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Artificial intelligence knowledge of evidence-based recommendations in gender affirmation surgery and gender identity: is ChatGPT aware of WPATH recommendations?

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European Journal of Plastic Surgery Aims and scope Submit manuscript

Abstract

Background

Artificial intelligence (AI) is evolving rapidly as are its uses in healthcare and scientific literature. There are concerns about whether AI like ChatGPT has implicit biases. This study explores ChatGPT’s ability to reference evidence-based recommendations related to gender-affirming surgery (GAS).

Methods

ChatGPT was prompted using open-ended questions on GAS as well as the World Professional Association for Transgender Health Standards of Care (WPATH SOC) for the Health of Transgender and Gender Diverse People, Version 8’s statements of recommendations. Responses were analyzed based on agreement to and reference of WPATH SOC recommendations.

Results

A total of 95 WPATH statements of recommendations were given to the chatbot. There were 70 (74%) agreements, 0 (0%) disagreements, and 25 (26%) neutral responses. WPATH was directly referenced in 12 (13%) responses. ChatGPT was successful in describing aspects of gender diversity, including the treatment of gender dysphoria.

Conclusions

While often using neutral language, ChatGPT does intermittently reference WPATH and its evidence-based recommendations. As AI evolves, so can the spread of misinformation if it is not rooted in evidence-based recommendations. Furthermore, AI may serve as a viable tool for patient education on GAS.

Level of evidence: Not gradable

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Data Availability

Data was gathered using ChatGPT and its corresponding responses.

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Acknowledgements

We would like to acknowledge OpenAI’s ChatGPT for its contributions to the questions answered and generated supplements.

Funding

The authors received no funding for data collection or preparation of the manuscript.

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Author notes

  1. Daniel Najafali and Chandler Hinson contributed equally to this work.

Authors and Affiliations

  1. Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Daniel Najafali

  2. Frederick P. Whiddon College of Medicine, University of South Alabama, Mobile, AL, USA

    Chandler Hinson

  3. Drexel University College of Medicine, Philadelphia, PA, USA

    Justin M. Camacho

  4. Northeast Ohio Medical University College of Medicine, Rootstown, OH, USA

    Logan G. Galbraith

  5. University of Minnesota Medical School, Minneapolis, MN, USA

    Tannon L. Tople

  6. Division of Plastic and Reconstructive Surgery, University of Washington, Seattle, WA, USA

    Danielle Eble

  7. Division of Plastic and Reconstructive Surgery, Rush University Medical Center, Chicago, IL, USA

    Brielle Weinstein, Loren S. Schechter & Amir H. Dorafshar

  8. Division of Plastic and Reconstructive Surgery, University of Washington at Harborview Medical Center, Seattle, WA, USA

    Shane D. Morrison

Authors
  1. Daniel Najafali
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Contributions

DN: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

CH: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

JMC: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

LGG: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

TLT: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

DE: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

BW: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

LSS: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

AHD: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

SDM: conceptualization, methodology, investigation, writing—original draft, writing—review and editing

All authors approved the final version.

Corresponding author

Correspondence to Daniel Najafali.

Ethics declarations

Ethical approval and informed consent

This article does not contain any studies involving human participants or animals performed by any of the authors. For this type of study, formal consent is not required.

Conflict of interest

Amir H. Dorafshar is entitled to a technology he invented based on a licensing agreement between KLS Martin and the Johns Hopkins University, is entitled to receive royalties on a book he published under a licensing agreement with Elsevier, receives an honorarium for educational lectures from De Puy Synthes, receives indirect research support from KLS Martin and De Puy Synthes, and has received payment from KCI as a consultant. Daniel Najafali, Chandler Hinson, Justin M. Camacho, Logan G. Galbraith, Tannon L. Tople, Danielle Eble, Brielle Weinstein, Loren S. Schechter, and Shane D. Morrison declare no competing interests.

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Najafali, D., Hinson, C., Camacho, J.M. et al. Artificial intelligence knowledge of evidence-based recommendations in gender affirmation surgery and gender identity: is ChatGPT aware of WPATH recommendations?. Eur J Plast Surg 46, 1169–1176 (2023). https://doi.org/10.1007/s00238-023-02125-6

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  • DOI: https://doi.org/10.1007/s00238-023-02125-6

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