AI platforms that answer clinical questions promise to make clinicians’ jobs easier by sifting through large volumes of evidence and pulling up relevant information. And it appears the technology is making good on that promise as these platforms become more popular. But which clinicians are using them most, and how, have remained open questions — until now.
A new study published in JAMA shows that the number of clinicians using OpenEvidence increased sharply over the last two years, with trainees and early-career physicians using the platform the most.
“Two key takeaways are that an enormous number of physicians are using AI for clinical care and that they are often using it in areas where they don’t have specific expertise,” said the study’s corresponding author Anupam Jena, M.D., Ph.D., the Joseph P. Newhouse Professor of Health Care Policy at Harvard Medical School and HMS professor of medicine at Massachusetts General Hospital, in the press release.
Researchers from Harvard Medical School and Brigham and Women’s Hospital assessed clinicians’ use of OpenEvidence, as it is the most widely used AI-driven clinical information platform in the country. They examined 271 million queries from 952,008 clinicians between Jan. 1, 2024, and June 30, 2026. Of the clinicians, 556,200 were physicians.
While OpenEvidence did not fund the study, three study co-authors are OpenEvidence employees. The company provided the data, but it was de-identified data for analysis.
Who is using the OpenEvidence platform?
The study shows that the number of active users per month on the platform jumped from 355 in January 2024 to 703,265 in June 2026. The average number of monthly queries per active user rose from 18 to 39.5 in the same period. The share of clinicians who were active users in one month and who queried again the next rose from approximately 70% in early 2024 to 90% in 2026.
Nurse practitioners and physician assistants submitted more queries per active user than physicians in one month. But clinical experience was the biggest driver of platform use. Clinicians with fewer than 10 years in practice submitted 43.4 queries per user per month compared with 29.9 among those with 20 or more years in practice.
This trend could also be seen among physicians. Close to two-thirds of the queries (61.8%) were from physicians within a decade of medical school graduation, while residents and fellows submitted 27.2% of the queries and physicians with 20 or more years of experience submitted 16.5%.
Oncologists and hematologists used the platform most, followed by internal medicine physicians and family medicine physicians. Use of the platform was lower in procedural and diagnostic specialties, such as ophthalmology, radiology and orthopedics.
What are clinicians using AI for?
Clinicians are primarily using the OpenEvidence platform for clinical decision-making, with more than half of physicians (51.9%) saying they are using it for this purpose. Physicians’ queries focused on treatment and management (33.1%) and diagnosis (13.9%).
Similarly, 55% of nurse practitioners are using the OpenEvidence platform for clinical decision-making. A third of their queries (34.3%) focused on treatment and management, and 17.3% on diagnosis.
Among physician specialties, the queries reflected specialty-specific priorities. For example, medication information queries were most common in psychiatry, while queries about specific studies and trials were most common in oncology and hematology and practice guideline queries were most common in gastroenterology and cardiology.
Physicians also frequently queried the platform outside their own specialty. About 70% of surgeons’ queries, for instance, were about non-surgical topics and 73% of anesthesiologists’ were about non-anesthesiology topics. These findings might indicate that AI helps fill in knowledge gaps in areas where the physician has limited expertise.
Queries within a specific specialty were mostly about searching for evidence, such as sifting through medical literature and guidelines, whereas outside-specialty searchers were about getting disease and medication information.
“AI tools have become integrated into routine in clinical practice faster than almost any technology we’ve seen, but until now we’ve had very little data on how clinicians actually use them,” said study first author Vishal Patel, M.D., HMS clinical fellow in surgery at Brigham and Women’s, in the press release. “This research gives us an early picture of where these tools fit into clinical decision-making and where we need to study their accuracy and effects on patient care next.”
Anuja Vaidya has covered the healthcare industry since 2012. She currently covers healthcare IT and innovation, including artificial intelligence, digital healthcare, EHRs and interoperability.
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