AI in medicine helps Boston Children's Hospital crack 18 rare disease diagnosis cases

AI helped diagnose 18 children whose rare diseases had stumped doctors

The Manton Center at Boston Children's Hospital works with over 3,500 people who are affected by rare diseases, partnering with hospitals and health centers around the world.Charles Krupa / AP file 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, like, 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 around the world and across all 50 states who are affected by rare diseases, partnering with hospitals and health centers all over 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 very complex -- there are around 20,000 protein-coding genes in the human genome. Though sequencing a patient's full genome is now straightforward, matching errors or abnormalities in certain genes with illnesses is very complicated -- 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 got 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 research, 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 to identify 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."

Harvard and Boston Children's use AI to revisit unsolved genetic cases

Researchers at Boston Children's Hospital and Harvard used OpenAI's o3 Deep Research model to identify 18 new diagnoses among children with rare genetic diseases that had previously gone unresolved, according to a study published in NEJM AI. The team reanalyzed 376 de-identified pediatric cases that had undergone genetic testing and expert review without yielding a diagnosis. Using the o3 Deep Research model, the clinicians synthesized genetic data, clinical presentations, and medical literature. Diagnoses identified from this analysis included neurodevelopmental disorders, rare neuromuscular diseases, sudden unexpected death in pediatrics, and early-onset psychosis. The tool is designed to assist clinicians and researchers in navigating complex medical information rather than serve as a direct consumer diagnostic product. John Brownstein, Chief Innovation Officer at Boston Children's Hospital, noted the challenge in diagnosing complex cases is often due to cognitive limits. "We combine genetic information, phenotypic information, literature search, and the reasoning of AI to deliver diagnoses to families that were once left without any answers," Brownstein said. The NEJM AI publication is part of a broader initiative at the hospital, which announced in May that its AI efforts had led to over 40 rare disease diagnoses previously deemed unsolvable. OpenAI has committed $50 million to support Boston Children's AI initiatives, which began in early 2025. The hospital reports that more than one-third of its employees now use AI tools in their daily work, resulting in approximately 60,000 hours saved, valued at over $7 million. Rare diseases affect an estimated 300 million people globally, with many families navigating lengthy diagnostic processes. "This was unthinkable before, but is now providing hope to so many families," Brownstein added. The researchers emphasized that each diagnosis still requires clinician verification and interpretation, demonstrating that AI serves to augment, rather than replace, human expertise.

OpenAI Isn't Just Writing Emails -- It's Solving Cold Cases in Medicine

OpenAI's large language model is reshaping the world of medicine. A new study just found that the AI tool helped researchers identify new diagnoses in rare disease cases that had gone unsolved for years. A study published last week in the New England Journal of Medicine's AI-focused publication, NEJM AI, found that OpenAI's o3 Deep Research model helped diagnose 18 children at Boston Children's Hospital. Previously, these children and their families were left in the dark as doctors struggled to find the causes of their rare illnesses. These findings suggest that the end of the search for answers may not be permanent. The researchers called the findings "a total game changer," noting that the software increased new diagnoses by 5 percent. "Which doesn't sound like a lot," Catherine Brownstein, PhD, one of the study's lead researchers told NBC News, "but considering how many times these had already been analyzed, that's a huge number, and each one means an answer for a family." Brownstein is the scientific director of the genetic investigations arm of the Manton Center for Orphan Disease Research at Boston Children's Hospital. The organization conducted the research last year by running the genomes of 376 patients who did not have diagnoses through the o3 system. At the time, that was the most powerful system available.