Pranav Rajpurkar
Pranav Rajpurkar, PhD
Member of the Faculty of Biomedical Informatics, Harvard Medical School
10 Shattuck Street, Boston, MA 02115

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

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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Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.
Authors: Rajpurkar P, Yang J, Dass N, Vale V, Keller AS, Irvin J, Taylor Z, Basu S, Ng A, Williams LM.
JAMA Netw Open
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Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.
Authors: Irvin JA, Kondrich AA, Ko M, Rajpurkar P, Haghgoo B, Landon BE, Phillips RL, Petterson S, Ng AY, Basu S.
BMC Public Health
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AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining.
Authors: Rajpurkar P, Park A, Irvin J, Chute C, Bereket M, Mastrodicasa D, Langlotz CP, Lungren MP, Ng AY, Patel BN.
Sci Rep
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PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.
Authors: Huang SC, Kothari T, Banerjee I, Chute C, Ball RL, Borus N, Huang A, Patel BN, Rajpurkar P, Irvin J, Dunnmon J, Bledsoe J, Shpanskaya K, Dhaliwal A, Zamanian R, Ng AY, Lungren MP.
NPJ Digit Med
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Impact of a deep learning assistant on the histopathologic classification of liver cancer.
Authors: Kiani A, Uyumazturk B, Rajpurkar P, Wang A, Gao R, Jones E, Yu Y, Langlotz CP, Ball RL, Montine TJ, Martin BA, Berry GJ, Ozawa MG, Hazard FK, Brown RA, Chen SB, Wood M, Allard LS, Ylagan L, Ng AY, Shen J.
NPJ Digit Med
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