How Artificial Intelligence Will Enhance Imaging Access and Analysis.

This Medical News article is an interview with Saurabh Jha, a cardiothoracic radiologist and an associate professor of radiology at the University of Pennsylvania, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

One Day, AI Could Mean Better Mental Health for All.

This Medical News article is an interview with psychiatrist Vikram Patel, chair of the Department of Global Health and Social Medicine at Harvard Medical School.

Will Generative AI Tools Improve Access to Reliable Health Information?

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and Virologist Davey Smith, head of the Division of Infectious Diseases and Global Public Health at the University of California, San Diego.

How Machine Learning Might Help Improve Cardiac Imaging.

In this Medical News interview, University of California, San Francisco, cardiologist Rima Arnaout, joins JAMA Editor in Chief Kirsten Bibbins-Domingo to discuss the transformative potential of AI on cardiac imaging.

Digital Avatars and Personalized Voices-How AI Is Helping to Restore Speech to Patients.

In this Medical News article, Edward Chang, MD, chair of the department of neurological surgery at the University of California, San Francisco Weill Institute for Neurosciences joins JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss the potential for AI to revolutionize communication for those unable to speak due to aphasia.

Blind Spots, Shortcuts, and Automation Bias-Researchers Are Aiming to Improve AI Clinical Models.

This Medical News article is an interview with University of Michigan computer scientist Jenna Wiens, whose research interests lie at the intersection of AI and health care, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

AI Developers Should Understand the Risks of Deploying Their Clinical Tools, MIT Expert Says.

This Medical News article is an interview with Marzyeh Ghassemi, a machine learning expert at the Massachusetts Institute of Technology who focuses on health care applications, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

How to Navigate the Pitfalls of AI Hype in Health Care.

In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues.

"Scalable Privilege"-How AI Could Turn Data From the Best Medical Systems Into Better Care for All.

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and physician Atul Butte, the University of California Health System's chief data scientist.

Vigilance and Protection: How Asian and Pacific Islander, Black, Latina, and Middle Eastern Women Cope with Racism.

BACKGROUND: Research is needed to fully investigate the differential mechanisms racial and ethnic groups use to deal with ongoing intersectional racism in women's lives. The aim of this paper was to understand how Asian American and Pacific Islander, Black, Latina, and Middle Eastern women experience racism-from personal perceptions and interactions to coping mechanisms and methods of protection. METHODS: A purposive sample of 52 participants participated in 11 online racially/ethnically homogeneous focus groups conducted throughout the USA. A team consensus approach was utilized with codebook development and thematic analysis. RESULTS: The findings relate to personal perceptions and interactions related to race and ethnicity, methods of protection against racism, vigilant behavior based on safety concerns, and unity across people of color. A few unique concerns by group included experiences of racism including physical violence among Asian American Pacific Islander groups, police brutality among Black groups, immigration discrimination in Latina groups, and religious discrimination in Middle Eastern groups. Changes in behavior for safety and protection include altering methods of transportation, teaching their children safety measures, and defending their immigration status. They shared strategies to help racial and ethnic minorities against racism including mental health resources and greater political representation. All racial and ethnic groups discussed the need for unity, solidarity, and allyship across various communities of color but for it to be authentic and long-lasting. CONCLUSION: Greater understanding of the types of racism specific groups experience can inform policies and cultural change to reduce those factors.

How AI Assistants Could Help Answer Patients' Messages-and Potentially Improve Their Outcomes.

This Medical News article is an interview with John Ayers, PhD, MA, vice chief of innovation in the Division of Infectious Diseases & Global Public Health at the University of California, San Diego, the lead author of a recent study on chatbot responses to patient questions.

Travel Time and Costs for Abortion for Military Service Members After the Dobbs Decision.

This study quantifies the change in travel times for military service personnel to abortion facilities following the US Supreme Court Dobbs decision and estimates the cost of an abortion-related travel reimbursement policy.

How Do Policymakers Regulate AI and Accommodate Innovation in Research and Medicine?

In this Medical News article, JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, and Alondra Nelson, PhD, the Harold F. Linder Professor at the Institute for Advanced Study, discuss effective AI regulation frameworks to accommodate innovation.

A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021.

BACKGROUND: Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment. METHODS: We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitter's Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis. RESULTS: The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment. CONCLUSIONS: This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91.

The impact of the federal menu labeling law on the sentiment of Twitter discussions about restaurants and food retailers: An interrupted time series analysis.

The US federal menu labeling law, implemented on May 7 th 2018, required that restaurant chains post calorie counts on menu items. The purpose of this study was to analyze the change in public sentiment, using Twitter data, regarding eight restaurant chains before and after the calorie labeling law's implementation. Twitter data was mined from Twitter's application programming interface (API) for this study from the calendar year 2018; 2016 and was collected as a control. We selected restaurant chains that had a range of compliance dates with the law. Tweets about each chain were filtered by brand-specific keywords, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis was applied to receive a continuous compound score (-1-1) of how positive (1) or negative (-1) each tweet was. Controlled Interrupted Time Series (CITS) was performed with Ordinary Least Squares (OLS) Regression on 2018 and 2016 series of compound scores for each brand, and level and trend changes were calculated. Most restaurant chains that implemented the federal menu calorie labeling law experienced no change or a small change in level or trend in sentiment after they implemented labeling. Chains experienced mildly more negative sentiment right after the law was implemented, with attenuation of this effect over time. Calorie labeling did not have a strong effect on the public's perception of food brands over the long-term on Twitter and may imply the need for greater efforts to change the sentiment towards unhealthy restaurant chains.

Clinical AI Tools Must Be Fed the Right Data, Stanford Health Care's Chief Data Scientist Says.

In this Medical News interview, Stanford Health Care's chief data scientist Nigam Shah, MBBS, PhD, discusses ways to train and evaluate artificial intelligence models for use in health care.