Rima Arnaout, MD

Associate Professor

Dr. Rima Arnaout is an Assistant Professor in Medicine (Cardiology) and a member of the Bakar Computational Health Sciences Institute, the Biological and Medical Informatics graduate program, and the Center for Intelligent Imaging. She is a physician-scientist with a background in genetics, clinical research and programming, and a practicing cardiologist board-certified in multi-modality cardiovascular imaging. Improving the resolution and accuracy of cardiovascular phenotypes will lead to novel insights and therapies. Dr. Arnaout is currently developing computational methods to bring precision phenotyping to echocardiography. Her background as a physician-scientist informs the future scope of this work as a technology that can transform non-invasive imaging into a big-data tool for both research and clinical use.

Education
2021 - Diversity, Equity and Inclusion Champion Training, University of California
MD, - Medicine, Harvard Medical School
- Internal Medicine Residency, Massachusetts General Hospital
SB, - Biology, Bioengineering, Massachusetts Institute of Technology
- Cardiology Fellowship, University of California, San Francisco
Honors and Awards
  • UCSF Chen Scholar, Tianqiao and Chrissy Chen Institute, 2024
  • Fellow, American Institute for Biological and Medical Engineering, 2023
  • Fellow, American Society of Echocardiography, 2022
  • Emerging Leader in Health and Medicine, National Academy of Medicine, 2022
  • Sarnoff Scholar Award, Sarnoff Cardiovascular Research Foundation, 2013-2015
  • Pierre and Christine Lamond Research Fellow in Cardiology, UCSF, 2012-2013
  • National Institutes of Health Loan Repayment Program Award, NIH, 2011-2013
  • Sarnoff Fellow Award, Sarnoff Cardiovascular Research Foundation, 2005-2006
  • AAAS Mass Media Science and Engineering Fellowship Award, Voice of America, 2004-2005
Websites
Publications

  1. Kagiyama N, Tokodi M, Hathaway QA, Arnaout R, Davies R, Dey D, Duchateau N, Fraser AG, Goto S, Jamthikar AD, Lam CSP, Oikonomou EK, Ouyang D, Pandey A, Poterucha TJ, Raisi-Estabragh Z, Strom JB, Zhang Q, Yanamala N, Sengupta PP. PRIME 2.0: An Update to The Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation Checklist. JACC. Cardiovascular imaging 2025. PMID: 40892627


  3. Wang Q, Tang TM, Youlton M, Weldy CS, Kenney AM, Ronen O, Hughes JW, Chin ET, Sutton SC, Agarwal A, Li X, Behr M, Kumbier K, Moravec CS, Tang WHW, Margulies KB, Cappola TP, Butte AJ, Arnaout R, Brown JB, Priest JR, Parikh VN, Yu B, Ashley EA. Epistasis regulates genetic control of cardiac hypertrophy. Nature cardiovascular research 2025. PMID: 40473955


  4. Couch J, Li M, Arnaout R, Arnaout R. X-Factor: Quality Is a Dataset-Intrinsic Property. arXiv 2025. PMID: 40492246


  5. Miao BY, Chen IY, Williams CYK, Davidson J, Garcia-Agundez A, Sun S, Zack T, Saria S, Arnaout R, Quer G, Sadaei HJ, Torkamani A, Beaulieu-Jones B, Yu B, Gianfrancesco M, Butte AJ, Norgeot B, Sushil M. The MI-CLAIM-GEN checklist for generative artificial intelligence in health. Nature medicine 2025. PMID: 39915678


  6. Ferreira DL, Lau C, Salaymang Z, Arnaout R. Self-supervised learning for label-free segmentation in cardiac ultrasound. Nature communications 2025. PMID: 40307208


  7. Subramaniam S, Rizvi S, Ramesh R, Sehgal V, Gurusamy B, Arif H, Tran J, Thamman R, Anyanwu EC, Mastouri R, Mackensen GB, Arnaout R. Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports. Scientific Reports 2025. PMID: 39953053


  8. Ferreira DL, Arnaout R. Are AI foundation models efficient for segmentation of echocardiograms? Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2025. PMID: 39929395


  9. Reddy A, Rizvi S, Moon-Grady AJ, Arnaout R. Improving prenatal detection of congenital heart disease with a scalable composite analysis of six fetal cardiac ultrasound biometrics. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2024. PMID: 39209237


