5 ways AI is making health care better
A message from: The University of Texas at San Antonio
Artificial Intelligence (AI) is transforming health care as we know it.
Looking ahead: Researchers at The University of Texas at San Antonio (UTSA) are leaning into its potential to build a healthier future.
Here are five innovative examples.
1. Next-gen doctors
AI's presence in the medical field is ever-growing — and so is the need for medical practitioners with expertise in this advanced technology to enhance patient care.
How it's done: UT Health San Antonio and UTSA are graduating the first-known students to hold a dual degree in medicine and AI, arming them with the skills to help improve diagnostic and treatment outcomes.
The impact: More access to health care providers with specialized training could mean quicker, more accurate patient diagnoses.
2. Faster trauma care
Timely ER access after a traumatic injury is critical for survival.
That's why UTSA's MATRIX: AI Consortium for Well-Being — a collaboration with UT Tyler and UT Health San Antonio — analyzes the time elapsed between patients' injuries and their arrival at trauma centers.
The strategy: Researchers will use AI to map geographic locations of injury scenes to identify hotspots.
- Once identified, these data sets can inform injury prevention initiatives and interventions, like deploying additional resources to improve response or transit time.
Why it's important: AI-informed decision-making can improve survival rates, reduce the long-term effects of serious injuries and enhance the Texas trauma system's efficiency.
3. Real-time coronary imaging
The challenge: Current coronary imaging technologies are limited in the detailed resolution needed for physicians to predict future cardiac events accurately.
The solution: UTSA and UT Health San Antonio researchers are developing an advanced Generative AI algorithm to interpret coronary intravascular images faster and more reliably than human analysts.
- They've gathered ~2,000 Optical Coherence Tomography scans and histology images over five years for precise information about tissue composition to teach their AI model to recognize different artery plaque and heart tissue types — crucial for identifying vulnerable plaques that could lead to heart attacks.
The goal: Enable clinicians to view inside a patient's coronary artery to assess plaque build-up and future risk of heart attacks in real-time, allowing them to offer preventative care.
4. Individualized treatments for chronic diseases
UTSA AI researchers are partnering with the UT Health San Antonio Center for Precision Health to further technological biomedical breakthroughs.
An example: They're designing ways to target the substances made and used when the body breaks down food, drugs and chemicals — or its own tissue to treat diseases like diabetes, stroke, Alzheimer's and lymphedema.
- By tracking differences in lifestyle and environment with biomarkers and imaging, researchers hope to uncover relevant molecular paths to help doctors design therapies based on a patient's precise diagnoses for individualized treatment.
5. Secure and accurate health care modeling
AI models are more likely to give erroneous data as they get more sophisticated.
Next steps: UTSA researchers are working to control what AI models should and shouldn't learn from a given data set.
- One group of scholars developed AlphaDog to identify where images can be easily exploited and attacked.
- Other UTSA experts have identified the best way for practitioners to use medical image Deep Learning models with the least interference from cyberattacks.
In health care, these discoveries could provide more accurate imaging predictions with less margin of error to improve identification and patient treatment.
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