Life and Death in Fort Worth 76104
People using the Fort Worth zip code 76104 on average won’t see their 67th birthday. What is the cause of the lowest life expectancy rate in Texas? What can we do to help? Read the Star-Telegram survey:
The University of North Texas Health Sciences Center to lead massive new federal project to train a more diverse workforce in artificial intelligence and healthcare, the National Institutes have announced. of Health.
The Fort Worth campus will serve as the starting point for the national project to tackle a pressing issue in the nation’s health care system. Congress has allocated $ 100 million over the next two years to fund the program, for which UNT has been named a “focal point,” said Dr Jamboor Vishwanatha, who will head the center.
Vishwanatha, the director of Texas Center for Health Disparities, and his peers are charged with using computers to make healthcare more equitable and to prevent bad data or biased algorithms from worsening health disparities than they already are. This mission becomes more and more urgent as machine learning touches more and more aspects of our daily lives, from an Apple Watch or a Fitbit that monitors our movements today to pacemakers tomorrow. who could be powered by algorithms to meet the needs of an individual patient.
âThe biggest concern is: Yes, technology is moving in that direction, but if the algorithms being developed and the people who are developing those algorithms, if they are not diversified, what will eventually happen is that you will have progress in the medicine, but it is not applicable to many groups, âsaid Vishwanatha.
Where do we meet artificial intelligence in healthcare?
Artificial intelligence and machine learning algorithms power many of the tools we use on a daily basis, from search engines like Google to the software behind ridesharing apps like Uber.
The average person will likely only experience machine learning as it affects their healthcare through a medical device or implant.
âAnywhere there is a device that is making inferences about your body, it will almost certainly be powered by AI,â said Dr Shiri Dori-Hacohen, Assistant Professor in the Department of Computer Science and Engineering at the University of Connecticut.
Because artificial intelligence systems can use tons of data to understand and analyze complex problems, they have the potential to tackle some of the thorniest issues in health and healthcare. But because these systems are designed by humans, they also have the potential to be biased and potentially exacerbate the problems they are designed to solve.
âBecause we live in a biased world, our machine learning approaches learn from the data we provide them. And if the data is biased, because the world is biased, then naturally our algorithms will also learn biased predictions, âsaid Dori-Hacochen, who is not affiliated with the NIH project. “There is no magic there, [AI systems] cannot be magically better than the humans who train them.
Poorly designed systems in other industries have had disastrous effects: News agency ProPublica discovered that an algorithm designed to predict an accused’s likelihood of committing future crimes was only correct about 20% of the time, and the algorithm was “particularly likely to falsely flag black defendants as future criminals.”
Step one: Recruit a diverse workforce
The coalition that the UNT will lead is tasked with training and recruiting a more representative workforce to study and develop algorithms. Currently, the field is dominated by men, with few women of any race or ethnicity and few black or Hispanic scientists of any gender entering the field. Of new artificial intelligence doctorates in 2019, only 3.2% were Hispanic and 2.4% were black, according to a survey conducted by the Computing Research Association. That same survey found that 45% of graduates were white and 22.4% were Asian, with almost 25% of graduates of an unknown race or ethnicity. And over the past 10 years, women of any race or ethnicity made up 18.3% of new artificial intelligence and computer science doctoral graduates.
UNT will be the core of a network of institutions, said Vishwanatha. The school will team up with private businesses, community organizations and other schools to create a consortium. The consortium will historically involve black colleges and universities and tribal colleges so that while these institutions do not have a formal machine learning program, they can take courses and access data through the infrastructure created by the project.
The project will initially focus on partnerships with colleges and universities, but ultimately Vishwanatha said he hoped to expand the program to the K-12 level. All data and lessons will also be publicly available, so that even any student can learn about artificial intelligence regardless of the academic resources available at their school.
âIt could be a community college, it could be a small tribal college in a rural area, but you will still have the same access as anyone in a big city to be able to use this technology,â Vishwanatha said.
Step two: Use artificial intelligence to address health care disparities
Once the consortium develops the educational tools to train future leaders in artificial intelligence, these students will address some of the biggest questions in health research, using electronic health records and other data sources to fight against disparities in health outcomes.
Health disparities are visible across a range of different identities, based on gender, gender orientation and gender identity, race and ethnicity, and geography. In Fort Worth, residents of ZIP code 76104 have the lowest life expectancy in all of Texas, according to a survey released by the Star-Telegram in September 2020. In 76104, the average life expectancy was 66, 7 years in 2019, almost 12 years. younger than the national average.
Vishwanatha and other leaders in the AI ââcommunity are hopeful that carefully designed systems can actually address disparities like those experienced by people who live in 76104. A diverse workforce and more comprehensive data could potentially use social determinants of health to keep people from getting sick in the first place.
A described potential Vishwanatha is a partnership between medical clinics and a community to better understand what factors contribute to a health disparity. For example, in local health clinics, patients’ medical information is typically recorded in their electronic health records, which can be anonymized for researchers to analyze. But if patients were willing to provide additional information, such as their access to fresh food and exercise opportunities, income level, and stressors in their lives, researchers could potentially link this information to their health records. . Access to such information, however, would require communities to trust that the consortium could handle this information responsibly.
âWe need to collect this data and link it to the electronic health record,â Vishwanatha said. “And the only people who can provide that is the community.”