Routinely collected patient data can also be used by researchers who are trying to better understand social determinants of health. For example, AI data synthesis can help researchers more easily study social risks that are linked to the development of Alzheimer’s disease, such as marital status and education level.
“AI can comb through patient data and find people who might be at a greater risk of developing Alzheimer’s, for example, and they can be monitored to see how their health progresses and may also become eligible to participate in clinical trials,” says Yonghui Wu, PhD, director of natural language processing at the UF Clinical and Translational Science Institute.
Transforming care for clinicians and patients
Understanding the limitations of AI-generated conclusions and ensuring that implicit biases are being addressed is at the forefront for UF researchers. AI systems developed at UF routinely undergo rounds of study using different populations to better understand how algorithms might change across populations. Researchers also review data sets to flag and potentially remove pieces of information for study that have minimal impact on causality.
Increasing doctor-patient engagement
Medical chatbots like SynGatorTron™, developed by UF researchers, can use conversational language to communicate with and educate patients in much the same way people now interact with Apple’s Siri and Amazon’s Alexa. SynGatorTron™ can generate synthetic patient data untraceable to real patients, which can be used to train the next generation of medical AI systems to understand conversational language and medical terminology. Having a medical chatbot available in a health care system can decrease barriers to care for patients after hours and allow for real-time conversations about their health, no appointment necessary.
Detecting and diagnosing disease
Using a branch of AI known as machine learning, UF researchers have zeroed in on the specific bacteria suspected of causing depression and high blood pressure. In another UF study, researchers combined an AI computer algorithm with MRI brain scans to accurately predict whether people with a specific type of early memory loss will go on to develop Alzheimer’s disease or other forms of dementia.
Personalizing treatment and informing care
UF researchers have developed and successfully tested an AI system that delivers streamlined and timely details about crucial changes in a patient’s condition. The system, known as Deep Sequential Organ Failure Assessment, or DeepSOFA, works by collecting, organizing and presenting a patient’s medical data so doctors can make nimbler, better-informed decisions. DeepSOFA buys critical time by indicating which intensive care unit patients may need a lifesaving intervention to prevent potentially fatal conditions, and it can be a powerful predictive tool to help doctors determine how a patient’s condition is trending and what may be causing that change.
Training the next generation
While the College of Medicine’s clinicians and researchers are pioneering the use of AI in the medical field, students and trainees are taking a deep dive into one of the nation’s first curriculums centered on the intersection of AI and medical education.
“AI is becoming part of our daily lives in so many ways that are sometimes obvious, and sometimes less readily apparent,” says Patrick Tighe, MD ’05, MS, the associate dean for AI application & innovation and an associate professor of anesthesiology at UF. “It’s important for physicians to understand how these tools work from both a theoretical perspective and a more pragmatic perspective, to understand how AI can be applied in clinical practice as well as for broader population health.”
With these goals in mind, Tighe is one of the leaders on a team of College of Medicine faculty who are developing a comprehensive AI curriculum aimed at clinicians, students and trainees who want to learn more about ways to incorporate data and machine learning into patient care. Chris Giordano, MD, an associate professor of anesthesiology, and Francois Modave, PhD, a professor of AI in the department of anesthesiology, are also leading the development of the curriculum.
The first online course, which was created in partnership with the UF College of Education, launched this spring for various members of the college community and will continue to roll out into 2023. It allows those with a clinical background to learn the basics of AI and how to become further involved in AI research.