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2025

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Computer Science

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  • 2025 - Research.com Rising Stars Award

Overview

Pranav Rajpurkar is affiliated with Harvard University in the United States. Their research intersects primarily with the fields of Medicine and Computer Science, contributing to a diverse range of topics and subfields.

The scientist's main areas of study include:

  • Medicine
  • Computer Science

In terms of subfields, their work focuses on:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Health Informatics
  • Molecular Biology
  • Computer Vision and Pattern Recognition

The primary research topics covered by Pranav Rajpurkar are:

  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • COVID-19 diagnosis using AI
  • AI in cancer detection
  • Topic Modeling
  • Machine Learning in Healthcare
  • Natural Language Processing Techniques

Publication venues frequently featuring their work include:

  • arXiv (Cornell University)
  • npj Digital Medicine
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Biomedical Engineering
  • Nature Medicine

Some of the recent papers authored or co-authored by Pranav Rajpurkar are:

  • AI in health and medicine, 2022, Nature Medicine
  • Foundation models for generalist medical artificial intelligence, 2023, Nature
  • Multimodal biomedical AI, 2022, Nature Medicine
  • Self-supervised learning in medicine and healthcare, 2022, Nature Biomedical Engineering
  • The Current and Future State of AI Interpretation of Medical Images, 2023, New England Journal of Medicine

Their frequent co-authors include:

  • Andrew Y. Ng
  • Matthew P. Lungren
  • Oishi Banerjee
  • Eric J. Topol
  • Julián Acosta

Best Publications

  • SQuAD: 100,000+ Questions for Machine Comprehension of Text

    Pranav Rajpurkar;Jian Zhang;Konstantin Lopyrev;Percy Liang

  • Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network

    Awni Y. Hannun;Pranav Rajpurkar;Masoumeh Haghpanahi;Geoffrey H. Tison

  • Know What You Don't Know: Unanswerable Questions for SQuAD

    Pranav Rajpurkar;Robin Jia;Percy Liang

  • CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison

    Jeremy Irvin;Pranav Rajpurkar;Michael Ko;Yifan Yu

  • CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

    Pranav Rajpurkar;Jeremy Irvin;Kaylie Zhu;Brandon Yang

  • Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

    Pranav Rajpurkar;Jeremy Irvin;Robyn L. Ball;Kaylie Zhu

  • Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

    Pranav Rajpurkar;Awni Y. Hannun;Masoumeh Haghpanahi;Codie Bourn

  • An Empirical Evaluation of Deep Learning on Highway Driving

    Brody Huval;Tao Wang;Sameep Tandon;Jeff Kiske

  • Self-supervised learning in medicine and healthcare

    Unknown

  • Impact of a deep learning assistant on the histopathologic classification of liver cancer.

    Amirhossein Kiani;Bora Uyumazturk;Pranav Rajpurkar;Alex Wang

  • MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs.

    Pranav Rajpurkar;Jeremy Irvin;Aarti Bagul;Daisy Ding

  • Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

    Allison Park;Chris Chute;Pranav Rajpurkar;Joe Lou

  • Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

    Bhavik N. Patel;Louis Rosenberg;Gregg Willcox;David Baltaxe

  • Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

    Akshay Smit;Saahil Jain;Pranav Rajpurkar;Anuj Pareek

  • PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

    Shih Cheng Huang;Tanay Kothari;Imon Banerjee;Chris Chute

  • Automated coronary calcium scoring using deep learning with multicenter external validation

    David Eng;Christopher Chute;Nishith Khandwala;Pranav Rajpurkar

  • AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining.

    Pranav Rajpurkar;Allison Park;Jeremy Irvin;Chris Chute

  • CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV.

    Pranav Rajpurkar;Chloe O'Connell;Amit Schechter;Nishit Asnani

  • RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

    Saahil Jain;Ashwin Agrawal;Adriel Saporta;Steven Q. H. Truong

  • CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation

    Alexander Ke;William Ellsworth;Oishi Banerjee;Andrew Y. Ng

  • MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models

    Hari Sowrirajan;Jingbo Yang;Andrew Y. Ng;Pranav Rajpurkar

  • CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

    Akshay Smit;Saahil Jain;Pranav Rajpurkar;Anuj Pareek

Frequent Co-Authors

Andrew Y. Ng
Andrew Y. Ng Stanford University
Christopher G. Chute
Christopher G. Chute Johns Hopkins University
Sanjay Basu
Sanjay Basu Stanford University
Percy Liang
Percy Liang Stanford University
Michael S. Bernstein
Michael S. Bernstein Stanford University
William L. Ellsworth
William L. Ellsworth Stanford University
Nigam H. Shah
Nigam H. Shah Stanford University
Tao Wang
Tao Wang Stanford University
Li Fei-Fei
Li Fei-Fei Stanford University

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