A new long COVID study could be a “game changer,” according to local researchers who found that an AI tool can identify hidden cases of the mysterious condition from patient health records.

While some diagnostic studies suggest that about 7% of the population suffers from long COVID, this new approach from Mass General Brigham researchers revealed a much higher 22.8% of the population.

The Mass General Brigham scientists developed the AI algorithm to sift through electronic health records to help clinicians identify cases of long COVID — an often mysterious condition that can be debilitating and lead to chronic fatigue, cough, and brain fog.

The study could help identify more people who should be receiving care for long COVID, according to the researchers, who said the greater 22.8% figure may align more closely with national trends and paint a more realistic picture of the pandemic’s long-term toll.

“Our AI tool could turn a foggy diagnostic process into something sharp and focused, giving clinicians the power to make sense of a challenging condition,” said senior author Hossein Estiri, head of AI Research at the Center for AI and Biomedical Informatics of the Learning Healthcare System at Mass General Brigham.

“With this work, we may finally be able to see long COVID for what it truly is — and more importantly, how to treat it,” added Estiri, who’s also an associate professor of medicine at Harvard Medical School.

The algorithm used in the AI tool was developed by drawing patient data from the clinical records of nearly 300,000 patients across 14 hospitals and 20 community health centers in the Mass General Brigham system.

The AI uses a novel method developed by Estiri and colleagues called “precision phenotyping,” which sifts through individual records to identify symptoms and conditions linked to COVID, and to track symptoms over time in order to differentiate them from other illnesses.

For instance, the algorithm can detect if shortness of breath may be the result of pre-existing conditions like heart failure or asthma rather than long COVID. Only when every other possibility was exhausted would the tool flag the patient as having long COVID.

“Physicians are often faced with having to wade through a tangled web of symptoms and medical histories, unsure of which threads to pull, while balancing busy caseloads,” said Alaleh Azhir, the co-lead author who’s an internal medicine resident at Brigham Women’s Hospital. “Having a tool powered by AI that can methodically do it for them could be a game changer.”

The patient-centered diagnoses may also help alleviate biases built into current diagnostics for long COVID, according to the researchers.

Their study showed that the individuals they identified as having long COVID mirror the broader demographic makeup of Massachusetts — unlike long COVID algorithms that rely on a single diagnostic code or individual clinical encounters, skewing results toward certain populations such as those with more access to care.

“This broader scope ensures that marginalized communities, often sidelined in clinical studies, are no longer invisible,” said Estiri.

Future studies may explore the algorithm in cohorts of patients with specific conditions, like COPD or diabetes. The researchers also plan to release this algorithm publicly on open access where physicians and healthcare systems globally can use it in their patient populations.

In addition to opening the door to better clinical care, this work may lay the foundation for future research into the genetic and biochemical factors behind long COVID’s various subtypes.

Estiri said, “Questions about the true burden of long COVID—questions that have thus far remained elusive—now seem more within reach.”