01SPEAKING · MEDIA
Speaking and media requests
Share the event or outlet, audience, format, deadline, and topic for keynotes, panels, grand rounds, podcasts, interviews, or expert comment.
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Contact
Three focused forms route each request to the right review process. Applications to join the lab go through the Join page, not email.
02RESEARCH PARTNERSHIPS
Explore sponsored research or a collaborative grant
The lab collaborates with industry often. Its research has been supported by Microsoft, Google, and Amazon alongside NIH R01 funding, and with Gradient Health it released ReXGradient-160K, one of the largest public chest X-ray datasets. Propose a question aligned with one of the lab's research programs, with scope, timeline, and budget context.
This is preliminary intake. Any proposal or agreement is reviewed and authorized through Harvard.
03SUPPORT OUR WORK
Help ambitious medical AI research move forward
Gift support can advance open research, train early-career scientists, and provide the infrastructure needed to turn technical progress into clinical evidence. Philanthropy already powers signature efforts: the Biswas Family Foundation, in partnership with the Milken Institute, funds the lab's MAIDA Initiative to democratize global medical imaging data.
This form starts a conversation; it does not accept or process a gift. Contributions are coordinated through Harvard Medical School Alumni Affairs and Development.
Exploring a hospital or industry collaboration? See how collaborations with the lab work. Looking to join the lab as a postdoc, predoc, or Bootcamp participant? Start on the Join page.
SPEAKER KIT
For organizers: a ready-to-use talk title, abstract, and bio. To invite Pranav, use the speaking form above.
Talk: Reason, Test, Assist: Medical AI for Complex Clinical Work
Medical AI began by matching specialists one image and one task at a time. Complex clinical work asks for more: reading every scan a patient has, earning trust before meeting a patient, and helping with the physical work of a procedure. This talk covers the three research programs of the Rajpurkar Lab. Reason: generalist models that read and write across images and text, tie each finding to its evidence in 3D, and find what no one asked them to look for, such as heart risk in routine emergency head CTs. Test: why fixed benchmarks overstate readiness, from a held-out report generation evaluation across 67 sites to one AI tested at 23 hospitals in 12 countries, and toward simulated hospitals where every decision changes what happens next. Assist: teaching robots to help in the operating room and at the bedside, including a low-cost robot arm that learned to follow sutures alongside a surgeon from under an hour of demonstrations, and video models for ultrasound and colonoscopy. The talk ends with the open problems in each.
Short version
Medical AI matched specialists one task at a time. Complex clinical work asks for AI that reasons from evidence, is tested before it is trusted, and learns to assist with the physical work of care. Drawing on generalist imaging models, evaluation across many hospitals, simulated hospitals, and surgical robotics, this talk covers what that takes and what remains open.
Bio and headshot
Speaker bios in four lengths, plus a high-resolution headshot, are maintained at pranavrajpurkar.com/bio.