Clinical Focus

  • Diabetes and Metabolism
  • Endocrinology

Academic Appointments

Professional Education

  • Fellowship: Stanford University Endocrinology Fellowship (2025) CA
  • Board Certification: American Board of Internal Medicine, Internal Medicine (2023)
  • Residency: Marshfield Clinic Health System (2023) WI
  • Medical Education: Istanbul University, Istanbul Medical Faculty (2017) Turkey

Contact

  • Academic
    takcan@stanford.edu
    University - Other Teaching/Research Department: Medicine - Med/Endocrinology Position: Instructor
  • 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

  • HDL Meets Triglyceride. Journal of lipid research Akcan, T., Kraemer, F. B. 2025: 100796

    View details for DOI 10.1016/j.jlr.2025.100796

    View details for PubMedID 40189208

  • Safe and Effective Use of U-200 Insulin With Automated Insulin Delivery Systems. Clinical diabetes : a publication of the American Diabetes Association Akcan, T., Needleman, L., Basina, M. 2025; 43 (3): 449-452

    View details for DOI 10.2337/cd24-0110

    View details for PubMedID 40741456

    View details for PubMedCentralID PMC12304551

  • Automated Insulin Delivery for Type 1 Diabetes: Present and Future. Diabetes spectrum : a publication of the American Diabetes Association Akcan, T., Lee, M. Y., Needleman, L., Lal, R. A. 2025; 38 (3): 217-227

    Abstract

    Advancements in automated insulin delivery (AID) systems have transformed type 1 diabetes management, making AID the most effective technology for improving metabolic outcomes and quality of life in individuals with the disease. In this article, we review the available AID systems and their key features through case vignettes that illustrate their real-world applications in type 1 diabetes management. We then examine existing gaps in technology and explore future advancements to further enhance AID functionality and adoption.

    View details for DOI 10.2337/dsi25-0003

    View details for PubMedID 40823608

  • Refractory Hypercalcemia In a Patient With Multiple Granulomatous Disorders Velasquez, J., Akcan, T., Kraemer, F., Lin, C., Motlaghzadeh, Y., Sellmeyer, D. E. OXFORD UNIV PRESS. 2024: 233

    View details for Web of Science ID 001361790801224