  10. Couch J, Arnaout R, Arnaout R. Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning. arXiv 2024. PMID: 39830079


  11. Wang Q, Tang TM, Youlton N, Weldy CS, Kenney AM, Ronen O, Hughes JW, Chin ET, Sutton SC, Agarwal A, Li X, Behr M, Kumbier K, Moravec CS, Tang WHW, Margulies KB, Cappola TP, Butte AJ, Arnaout R, Brown JB, Priest JR, Parikh VN, Yu B, Ashley EA. Epistasis regulates genetic control of cardiac hypertrophy. medRxiv : the preprint server for health sciences 2023. PMID: 37987017


  12. Arnaout R. Adapting vision-language AI models to cardiology tasks. Nature medicine 2024. PMID: 38693248


  13. Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ronen O, Ye C, Ashley E, Butte AJ, Arnaout R, Brown B, Priest J, Yu B. Learning epistatic polygenic phenotypes with Boolean interactions. PloS one 2024. PMID: 38625909


  14. Datar Y, Cuddy SAM, Ovsak G, Giblin GT, Maurer MS, Ruberg FL, Arnaout R, Dorbala S. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2024. PMID: 38395112


  15. Sachdeva R, Armstrong AK, Arnaout R, Grosse-Wortmann L, Han BK, Mertens L, Moore RA, Olivieri LJ, Parthiban A, Powell AJ. Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement. Journal of the American College of Cardiology 2024. PMID: 38171712


  16. Nguyen P, Arora R, Hill ED, Braun J, Morgan A, Quintana LM, Mazzoni G, Lee GR, Arnaout R, Arnaout R. $\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets. arXiv 2023. PMID: 39070042


  17. Wang Q, Tang T, Youlton N, Weldy C, Kenney A, Ronen O, Hughes J, Chin E, Sutton S, Agarwal A, Li X, Behr M, Kumbier K, Moravec C, Tang WHW, Margulies K, Cappola T, Butte A, Arnaout R, Brown J, Priest J, Parikh V, Yu B, Ashley E. Epistasis regulates genetic control of cardiac hypertrophy. Research square 2023. PMID: 38045390


  18. Spector-Bagdady K, Armoundas AA, Arnaout R, Hall JL, Yeager McSwain B, Knowles JW, Price WN, Rawat DB, Riegel B, Wang TY, Wiley K, Chung MK, American Heart Association Advocacy Coordinating Committee. Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association. Circulation 2023. PMID: 37646159


  19. Arnaout R. ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram? Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2023. PMID: 37499771


  20. Dey D, Arnaout R, Antani S, Badano A, Jacques L, Li H, Leiner T, Margerrison E, Samala R, Sengupta PP, Shah SJ, Slomka P, Williams MC, Bandettini WP, Sachdev V. Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care. JACC. Cardiovascular imaging 2023. PMID: 37480904


  21. Chinn E, Arora R, Arnaout R, Arnaout R. ENRICHing medical imaging training sets enables more efficient machine learning. Journal of the American Medical Informatics Association : JAMIA 2023. PMID: 37036945


  22. Arnaout R, Hahn RT, Hung JW, Jone PN, Lester SJ, Little SH, Mackensen GB, Rigolin V, Sachdev V, Saric M, Sengupta PP, Strom JB, Taub CC, Thamman R, Abraham T. The (Heart and) Soul of a Human Creation: Designing Echocardiography for the Big Data Age. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2023. PMID: 37191597


  23. Athalye C, Arnaout R. Domain-guided data augmentation for deep learning on medical imaging. PloS one 2023. PMID: 36952442


  24. Athalye C, van Nisselrooij A, Rizvi S, Haak M, Moon-Grady AJ, Arnaout R. Deep learning model for prenatal congenital heart disease (CHD) screening generalizes to the community setting and outperforms clinical detection. medRxiv : the preprint server for health sciences 2023. PMID: 38903074


  25. Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Islam SMS, Hadley D, Arnaout R, Beygui RE. Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records. Frontiers in cardiovascular medicine 2022. PMID: 36505372


  26. Arnaout R, Arnaout R. Visualizing omicron: COVID-19 deaths vs. cases over time. PloS one 2022. PMID: 35439253


  27. Dabiri Y, Yao J, Mahadevan VS, Gruber D, Arnaout R, Gentzsch W, Guccione JM, Kassab GS. Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes. Frontiers in cardiovascular medicine 2021. PMID: 34957251


  28. Kornblith AE, Addo N, Dong R, Rogers R, Grupp-Phelan J, Butte A, Gupta P, Callcut RA, Arnaout R. Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma. 2015 Year Book of Pediatrics. Ed. Cabana MD. (Elsevier; Philadelphia, PA) 2021. PMID: 34741469


