Bio

Dr. Seav is a board-certified endocrinologist and Clinical Assistant Professor in the Division of Endocrinology at Stanford and, by courtesy, the Department of Neurosurgery. She graduated from Harvard University with an honors degree in molecular and cellular biology before completing her medical education and residency at the University of California, San Diego. She then completed her endocrinology fellowship at Stanford University.

She has a special interest in disorders that involve the pituitary and adrenal glands such as acromegaly, Cushing disease, hypopituitarism, and functional adrenal adenomas. Dr. Seav is determined to provide her patients with personalized, evidence-based medicine that will allow them to live their best lives. In addition to caring for patients, Dr. Seav is also passionate about medical education and devoted a chief medical residency year teaching medical students, interns, and residents.

In-person and telehealth appointments with Dr. Seav are available at Stanford Endocrinology Clinic at Hoover Pavilion, Pituitary Center at Stanford Neurosciences Health Center, and the Stanford Brain Tumor Center at Stanford Cancer Center.

Clinical Focus

  • Pituitary Disorders
  • Adrenal Disorders
  • Endocrinology
  • Diabetes and Metabolism

Academic Appointments

Professional Education

  • Board Certification: American Board of Internal Medicine, Endocrinology, Diabetes and Metabolism (2024)
  • Board Certification, American Board of Internal Medicine, Endocrinology, Diabetes, and Metabolism (2024)
  • Fellowship: Stanford University Endocrinology Fellowship (2024) CA
  • Board Certification: American Board of Internal Medicine, Internal Medicine (2021)
  • Residency: UCSD Internal Medicine Residency (2021) CA
  • Medical Education: University of California San Diego School of Medicine (2018) CA
  • College, Harvard University, Molecular and Cellular Biology (2012)

Contact

  • Academic
    smseav@stanford.edu
    University - Academic staff Department: Medicine - Med/Endocrinology Position: Clinical Assistant Professor
  • Clinical (Primary)  Endocrinology Academic Office 300 Pasteur Dr Rm S025 MC 5103 Stanford, CA 94305 (650) 725-7085 (fax)

Additional Clinical Info

Additional Info

  • Mail Code: 5103

All Publications

  • Artificial intelligence tools in supporting healthcare professionals for tailored patient care. NPJ digital medicine Kim, J., Chen, M. L., Rezaei, S. J., Hernandez-Boussard, T., Chen, J. H., Rodriguez, F., Han, S. S., Lal, R. A., Kim, S. H., Dosiou, C., Seav, S. M., Akcan, T., Rodriguez, C. I., Asch, S. M., Linos, E. 2025; 8 (1): 210

    Abstract

    Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients' needs and assess clinicians' perceptions about the usefulness of those AI tools. To define patients' issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients' needs substantiated by clinicians' evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond.

    View details for DOI 10.1038/s41746-025-01604-3

    View details for PubMedID 40240489

    View details for PubMedCentralID 5069713

  • Presentation and Management of Highly Differentiated Follicular Carcinoma of Ovarian Origin With DICER1 Gene Variants. JCEM case reports Seav, S., Atiq, M., Lo, Y. C., Shah, J., Dorigo, O., Dosiou, C. 2024; 2 (12): luae223

    Abstract

    Struma ovarii (SO) is a rare subtype of ovarian teratoma composed of more than 50% thyroid tissue. Extraovarian spread of SO, called peritoneal strumosis, was previously considered benign given the lack of histological malignant features. However, the 2020 World Health Organization Classification of Female Genital Tumors reclassified peritoneal strumosis as highly differentiated follicular carcinoma of ovarian origin (HDFCO), highlighting its low-grade malignant potential. We present a 38-year-old woman with SO treated initially with right salpingo-oophorectomy, with recurrence 2 years later with multifocal metastatic lesions in the abdomen and pelvis that was successfully treated with surgical resection, total thyroidectomy, and 157 mCi of I-131. Tumor molecular testing revealed a pathogenic DICER1 variant (c.5428G>T, exon 25). A second variant (c.319delins13, exon 4) was of uncertain significance. Germline testing confirmed the second DICER1 variant and also identified an increased risk variant in the APC gene (c.3920T>A). This is a rare case of extensive HFDCO with DICER1 variants, which has been associated with thyroid cancer. Given the germline DICER1 variant, this may also represent the first reported instance of DICER1 syndrome manifesting as HDFCO. Further research into the prognostic factors and optimal treatment of HFDCO is needed.

    View details for DOI 10.1210/jcemcr/luae223

    View details for PubMedID 39659389

    View details for PubMedCentralID PMC11630053

  • Perspectives on Artificial Intelligence-Generated Responses to Patient Messages. JAMA network open Kim, J., Chen, M. L., Rezaei, S. J., Liang, A. S., Seav, S. M., Onyeka, S., Lee, J. J., Vedak, S. C., Mui, D., Lal, R. A., Pfeffer, M. A., Sharp, C., Pageler, N. M., Asch, S. M., Linos, E. 2024; 7 (10): e2438535

    View details for DOI 10.1001/jamanetworkopen.2024.38535

    View details for PubMedID 39412810

  • Empowering Hospitalized Patients With Diabetes: Implementation of a Hospital-wide CGM Policy With EHR-Integrated Validation for Dosing Insulin. Diabetes care Lee, M. Y., Seav, S. M., Ongwela, L., Lee, J. J., Aubyrn, R., Cao, F. Y., Kalinsky, A., Aparicio Ramos, O., Gu, Y., Kingston, K., Ivanovic, M., Buckingham, B. A., Desai, D., Lal, R. A., Tan, M., Basina, M., Hughes, M. S. 2024

    Abstract

    We aimed to assess the feasibility, clinical accuracy, and acceptance of a hospital-wide continuous glucose monitoring (CGM) policy with electronic health record (EHR)-integrated validation for insulin dosing.A hospital policy was developed and implemented at Stanford Health Care for using personal CGMs in lieu of fingerstick blood glucose (FSBG) monitoring. It included requirements specific to each CGM, accuracy monitoring protocols, and EHR integration. User experience surveys were conducted among a subset of patients and nurses.From November 2022 to August 2023, 135 patients used the CGM protocol in 185 inpatient encounters. This included 27% with type 1 diabetes and 24% with automated insulin delivery systems. The most-used CGMs were Dexcom G6 (44%) and FreeStyle Libre 2 (43%). Of 1,506 CGM validation attempts, 87.8% met the %20/20 criterion for CGM-based insulin dosing and 99.3% fell within Clarke zones A or B. User experience surveys were completed by 27 nurses and 46 patients. Most nurses found glucose management under the protocol effective (74%), easy to use (67%), and efficient (63%); 80% of nurses preferred inpatient CGM to FSBG. Most patients liked the CGM protocol (63%), reported positive CGM interactions with nursing staff (63%), and felt no significant interruptions to their diabetes management (63%).Implementation of a hospital-wide inpatient CGM policy supporting multiple CGM types with real-time accuracy monitoring and integration into the EHR is feasible. Initial feedback from nurses and patients was favorable, and further investigation toward broader use and sustainability is needed.

    View details for DOI 10.2337/dc24-0626

    View details for PubMedID 39140891