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               funding sources and no conflicts of interest related to this financial arrangement.


               Conflicts of interest
               All authors declared that there are no conflicts of interest.


               Ethical approval and consent to participate
               The study was performed in accordance with the ethical principles as stated in the Declaration of Helsinki.
               This study was reviewed and determined to be exempt by the Institutional Review Board (IRB) at the
               University of California, San Francisco. The study met the criteria for exemption as it involved anonymous
               survey data collection with minimal risk to participants. Participation was entirely voluntary, and all
               participants provided informed consent electronically before beginning the survey. Participants were
               informed about the purpose of the study, the approximate time commitment, and their right to withdraw at
               any time without penalty.

               Consent for publication
               Not applicable.

               Copyright
               © The Author(s) 2025.

               REFERENCES
               1.       Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text
                   summarization. Nat Med. 2024;30:1134-42.  DOI  PubMed  PMC
               2.       Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)?
                   The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9:e45312.  DOI
                   PubMed  PMC
               3.       Denecke K, May R, Rivera Romero O; LLMHealthGroup. Potential of large language models in health care: delphi study. J Med Int
                   Res. 2024;26:e52399.  DOI  PubMed  PMC
               4.       Clusmann J, Kolbinger FR, Muti HS, et al. The future landscape of large language models in medicine. Commun Med. 2023;3:141.
                   DOI  PubMed  PMC
               5.       Karabacak M, Margetis K. Embracing large language models for medical applications: opportunities and challenges. Cureus.
                   2023;15:e39305.  DOI  PubMed  PMC
               6.       Liu J, Wang C, Liu S. Utility of ChatGPT in clinical practice. J Med Int Res. 2023;25:e48568.  DOI  PubMed  PMC
               7.       Amin K, Doshi R, Forman HP. Large language models as a source of health information: are they patient-centered? A longitudinal
                   analysis. Healthc. 2024;12:100731.  DOI  PubMed
               8.       Wright JD, Chen L, Suzuki Y, Matsuo K, Hershman DL. National estimates of gender-affirming surgery in the US. JAMA Netw Open.
                   2023;6:e2330348.  DOI  PubMed  PMC
               9.       Hirpara MM, Amin L, Aloyan T, Shilleh N, Lewis P. Does the internet provide quality information on metoidioplasty? Using the
                   modified ensuring quality information for patients tool to evaluate artificial intelligence-generated and online information on
                   metoidioplasty. Ann Plast Surg. 2024;92:S361-5.  DOI  PubMed
               10.      Berry CE, Fazilat AZ, Churukian AA, et al. Quality assessment of online resources for gender-affirming surgery. Plast Reconstr Surg
                   Glob Open. 2023;11:e5306.  DOI  PubMed  PMC
               11.      Vargas CR, Ricci JA, Lee M, Tobias AM, Medalie DA, Lee BT. The accessibility, readability, and quality of online resources for
                   gender affirming surgery. J Surg Res. 2017;217:198-206.  DOI  PubMed
               12.      Kalam KT, Rahman JM, Islam MR, Dewan SMR. ChatGPT and mental health: friends or foes? Health Sci Rep. 2024;7:e1912.  DOI
                   PubMed  PMC
               13.      Gerritse K, Hartman LA, Bremmer MA, Kreukels BPC, Molewijk BC. Decision-making approaches in transgender healthcare:
                   conceptual analysis and ethical implications. Med Health Care Philos. 2021;24:687-99.  DOI  PubMed  PMC
               14.      Lambert A, Pratt A, Conard LAE, et al. Supporting gender-related medical decision making for transgender and gender-diverse
                   individuals: a scoping review. Transgend Health. 2023;8:113-23.  DOI  PubMed  PMC
               15.      Song H, Xia Y, Luo Z, et al. Evaluating the performance of different large language models on health consultation and patient
                   education in urolithiasis. J Med Syst. 2023;47:125.  DOI
               16.      Entwistle VA, France EF, Wyke S, et al. How information about other people’s personal experiences can help with healthcare
                   decision-making: a qualitative study. Patient Educ Couns. 2011;85:e291-8.  DOI
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