Angela Wilkins joins the Houston Innovators Podcast to discuss the intersection of data and health care. Photo courtesy

When most people hear about Houston startup Starling Medical, they might think about how much potential the medical device company has in the field of urinalysis diagnostics. But that's not quite where Angela Wilkins's head went.

Wilkins explains on the Houston Innovators Podcast that when she met the company's co-founders, Hannah McKenney and Drew Hendricks, she recognized them as very promising startup leaders taking action on a real health care problem. Starling's device can collect urine and run diagnostics right from a patient's toilet.

"It was one of those things where I just thought, 'They're going to get a bunch of data soon,'" Wilkins says. "The opportunity is just there, and I was really excited to come on and build their AI platform and the way they are going to look at data."

For about a year, Wilkins supported the startup as an adviser. Now, she's working more hands on as chief data officer as the company grows.



Wilkins, who serves as a mentor and adviser for several startups, has a 20-year career in Houston across all sides of the innovation equation, working first at Baylor College of Medicine before co-founding Mercury Data Science — now OmniScience. Most recently she served as executive director of the Ken Kennedy Institute at Rice University.

This variety in her resume makes her super connective — a benefit to all the startups she works with, she explains. The decision to transition to a startup team means she gets to work hands on in building a technology — while bringing in her experience from other institutions.

"I think I've really learned how to partner with those institutions," she says on the show. "I've really learned how to make those bridges, and that's a big challenge that startups face."

"When we talk about the Houston innovation ecosystem, it's something we should be doing better at because we have so many startups and so many places that would like to use better technology to solve problems," she continues.

Wilkins has data and artificial intelligence on the mind in everything she does, and she even serves on a committee at the state level to learn and provide feedback on how Texas should be regulating AI.

"At the end of the day, the mission is to put together a report and strategy on how we think Texas should think about AI," she explains. "It's beyond just using an algorithm, they need infrastructure."

Colorado is the first state to pass legislation surrounding AI, and Wilkins says all eyes are on how execution of that new law will go.

"We should have technology that can be double checked to make sure we're applying it in a way that's fair across all demographics. It's obvious that we should do that — it's just very hard," she says.

"Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers." Photo via UH.edu

Houston expert explains health care's inequity problem

guest column

We are currently in the midst of what some have called the "wild west" of AI. Though healthcare is one of the most heavily regulated sectors, the regulation of AI in this space is still in its infancy. The rules are being written as we speak. We are playing catch-up by learning how to reap the benefits these technologies offer while minimizing any potential harms once they've already been deployed.

AI systems in healthcare exacerbate existing inequities. We've seen this play out into real-world consequences from racial bias in the American justice system and credit scoring, to gender bias in resume screening applications. Programs that are designed to bring machine "objectivity" and ease to our systems end up reproducing and upholding biases with no means of accountability.

The algorithm itself is seldom the problem. It is often the data used to program the technology that merits concern. But this is about far more than ethics and fairness. Building AI tools that take account of the whole picture of healthcare is fundamental to creating solutions that work.

The Algorithm is Only as Good as the Data

By nature of our own human systems, datasets are almost always partial and rarely ever fair. As Linda Nordling comments in a Nature article, A fairer way forward for AI in healthcare, "this revolution hinges on the data that are available for these tools to learn from, and those data mirror the unequal health system we see today."

Take, for example, the finding that Black people in US emergency rooms are 40 percent less likely to receive pain medication than are white people, and Hispanic patients are 25 percent less likely. Now, imagine the dataset these findings are based on is used to train an algorithm for an AI tool that would be used to help nurses determine if they should administer pain relief medication. These racial disparities would be reproduced and the implicit biases that uphold them would remain unquestioned, and worse, become automated.

We can attempt to improve these biases by removing the data we believe causes the bias in training, but there will still be hidden patterns that correlate with demographic data. An algorithm cannot take in the nuances of the full picture, it can only learn from patterns in the data it is presented with.

Bias Creep

Data bias creeps into healthcare in unexpected ways. Consider the fact that animal models used in laboratories across the world to discover and test new pain medications are almost entirely male. As a result, many medications, including pain medication, are not optimized for females. So, it makes sense that even common pain medications like ibuprofen and naproxen have been proven to be more effective in men than women and that women tend to experience worse side effects from pain medication than men do.

In reality, male rodents aren't perfect test subjects either. Studies have also shown that both female and male rodents' responses to pain levels differ depending on the sex of the human researcher present. The stress response elicited in rodents to the olfactory presence of a sole male researcher is enough to alter their responses to pain.

While this example may seem to be a departure from AI, it is in fact deeply connected — the current treatment choices we have access to were implicitly biased before the treatments ever made it to clinical trials. The challenge of AI equity is not a purely technical problem, but a very human one that begins with the choices that we make as scientists.

Unequal Data Leads to Unequal Benefits

In order for all of society to enjoy the many benefits that AI systems can bring to healthcare, all of society must be equally represented in the data used to train these systems. While this may sound straightforward, it's a tall order to fill.

Data from some populations don't always make it into training datasets. This can happen for a number of reasons. Some data may not be as accessible or it may not even be collected at all due to existing systemic challenges, such as a lack of access to digital technology or simply being deemed unimportant. Predictive models are created by categorizing data in a meaningful way. But because there's generally less of it, "minority" data tends to be an outlier in datasets and is often wiped out as spurious in order to create a cleaner model.

