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

Publisher Correction: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
Authors: Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med
View full abstract on Pubmed
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
Authors: Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med
View full abstract on Pubmed
Erratum: Author Correction: Human-machine partnership with artificial intelligence for chest radiograph diagnosis.
Authors: Patel BN, Rosenberg L, Willcox G, Baltaxe D, Lyons M, Irvin J, Rajpurkar P, Amrhein T, Gupta R, Halabi S, Langlotz C, Lo E, Mammarappallil J, Mariano AJ, Riley G, Seekins J, Shen L, Zucker E, Lungren MP.
NPJ Digit Med
View full abstract on Pubmed
Human-machine partnership with artificial intelligence for chest radiograph diagnosis.
Authors: Patel BN, Rosenberg L, Willcox G, Baltaxe D, Lyons M, Irvin J, Rajpurkar P, Amrhein T, Gupta R, Halabi S, Langlotz C, Lo E, Mammarappallil J, Mariano AJ, Riley G, Seekins J, Shen L, Zucker E, Lungren M.
NPJ Digit Med
View full abstract on Pubmed
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Authors: Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, Bereket M, Patel BN, Yeom KW, Shpanskaya K, Halabi S, Zucker E, Fanton G, Amanatullah DF, Beaulieu CF, Riley GM, Stewart RJ, Blankenberg FG, Larson DB, Jones RH, Langlotz CP, Ng AY, Lungren MP.
PLoS Med
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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.
Authors: Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP.
PLoS Med
View full abstract on Pubmed