Spears Business professor Luo strives to make lifesaving health care more accessible
Wednesday, May 14, 2025
Media Contact: Mack Burke | Associate Director of Media Relations | 405-744-5540 | editor@okstate.edu
Dr. Xiao Luo’s research revolves around unlocking the tremendous potential of electronic medical records to help physicians and patients.
Luo, a Spears School of Business management science and information systems professor, holds a doctorate in computer science from Canada’s Dalhousie University and a bachelor’s degree in the same major from China’s Huazhong University of Science and Technology. She is exploring a big question that supports Oklahoma State University’s interdisciplinary focus on public health innovation.
How can data and artificial intelligence assist physicians to keep us alive?
Luo started focusing on this topic while working in the Canadian health care system. There, she noticed troves of patient data that often went underutilized, like boxes of valuables stowed away in a dusty attic.
“I thought, ‘OK, maybe I should come to academia to do some research, to work with physicians on how we can improve the health care system in general by utilizing those data,’” Luo said. “That’s the first motivation. That’s why I work with physicians in the health care domain.”
Luo is collaborating with Dr. Alan Sawchuk, an Indiana University professor of surgery, to create an AI-powered, personalized system allowing physicians and patients to assess and understand stroke risks. In summer 2024, her proposal was one of seven to receive a $10,000 seed grant as part of the Spears Sponsored Research Initiation Program at OSU.
The program supported Luo’s study, “Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records,” published in February 2025 in the Journal of Cardiovascular Development and Disease. Spears Business student Jackson Silvey is also an author of this paper, along with IU researchers Mackenzie Madison, Robert Brenner, Kartik Gannamaneni and Sawchuk.
"What I learned from Dr. Luo is the art of digging into data and understanding what
patterns exist within it.”
Luo thrives at the intersection of data-driven technology and medicine, and her latest study is one of many examples. In her lab, she works with Spears Business undergraduate students to develop a personalized system for preventing diseases such as breast and lung cancer. They are partnering with OSU Medicine to incorporate data from Oklahoma. Luo is also creating AI-powered systems to help senior patients understand lab test results and assist first responders in providing emergency medical services for children.
With her groundbreaking research, Luo strives to make lifesaving health care more accessible. Particularly in rural areas, patients might not have access to technologies such as advanced biomedical imaging systems, but electronic health records are readily available tools that do not require excessive costs.
“We can analyze large amounts of data, look at the changes in the data, look at the trends in the data and do the personalized analysis,” Luo said.
After arriving at a doctor’s office, a patient typically fills out paperwork in the waiting room and then answers a nurse’s questions about health history and habits.
Those seemingly ordinary steps produce valuable data points that could, thanks to Luo’s models, preserve a patient’s quality of life and drastically reduce medical expenses by helping to prevent conditions such as stroke. According to the Centers for Disease Control and Prevention, stroke occurs in more than 795,000 people in a year in the United States. Additionally, expenses associated with stroke totaled almost $56.2 billion in the U.S. between 2019-20, per the CDC.
Patients with asymptomatic carotid stenosis, a diagnosis characterized by artery blockage or narrowing, might never have externally visible health complications until a stroke occurs.
Luo said ACS patients often undergo operations to prevent stroke, but the procedures are sometimes unnecessary. Occasionally, the surgeries themselves increase health risks, particularly for aging patients. Sometimes, a lifestyle change might be enough to minimize risk. No two humans are the same, so health care providers cannot rely solely on generalized guidelines to evaluate the probability of stroke.
This is why Luo, Sawchuk and their research team are assessing individualized data sets from electronic health records. The AI models synthesize information that includes demographics, comorbidities, vital signs, medication history, health habits and laboratory test results, generating an educated prediction of the patient’s likelihood of experiencing a stroke. This risk assessment guides physicians in determining whether a preventative operation is necessary for an individual. In clinical settings, patients would sign waivers agreeing to input their data into the system as they already do with electronic health records.
Luo is training the AI models to not only predict a patient’s stroke risk but also communicate with physicians and patients to help them understand the calculation.
“If AI only produced a probability, like a 90% chance this patient is going to develop stroke, it’s not convincing because physicians need to know why,” Luo said. “AI needs to explain. How did you come to this conclusion? We call it explainable AI. In order to have physicians utilize these in the clinical setting, it has to be explainable.”
For AI to communicate like a person, the machine needs human coaching.
To build the model, Luo and Sawchuk reviewed data from 872 ACS patients (whose identities were not disclosed) in the Indiana University School of Medicine system from 2009-22. They partnered with physician residents and students who annotated those past records, able to note if the patients did or did not experience stroke.
Compared with the AI-generated risk assessments, these historical outcomes could verify the accuracy of the AI and guide Luo in fine-tuning it. Seventy percent of the initial patient data was used just to train the AI, while 30% was devoted to testing it.
Silvey, a business analytics and data science graduate student, assisted Luo with this preliminary work. The researchers utilized scientific publications and clinical guidelines to equip the AI models with deep knowledge about ACS, essentially building a digital medical encyclopedia for disease prediction and analysis.
“What I learned from Dr. Luo is the art of digging into data and understanding what patterns exist within it,” Silvey said. “It is incredibly easy to make assumptions based on speculative patterns one might see, but it takes patience, time and thought to come up with the answer that holistically makes the most sense.”
With this knowledge base, the AI models can offer an in-depth look at medicine as opposed to scratching the surface. A busy physician typically has a short window of time to meet with one patient before the next appointment begins, Luo pointed out, so they might not be able to review a patient’s comprehensive health history.
“The physician may only look at the recent lab test or compare it to a few previous ones,” Luo said. “For us, we can consider long-term and complex medical history by using artificial intelligence.”
As Luo strives to include clinical data from electronic health records systems across the United States, she is cognizant of the difficulties. While the AI models start as neutral entities, they can, like a child’s mind, develop bias depending on how they are taught. If the sample data were to include only male patients, for example, then the model would not be adequately equipped to guide health care decisions for women.
Luo has paid careful attention to these factors, ensuring to evaluate men and women in the initial patient sample and note differences that could be due to biological sex. She said the research team is also working to add more Black and Hispanic patients to the database, groups that were underrepresented in the IU records from the pilot study.
“AI needs to consume a lot of data in order to make correct decisions,” Luo said. “So, that’s a challenge of ultimately utilizing AI in the real world. We must have an unbiased algorithm, although data might be biased.”
In clinical applications of the models, humans would continue to have the final say about medical decisions. While a physician would communicate with a patient about preventative measures, the AI model would enhance the physician’s knowledge, offering guidelines based on medical literature and the patient’s risk factors.
“When AI communicates with patients, we don’t want to override physicians’ communication because AI does not have enough sense to consider the ethics,” Luo said. “As a physician, you need to have enough empathy when you talk to the patients, in how you deal with situations. We have AI as the assistant, so the physician will direct AI.”
Once these “assistants” are sharp enough for clinical settings, they can apply to numerous areas of medicine. As a Spears Business professor since August 2023, Luo is already exemplifying the difference-making potential of the school’s elevated focus on faculty grants.
“I feel very appreciative to receive the seed grant, and I also feel very proud to work with the school that provides faculty the seed grants to advance their research,” Luo said. “It’s an important research collaboration with different institutions and across different departments at OSU.
“This seed grant not only advances faculty research and encourages more collaborations but can also provide students opportunities to research with me in my lab to understand how we can utilize AI to advance human health to improve health care outcomes.”
Photos by: Gary Lawson
Story by: Hallie Hart | STATE Magazine