Data source matters because this detail unquestionably affects the outcome and interpretation of healthcare models. In sub-Saharan Africa, young women are diagnosed with breast cancer at a significantly higher rate. This reveals the need for AI tools and healthcare models tailored to this demographic group, as opposed to AI tools used to detect breast cancer that are only trained on mammograms from the Global North. Likewise, a growing body of work suggests that algorithms used to detect skin cancer tend to be less accurate for Black patients because they are trained mostly on images of light-skinned patients. The list goes on.

We are creating tools and systems that have the potential to revolutionize the healthcare sector, but the benefits of these developments will only reach those represented in the data.

So, what can be done?

Part of the challenge in getting bias out of data is that high volume, diverse and representative datasets are not easy to access. Training datasets that are publicly available tend to be extremely narrow, low-volume, and homogenous—they only capture a partial picture of society. At the same time, a wealth of diverse health data is captured every day in many healthcare settings, but data privacy laws make accessing these more voluminous and diverse datasets difficult.

Data protection is of course vital. Big Tech and governments do not have the best track record when it comes to the responsible use of data. However, if transparency, education, and consent for the sharing of medical data was more purposefully regulated, far more diverse and high-volume data sets could contribute to fairer representation across AI systems and result in better, more accurate results for AI-driven healthcare tools.

But data sharing and access is not a complete fix to healthcare's AI problem. Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers. At the end of the day, we want to teach our algorithms to make good choices but we are still figuring out what good choices should look like for ourselves.

AI presents the opportunity to bring greater personalization to healthcare, but it equally presents the risk of entrenching existing inequalities. We have the opportunity in front of us to take a considered approach to data collection, regulation, and use that will provide a fuller and fairer picture and enable the next steps for AI in healthcare.

------

Angela Wilkins is the executive director of the Ken Kennedy Institute at Rice University.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Houston biotech VC firm's portfolio cos. score $5.3M in federal funding

money moves

Three portfolio companies of Houston venture capital firm First Bight Ventures have received a combined $5.25 million from the U.S. Defense Department’s Distributed Bioindustrial Manufacturing Program.

“The allocation of funds by the federal government will be critical in helping grow biomanufacturing capacity,” Veronica Breckenridge (née Wu), founder of First Bight, says in a news release. “We are very proud to represent three dynamic companies that are awardees of this competitive and widely praised program.”

The three companies that were awarded Defense Department funds are:

  • Hayward, California-based Visolis, received $2.25 million to plan a facility for production of a chemical that can be transformed into rocket propellants, explosive binders, and sustainable aviation fuel.
  • Alameda, California-based Industrial Microbes received $1.55 million to plan a facility for converting ethanol feedstock into acrylic acid. This acid is a key component used in coatings, adhesives, sealants, lubricants, corrosion inhibitors, and wound dressings.
  • San Diego-based Algenesis received $1.5 million to plan and develop a facility that’ll produce diisocyanates, which are chemical building blocks used to make polyurethane products.

“This award is a testament to our commitment to advancing sustainable materials and will enable us to contribute to both national security and industrial resilience. Our planned facility represents a key step towards securing a domestic supply of critical components for polyurethanes,” says Stephen Mayfield, CEO of Algenesis.

Texas grocer H-E-B finally rolls out digital tap-to-pay services

hi, tech

Texas' favorite grocery store has some good news for shoppers who have a habit of forgetting their wallets. H-E-B is starting a phased rollout for digital tap-to-pay services, starting in San Antonio before spreading to the rest of the chain's stores.

The rollout began Monday, October 7. A release says it'll take "about a week" to spread to all stores in the region before making it ways across Texas. Although it is not known which stores will add the service on what date, the rollout includes all H-E-B stores, including Mi Tienda, H-E-B's Mexican grocery store that has locations in Houston.

With tap to pay, shoppers will finally be able to use smartphone-based systems such as Apple Pay, Samsung Pay, and Google Pay, as well as tapping a physical card.

Payments can be made with those apps, or "digital wallets," at cash registers and self-checkout lanes, as well as restaurants and pharmacies within H-E-B stores. They won't be accepted right away at H-E-B fuel pumps, but customers can use them to pay for gas if they bring their phones to the fuel station payment window.

This isn't exactly cutting-edge technology; Google Wallet launched in 2011, leading the market, and was followed by Apple Pay in 2014. But it's not ubiquitous either. In 2023, a poll by Forbes Advisor found that barely more than half of respondents used digital wallets more than traditional forms of payment.

H-E-B is on a bit of a payment revolutionizing kick, also launching a debit card in 2022 and a partnership in August of 2024 with the H-E-B-owned delivery service Favor for its fastest order fulfillment yet. Central Market and Joe V’s Smart Shop, two other H-E-B brands, also recently launched tap to pay.

“At H-E-B, we’re always exploring a broad range of technologies to enhance how customers shop and pay for products,” H-E-B vice president Ashwin Nathan said in a statement. “This has been one of the most requested services we have received from our customers and partners, and we are excited to now make this popular technology available at all our H-E-B locations.”

------

This article originally ran on CultureMap.