AI Tool Catches Missed Illnesses Associated with Inflammatory Bowel Disease

Beyond symptoms like abdominal pain, weight loss, and poor appetite, IBD is often linked to a range of other health issues, including autoimmune, cardiovascular and mental health disorders. Recognizing these associated conditions early is essential for effective management, but many go unnoticed. Now, a new artificial intelligence (AI)-powered tool can help patients and clinicians visualize and detect illnesses that frequently occur alongside IBD.

The solution was developed by researchers at the University of Alberta (Edmonton, AB, Canada) after analyzing health records from 30,334 Albertans diagnosed with IBD over a span of 20 years. Their work, recently published in a Nature Portfolio journal, originates from a multidisciplinary effort to understand the broader systemic effects of IBD using data-driven tools. At the core of their innovation is the use of “network medicine”—an advanced analytical approach that maps and connects disease patterns across patient data. This interactive tool visually represents the occurrence, clustering, sequence and frequency of disorders associated with IBD. The goal is to make it easier for healthcare providers to identify comorbidities that may be otherwise overlooked in conventional diagnostic pathways. Findings from the study show that 57% of IBD patients have at least one additional autoimmune or extraintestinal disorder. The most frequently seen comorbidities were mental health issues and musculoskeletal disorders.

However, the analysis also revealed elevated rates of skin and respiratory conditions, cardiovascular disease, and disorders affecting the genitals and urinary tract. The researchers identified clusters of patients suffering from anxiety, depression, and chronic pain, pointing to systemic inflammation as the underlying cause affecting not just the gut, but the brain, blood vessels, and other organs. This AI-based tool has the potential to transform IBD care by providing a more holistic and personalized view of each patient’s condition. It enables more comprehensive diagnosis and helps inform the choice of targeted therapies that address the full spectrum of disease manifestations. Looking ahead, the team plans to build predictive models using Alberta’s unique health data to forecast the progression of IBD in individual patients, predict their response to treatment, and reduce the risk of future complications.

“The takeaway for the patient is that while they may be seeing a gastroenterologist with expertise in inflammatory bowel disease because they experience diarrhea, rectal bleeding or abdominal pain, it may turn out that they actually have several other conditions as well,” said principal investigator Daniel C. Baumgart, professor of medicine and adjunct professor of computing science. “Physicians will want to ensure that patients are managed by a multidisciplinary team, and the tools help to find out which other conditions to actively screen for.”