How to Use Artificial Intelligence for Chronic Disease Management 

Using AI to treat heart disease 

At several organizations, researchers are taking steps to advance cardiovascular medicine using artificial intelligence.  

Hospitals and researcher centers will use AI to program computers to process and respond to data quickly and accurately to provide better treatment outcomes. Programs can conduct tasks including detecting heart disease, treating strokes, and enhancing diagnostic radiology capabilities. 

At Johns Hopkins University, researchers explored the use of whole-heart computational models to better understand ventricular arrhythmias. According to the research team, whole-heart computational modeling can lead to personalized medicine. 

“Personalized computational modeling of patient hearts is making strides developing models that incorporate the individual geometry and structure of the heart, as well as other patient-specific information,” co-author and professor of biomedical engineering and medicine at Johns Hopkins University, Natalia Trayanova, said in a press release. 

According to researchers, patient-specific models can also use predictive analytics to determine the risk of sudden cardiac death or the outcomes of a cardiac procedure. 

“Patient-specific models are also used for determining the optimal treatment for arrhythmia, both atrial and ventricular, with the latter often based on different biophysical underpinnings,” said Trayanova. 

 “These types of models can enable fast evaluation of medical device settings and patient-​selection criteria, as well as the development of novel therapeutic agents.” 

By pursuing the development of whole-heart computational models, researchers can enhance personalized treatment and risk predictions. Additionally, the model can work collaboratively with artificial intelligence to improve patient outcomes. 

 At Mayo Clinic, the organization provides three examples of recent ways doctors implemented AI research into clinical practice: 

• Helping patients following a stroke: In emergency rooms, when patients come in with a stroke called an intracerebral hemorrhage, they undergo a CT scan. That scan is examined by a computer trained to analyze CT data, cutting the time to diagnosis and limiting brain damage. 
• Preventing heart problems:  Applying AI to ECGs has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump, which can lead to heart failure if left untreated. Mayo Clinic is well situated to advance this use of AI because it has a database of more than 7 million ECGs. First, all identifying patient information is removed to protect privacy. Then this data can be mined to accurately predict heart failure noninvasively, inexpensively, and within seconds. 
• Detecting atrial fibrillation sooner: AI-guided ECGs are also used to detect faulty heart rhythms before any symptoms are evident. 

AI in cancer detection and treatment 

There has been a recent expansion of providers using AI for cancer detection. At Tulane University, researchers discovered that  AI can accurately detect and diagnose colorectal cancer by analyzing tissue scans as well or better than pathologists. 

The researchers gathered over 13,000 images of colorectal cancer from 8,803 subjects and 13 independent cancer centers in China, Germany, and the United States. Then, using images that technicians selected at random, the researchers built a machine learning program. 

The program can recognize images of colorectal cancer, which according to researchers, is one of the most common causes of cancer-related deaths in Europe and the US. 

After developing the performance measurement tool, the researchers compared the machine learning technique to the work done by the pathologists. The study indicated that the average pathologist scored around 0.969 for accuracy when identifying colorectal cancer, while the program scored 0.98, proving to be slightly more accurate than the manual data by pathologists. 

Artificial intelligence can also assist in deciding the best course of treatment for patients. For patients with cancer, researchers can use predictive analytics to determine how an individual will respond to a certain medication. 

Not every patient will have the same response to medications,  so machine learning and predictive analytics can also prevent unnecessary side effects from cancer treatments that may not work for the patient. 

The Georgia Institute of Technology and Ovarian Cancer Institute researchers used machine learning algorithms to determine how their patients would respond to cancer-fighting drugs. 

For the study, professor at the School of Biological, John F. McDonald, and his team created predictive machine learning-based models for 15 distinct cancer types, using data from 499 independent cell lines from the National Cancer Institute. 

The models were then validated with a clinical dataset containing seven chemotherapeutic drugs, administered either singularly or in combination to 23 ovarian cancer patients. The model showed an overall predictive accuracy of 91 percent. 

As artificial intelligence continues to grow in the healthcare space, more providers will utilize the technology to treat chronic diseases and lighten the load for physicians.  

Managing and preventing diabetes with AI 

Over the past several years, researchers have been investigating methods to use AI for diabetes management. Different strategies include self-management, remote patient monitoring, and support from wearable devices.  

In 2019, Rensselaer Polytechnic Institute researchers conducted a studying using AI and big analytics to assess information from thousands of continuous glucose monitor insulin pumps. Those with Type 1 diabetes must test their blood sugar often to determine how much insulin they should infect using a needle or insulin pump.  

However, individuals can get a better idea of their blood sugar levels with continuous glucose monitors by offering blood sugar estimated every five minutes, without finger sticks. Data analysis allowed researchers to build models that can better predict the impact of meals and insulin on glucose levels, leading to better control of blood sugar levels. 

According to researchers, artificial intelligence has assisted in advancing diabetes care. AI-backed mobile health tools have demonstrated improvements in diabetes management, reducing the need for in-person appointments and patient-provider interaction.  

Artificial intelligence also plays an important role in diabetes prevention. According to research by Marshall University, the success of chronic disease prevention and population health management is dependent upon a provider’s ability to identify high-risk patients.  

With big data analytics and AI, practitioners can identify warning signs of illnesses promptly, allowing for a quick treatment turnaround and reducing costs. Providers can identify early signs of Ttype 2 diabetes using risk prediction models.  

To prevent disease progression, physicians can encourage patients to engage in early intervention strategies including, increasing exercise and striving to make healthy food choices.  

As researchers learn more about artificial intelligence capabilities, technology is growing as a tool for effective chronic disease management and prevention.