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Computer Science

D-Index
43
Citations
30451
World Ranking
7728
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3336

Pranav Rajpurkar publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Pranav Rajpurkar sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 103 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Pranav Rajpurkar D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Pranav Rajpurkar sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 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
Tao Wang
Tao Wang Stanford University
Nigam H. Shah
Nigam H. Shah Stanford University
William L. Ellsworth
William L. Ellsworth Stanford University
Gerald J. Berry
Gerald J. Berry Stanford University

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