Bio

Rita Lee is a Physician Assistant that specializes in Cardiac Surgery. She received her Master of Science degree in Physician Assistant Studies from the Weill Cornell Graduate School of Medical Sciences in New York which has a surgical focus. She is involved with patient care in both the operating room and outpatient clinic settings.

Clinical Focus

  • Cardiac Surgery
  • Coronary Artery Disease
  • Aortic Valve Disease
  • Mitral Valve Disease
  • Tricuspid Valve Insufficiency
  • Aortic Aneurysm, Thoracic
  • Aortic Dissection
  • Hypertrophic Cardiomyopathy
  • Ventricular Assist Devices
  • Physician Assistant

Professional Education

  • Board Certification: National Commission on Certification of Physician Assistants, Physician Assistant (2020)
  • Bachelor of Science, University of California, Los Angeles, Physiological Science (2017)
  • Professional Education: Weill Cornell Medical College Physician Assistants Program (2020) NY

Contact

  • Clinical (Primary)  Dept of Cardiothoracic Surgery 300 Pasteur Dr Falk Bldg CVRB Stanford, CA 94305 (650) 725-4520 (fax)

Additional Clinical Info

All Publications

  • Almanac - Retrieval-Augmented Language Models for Clinical Medicine. NEJM AI Zakka, C., Shad, R., Chaurasia, A., Dalal, A. R., Kim, J. L., Moor, M., Fong, R., Phillips, C., Alexander, K., Ashley, E., Boyd, J., Boyd, K., Hirsch, K., Langlotz, C., Lee, R., Melia, J., Nelson, J., Sallam, K., Tullis, S., Vogelsong, M. A., Cunningham, J. P., Hiesinger, W. 2024; 1 (2)

    Abstract

    Large language models (LLMs) have recently shown impressive zero-shot capabilities, whereby they can use auxiliary data, without the availability of task-specific training examples, to complete a variety of natural language tasks, such as summarization, dialogue generation, and question answering. However, despite many promising applications of LLMs in clinical medicine, adoption of these models has been limited by their tendency to generate incorrect and sometimes even harmful statements.We tasked a panel of eight board-certified clinicians and two health care practitioners with evaluating Almanac, an LLM framework augmented with retrieval capabilities from curated medical resources for medical guideline and treatment recommendations. The panel compared responses from Almanac and standard LLMs (ChatGPT-4, Bing, and Bard) versus a novel data set of 314 clinical questions spanning nine medical specialties.Almanac showed a significant improvement in performance compared with the standard LLMs across axes of factuality, completeness, user preference, and adversarial safety.Our results show the potential for LLMs with access to domain-specific corpora to be effective in clinical decision-making. The findings also underscore the importance of carefully testing LLMs before deployment to mitigate their shortcomings. (Funded by the National Institutes of Health, National Heart, Lung, and Blood Institute.).

    View details for DOI 10.1056/aioa2300068

    View details for PubMedID 38343631

    View details for PubMedCentralID PMC10857783