  29. Arnaout R. Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography? JACC. Cardiovascular imaging 2021. PMID: 34274276


  30. Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nature medicine 2021. PMID: 33990806


  31. Arnaout R.. Intelligence-based medicine. Chang AC, editor Deep learning for medical ultrasound 2021. PMID:


  32. Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. Journal of the American College of Cardiology 2021. PMID: 33478654


  33. Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ashley E, Butte A, Arnaout R, Brown B, Priest J, Yu B.. Learning epistatic polygenic phenotypes with Boolean interactions bioRxiv 2020. PMID:


  34. Kakarmath S, Esteva A, Arnaout R, Harvey H, Kumar S, Muse E, Dong F, Wedlund L, Kvedar J. Best practices for authors of healthcare-related artificial intelligence manuscripts. NPJ digital medicine 2020. PMID: 33083569


  35. Kornblith A, Addo N, Dong R, Rogers R, Grupp-Phelan J, Butte A, Gupta P, Callcut R, Arnaout R.. Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST) medRxiv 2020. PMID:


  36. Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nature medicine 2020. PMID: 32908275


  37. Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC. Cardiovascular imaging 2020. PMID: 32912474


  38. Arnaout R, Curran L, Zhao Y, Levine J, Chinn E, Moon-Grady A.. Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning medRxiv 2020. PMID:


  39. Arnaout R, Nah G, Marcus G, Tseng Z, Foster E, Harris IS, Divanji P, Klein L, Gonzalez J, Parikh N. Pregnancy complications and premature cardiovascular events among 1.6 million California pregnancies. Open heart 2019. PMID: 30997125


  40. Arnaout R. Toward a clearer picture of health. Nature medicine 2019. PMID: 30613101


  41. Guerra A, Germano RF, Stone O, Arnaout R, Guenther S, Ahuja S, Uribe V, Vanhollebeke B, Stainier DY, Reischauer S. Distinct myocardial lineages break atrial symmetry during cardiogenesis in zebrafish. eLife 2018. PMID: 29762122


  42. Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ digital medicine 2018. PMID: 30828647


  43. Brown D, Samsa LA, Ito C, Ma H, Batres K, Arnaout R, Qian L, Liu J. Neuregulin-1 is essential for nerve plexus formation during cardiac maturation. Journal of cellular and molecular medicine 2017. PMID: 29265764


  44. Gut P, Reischauer S, Stainier DYR, Arnaout R. LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE. Physiological reviews 2017. PMID: 28468832


  45. Madani A, Arnaout R, Mofrad M, Arnaout R.. Fast and accurate classification of echocardiograms using deep learning ArXiv e-prints. 2017. PMID:


  46. Brown D, Samsa L, Ito C, Ma H, Arnaout R, Qian L, Liu J.. Neuregulin-1 is essential for nerve plexus formation during cardiac maturation Journal of Molecular and Cellular Medicine. doi: 10.1111/jcmm.13408 2017. PMID:


  47. Orr N, Arnaout R, Gula LJ, Spears DA, Leong-Sit P, Li Q, Tarhuni W, Reischauer S, Chauhan VS, Borkovich M, Uppal S, Adler A, Coughlin SR, Stainier DY, Gollob MH. A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation. Nature communications 2016. PMID: 27066836


  48. Arnaout R, Reischauer S, Stainier DY. Recovery of adult zebrafish hearts for high-throughput applications. Journal of visualized experiments : JoVE 2014. PMID: 25548868


  50. Arnaout R, Stainier DY. Developmental biology: physics adds a twist to gut looping. Current biology : CB 2011. PMID: 22032191


  51. Arnaout R, Thorson A. Late Recognition of Malignant Vasovagal Syncope. Cardiac electrophysiology clinics 2010. PMID: 28770764


  52. Chi NC, Shaw RM, Jungblut B, Huisken J, Ferrer T, Arnaout R, Scott I, Beis D, Xiao T, Baier H, Jan LY, Tristani-Firouzi M, Stainier DY. Genetic and physiologic dissection of the vertebrate cardiac conduction system. PLoS biology 2008. PMID: 18479184


  53. Arnaout R, Ferrer T, Huisken J, Spitzer K, Stainier DY, Tristani-Firouzi M, Chi NC. Zebrafish model for human long QT syndrome. Proceedings of the National Academy of Sciences of the United States of America 2007. PMID: 17592134