Over a thousand children visit Boston Children’s Hospital every day. Many get clear diagnoses and begin treatment, but a small subset of pediatric visitors with rare illnesses never get diagnoses at all. That has started to change with the help of AI.

New research from the hospital’s center for rare diseases and the AI company OpenAI reveals that off-the-shelf AI tools can help identify which errors in patients’ genomes might be causing the children’s diseases.

The findings, announced Thursday in the New England Journal of Medicine’s AI-focused publication, NEJM AI, show that OpenAI’s o3 model helped clarify 18 diagnoses for children who had struggled to find causes for their illnesses and symptoms.

“It’s a total game changer,” said one of the study’s lead researchers, Catherine Brownstein, the scientific director of the genetic investigations arm of the Manton Center for Orphan Disease Research at Boston Children’s Hospital. She said the research team analyzed several hundred genomes of patients who had not received diagnoses for their rare diseases.

“It got almost 5% new diagnoses, which doesn’t sound like a lot,” Brownstein acknowledged, “but considering how many times these had already been analyzed, that’s a huge number, and each one means an answer for a family.”

The Manton Center works with over 3,500 individuals globally and across all 50 states who are affected by rare diseases, partnering with hospitals and health centers around the world. Brownstein said that the hospital routinely screens those patients’ genomes against newly identified genes that might give diagnoses to children but that the screenings often do not turn up any new answers.

Finding the genetic cause of a disease is complex — there are around 20,000 protein-coding genes in the human genome. Though sequencing a patient’s full genome is now straightforward, identifying clear cause-and-effect relationships in the messy data of a human genome is not always possible.

“A researcher can only spend so much time on a single case,” said Suyash Shringarpure, a technical researcher at OpenAI who focuses on health applications. “Maybe a case remained unsolved when it came to them first, but a year later a paper was published that clarifies the link between the gene and the disease.”

Today’s leading generative AI systems excel at the sort of data crunching required to identify those relationships that might linger in existing — but scattered — data. Wondering whether OpenAI’s large language models could help geneticists make progress pairing patients’ illnesses with specific genes, Brownstein and the Manton Center’s researchers worked with OpenAI and Shringarpure to see whether publicly available products could provide new answers.

Conducting the research last year, Brownstein and the research team ran the genomes of 376 patients who lacked diagnoses through the o3 system, which was then the most powerful system available. To hunt for diagnostic clues in each genome, the researchers provided the o3 model with clinicians’ notes about the case, a description of the patient’s symptoms and a filtered list of certain genes that might be responsible for the patient’s symptoms. The human research team reviewed all of the systems’ outputs to make any final diagnosis.

Of the 376 cases spanning four different disease areas, the team identified new diagnoses for 10 patients with rare neurodevelopmental diseases, four patients with neuromuscular disorders, two children who had died suddenly without further specification and two patients with early childhood psychosis illnesses.

Kyra Benton was one of the people who received a diagnosis as a result of the new research.

When Benton was nine, her mother became concerned that she seemed to move differently from her peers, starting to walk on her tiptoes and struggling to run with a normal gait. She soon took Benton to see a neuromuscular disease expert in New York City, but the expert had no idea what Benton was facing.

After years of worsening health, Benton visited Boston Children’s Hospital, only to be told it also did not know the root of her disease. She soon faced severe heart problems, and she underwent a tracheotomy when she was only 13.

Benton said she came to terms with never knowing her diagnosis — until researchers from the Manton Center called her last year to let her know that the new research had unearthed a cause for her symptoms: myofibrillar myopathy, a progressive genetic neuromuscular disorder that causes muscle fibers to break down.

“Last summer, about a week before my 20th birthday, we got a call from one of the researchers at the lab,” Benton said. “She said, ‘Hi, we know it’s been about 15 years, but we have some news for you,’ and it kind of just blossomed from there.”

Brownstein said she was shocked that a commercial system like o3 could spot new diagnoses in genomes that have been analyzed many times and that might require days of research for individual human analysts to reach the same conclusions — time that Brownstein and her colleagues just do not have.

“There’s pages upon pages of these genes that I have to get through for a case, while the LLM doesn’t get tired,” she said, noting that there simply are not enough geneticists and analysts to find the biological needle in a genome-wide haystack that might cause a patient’s symptoms.

Adam Rodman, a doctor and expert on the use of AI in medicine at Beth Israel Deaconess Medical Center who was not involved in the research, said the new paper was an exciting demonstration of AI systems’ ability to diagnose diseases when used by doctors. “A diagnostic yield of 5% is truly meaningful and could serve as a significant screening tool to help speed up the reanalysis of significant backlogs of cases,” he told NBC News in a note.

The paper treads similar ground as previous research, which has noted large language models’ ability to search genomes for genes that have been sequenced after a patient was first seen in a hospital. However, the authors stressed that the new research showed how doctors across the country can use commercial AI systems to speed up their work and help patients get elusive diagnoses, democratizing access to critical medical information.

Chunhua Weng, a professor of bioinformatics at Columbia University who was not involved in the new study, said the paper was a “wonderful” contribution to this area of research, though she — like the paper’s research team — cautioned that LLM results still require rigorous human review. “The appropriate use of LLMs in diagnosis requires careful attention to trustworthiness,” Weng told NBC News.

Thursday’s paper also notes that seven of the Manton Center’s identified diagnoses were actually “rediscoveries” — meaning a treatment team in one location had identified a patient’s specific diagnosis but had not shared it with researchers around the world.

Brownstein emphasized that even those rediscoveries were vital so that “when new treatments do come online, we can find the patients right away and make sure that they’re first in line for any new technological development or any new therapy.”

OpenAI’s health team trumpeted the advance, saying the news was proof that today’s publicly available AI systems can make profound differences in patients’ lives.

At the same time, the research team was clear that its findings are not a panacea — being diagnosed with a specific illness is often only an early step toward finding and then pursuing treatment options — and that LLMs are not meant to be used by consumers to treat or diagnose diseases. Instead, the tools can help people and doctors navigate complex medical information.

“We definitely don’t want to overhype this,” said Ashley Alexander, head of health for OpenAI. “But I also want to make sure that people don’t miss what’s happening and what’s possible with even just the version of ChatGPT that’s in their pocket today.”

For her part, Benton was surprised that AI was involved in the breakthrough. “Quite frankly, I’m the type of person that’s not all that much favor of AI,” she told NBC News. “On the other hand, I do acknowledge that it does have its advantages.”

“Such as in this case, where it can lead to massive breakthroughs that can really change people’s lives for the better.”