Pranav Rajpurkar

Pranav Rajpurkar, PhD

Assistant Professor of Biomedical Informatics

10 Shattuck Street, Boston, MA 02115

Rajpurkar Lab

Pranav Rajpurkar is driven by a fundamental passion for building reliable artificial intelligence (AI) technologies for biomedical decision making. His lab approaches biomedical problems with a computational lens, developing AI algorithms, datasets, and interfaces that cut across computer vision, natural language processing, and structured health data. He has collaborated with clinicians across medical specialties, including radiology, cardiology, and pathology, to make some of the first demonstrations of expert-level deep learning algorithms and their effects on clinician decision making. Previously, Dr. Rajpurkar received his B.S., M.S., and Ph.D. degrees, all in Computer Science from Stanford University.

His lab’s current research directions include algorithm development for limited labeled data settings, high-quality dataset curation at scale, and the design of effective clinician-AI collaboration setups.

DBMI Research Areas

Courses

In the News

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2.
Authors: Le AT, Wu M, Khan A, Phillips N, Rajpurkar P, Garland M, Magid K, Sibai M, Huang C, Sahoo MK, Bowen R, Cowan TM, Pinsky BA, Hogan CA.
Front Microbiol
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AI in health and medicine.
Authors: Rajpurkar P, Chen E, Banerjee O, Topol EJ.
Nat Med
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CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.
Authors: Irvin JA, Pareek A, Long J, Rajpurkar P, Eng DK, Khandwala N, Haug PJ, Jephson A, Conner KE, Gordon BH, Rodriguez F, Ng AY, Lungren MP, Dean NC.
J Thorac Imaging
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Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.
Authors: Hogan CA, Rajpurkar P, Sowrirajan H, Phillips NA, Le AT, Wu M, Garamani N, Sahoo MK, Wood ML, Huang C, Ng AY, Mak J, Cowan TM, Pinsky BA.
EBioMedicine
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A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.
Authors: Hariton E, Chi EA, Chi G, Morris JR, Braatz J, Rajpurkar P, Rosen M.
Fertil Steril
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Improving hospital readmission prediction using individualized utility analysis.
Authors: Ko M, Chen E, Agrawal A, Rajpurkar P, Avati A, Ng A, Basu S, Shah NH.
J Biomed Inform
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Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.
Authors: Chi EA, Chi G, Tsui CT, Jiang Y, Jarr K, Kulkarni CV, Zhang M, Long J, Ng AY, Rajpurkar P, Sinha SR.
JAMA Netw Open
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Automated coronary calcium scoring using deep learning with multicenter external validation.
Authors: Eng D, Chute C, Khandwala N, Rajpurkar P, Long J, Shleifer S, Khalaf MH, Sandhu AT, Rodriguez F, Maron DJ, Seyyedi S, Marin D, Golub I, Budoff M, Kitamura F, Takahashi MS, Filice RW, Shah R, Mongan J, Kallianos K, Langlotz CP, Lungren MP, Ng AY, Patel BN.
NPJ Digit Med
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DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set.
Authors: Vrabac D, Smit A, Rojansky R, Natkunam Y, Advani RH, Ng AY, Fernandez-Pol S, Rajpurkar P.
Sci Data
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CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV.
Authors: Rajpurkar P, O'Connell C, Schechter A, Asnani N, Li J, Kiani A, Ball RL, Mendelson M, Maartens G, van Hoving DJ, Griesel R, Ng AY, Boyles TH, Lungren MP.
NPJ Digit Med
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