| Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT HX Bai, B Hsieh, Z Xiong, K Halsey, JW Choi, TML Tran, I Pan, LB Shi, ... Radiology 296 (2), E46-E54, 2020 | 1553 | 2020 |
| Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT HX Bai, R Wang, Z Xiong, B Hsieh, K Chang, K Halsey, TML Tran, ... Radiology 296 (3), E156-E165, 2020 | 434 | 2020 |
| The RSNA pediatric bone age machine learning challenge SS Halabi, LM Prevedello, J Kalpathy-Cramer, AB Mamonov, A Bilbily, ... Radiology 290 (2), 498-503, 2019 | 377 | 2019 |
| Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation A Mahajan, CN Spracklen, W Zhang, MCY Ng, LE Petty, H Kitajima, ... Nature genetics 54 (5), 560-572, 2022 | 269 | 2022 |
| Deep learning: an update for radiologists PM Cheng, E Montagnon, R Yamashita, I Pan, A Cadrin-Chênevert, ... Radiographics 41 (5), 1427-1445, 2021 | 94 | 2021 |
| Deep learning in neuroradiology: a systematic review of current algorithms and approaches for the new wave of imaging technology AD Yao, DL Cheng, I Pan, F Kitamura Radiology: Artificial Intelligence 2 (2), e190026, 2020 | 77 | 2020 |
| Tackling the radiological society of North America pneumonia detection challenge I Pan, A Cadrin-Chênevert, PM Cheng American Journal of Roentgenology 213 (3), 568-574, 2019 | 66 | 2019 |
| Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks I Pan, S Agarwal, D Merck Journal of digital imaging 32, 888-896, 2019 | 59 | 2019 |
| Machine learning for social services: a study of prenatal case management in Illinois I Pan, LB Nolan, RR Brown, R Khan, P van der Boor, DG Harris, R Ghani American journal of public health 107 (6), 938-944, 2017 | 58 | 2017 |
| Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data R Wang, Z Jiao, L Yang, JW Choi, Z Xiong, K Halsey, TML Tran, I Pan, ... European radiology 32, 205-212, 2022 | 52 | 2022 |
| Deep learning-based classification of primary bone tumors on radiographs: A preliminary study Y He, I Pan, B Bao, K Halsey, M Chang, H Liu, S Peng, RA Sebro, J Guan, ... EBioMedicine 62, 2020 | 51 | 2020 |
| Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging R Wang, Y Cai, IK Lee, R Hu, S Purkayastha, I Pan, T Yi, TML Tran, S Lu, ... European radiology 31, 4960-4971, 2021 | 46 | 2021 |
| Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA machine learning challenge I Pan, HH Thodberg, SS Halabi, J Kalpathy-Cramer, DB Larson Radiology: Artificial Intelligence 1 (6), e190053, 2019 | 46 | 2019 |
| Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation A Mahajan, CN Spracklen, W Zhang, MCY Ng, LE Petty, H Kitajima, ... MedRxiv, 2020.09. 22.20198937, 2020 | 31 | 2020 |
| Evaluating progress in automatic chest x-ray radiology report generation F Yu, M Endo, R Krishnan, I Pan, A Tsai, EP Reis, EKUN Fonseca, ... Patterns 4 (9), 2023 | 27 | 2023 |
| Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs I Pan, GL Baird, S Mutasa, D Merck, C Ruzal-Shapiro, DW Swenson, ... Radiology: Artificial intelligence 2 (4), e190198, 2020 | 27 | 2020 |
| SCU‐Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms X Guo, WC O'Neill, B Vey, TC Yang, TJ Kim, M Ghassemi, I Pan, ... Medical physics 48 (10), 5851-5861, 2021 | 19 | 2021 |
| Reproducible artificial intelligence research requires open communication of complete source code FC Kitamura, I Pan, TL Kline Radiology: Artificial Intelligence 2 (4), e200060, 2020 | 12 | 2020 |
| Multi-ancestry genome-wide study in> 2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications K Suzuki, K Hatzikotoulas, L Southam, HJ Taylor, X Yin, KM Lorenz, ... medRxiv, 2023 | 11 | 2023 |
| A systematic review of deep learning applications for optical coherence tomography in age-related macular degeneration SK Paul, I Pan, WM Sobol Retina 42 (8), 1417-1424, 2022 | 11 | 2022 |
