt4eTlCVlZhdHZYZ0prLzBGZz09

Title: Medical AI After Deployment: Data-driven analyses and methods for clinically viable AI

Abstract: Medical AI algorithms have undergone significant development and regulatory approval, with over 600 FDA-approved medical AI devices currently. However, their actual clinical safety and impact remain unclear. First, we analyze FDA submission documents and find that the majority of FDA approvals do not report multi-site evaluation, and nearly none have prospective analyses. Second, we track the occurrences of newly released AI billing codes in a nationwide insurance claims database and find that only a handful of products have meaningful clinical adoption. Finally, we systematically track device updating in FDA submissions and find that the majority of devices have not had updates to model weights since initial approval. Given these limitations, we propose several methods to address common issues with algorithmic deployment. First, we present a framework for understanding the marginal contribution of distribution shifts to overall model degradation. Second, we present a method for efficient missing data collection in the context of fixed models. Finally, we present ways to improve the robustness of evaluating medical LLMs.

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Assistant Professor of Biomedical Data Science

Faculty Opening: Assistant Professor of Biomedical Data Science

Work type: University Tenure Line
Location: Stanford University
Categories: School of Medicine

The Department of Biomedical Data Science (DBDS) at Stanford University seeks two faculty members to join the Department as Assistant Professors on the University Tenure Line. One of these faculty will be additionally jointly appointed as a Core Investigator at the Arc Institute. The successful candidates will be expected to contribute creatively and in depth to the analysis of biomedical data and their use to advance science and health. A PhD or equivalent degree in data science, biostatistics, statistics, biomedical informatics, clinical informatics, computer science, biomedical engineering or a related area is required.

See https://facultypositions.stanford.edu/en-us/job/494643/assistant-professor-of-biomedical-data-science for more information.

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Open Position: Program Coordinator and Executive Associate

The DBDS administration is a team-first organization that is interdependent on its members to meet the demands of all faculty and students. We are seeking an excellent experienced administrator to fulfill the role of Program Coordinator and Executive Associate to our Department Chair. The incumbent will serve as a primary strategic partner to the Department Chair helping to plan and execute all programs and projects under her purview. To be successful, this position needs to be independent, strong, detail-oriented, personable, and able to cope with the demands of a very challenging and rewarding position.

For more information: https://careersearch.stanford.edu/jobs/chief-of-staff-project-coordinator-administrative-services-administrator-1-23433

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Min Woo Sun and Robert Tibshirani publish “Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic” in Nature Scientific Reports

Abstract

The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death.

Our work on COVID-19 excess death and public health factors has been published in Nature Scientific Reports: https://www.nature.com/articles/s41598-023-43407-0.

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