Browsing by Author "Abouyared, Marianne"

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    Clinical Management Update of Oral Leukoplakia: A Review From the American Head and Neck Society Cancer Prevention Service
    (Wiley, 2025) Gates, James C.; Abouyared, Marianne; Shnayder, Yelizaveta; Farwell, D. Gregory; Day, Andrew; Alawi, Faizan; Moore, Michael; Holcomb, Andrew J.; Birkeland, Andrew; Epstein, Joel; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Background: Oral potentially malignant disorders (OPMDs) occur in up to 4%-5% of the population, of which oral leukoplakia (OL) is the most common subtype. Predicting high-risk OL remains a challenge. Early diagnosis and effective treatment are thought to be of paramount importance to improve outcomes. Methods: We searched PubMed and Clinicaltrials.gov data for updates in the clinical management of OL from 2015 to current. Results: Recent publication of large cohorts of patients with OL aids in counseling patients regarding risk of malignant transformation. Management for OL includes surveillance, excision, and laser surgery, as well as local and systemic approaches to chemoprevention. Several new entities show promise regarding candidate biomarkers, chemoprevention agents, and diagnostic adjuncts, though all require further validation. Conclusion: This update serves to further inform clinical management of OL and provide impetus for future investigations.
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    Gender Bias in Artificial Intelligence-Written Letters of Reference for Otolaryngology Residency Candidates
    (2025-04-25) Young, Grace; Abouyared, Marianne; Kejner, Alexandra; Patel, Rusha; Edwards, Heather; Yin, Linda; Farlow, Janice
    Introduction/Background: Written letters of reference (LORs) are an important component of the residency application process, and human-written LORs have been shown to contain gender-bias. Given that AI tools such as ChatGPT are increasingly utilized to draft LORs, it is important to understand how bias may be perpetuated in these tools. Study objective/Hypothesis: In a previous study, we identified gender bias in AI-written LORs when using prompts with randomly-generated resume variables. We sought to investigate whether this bias persisted using real applicant experiences, and how this compared to the LORs written by otolaryngology faculty. Methods: We obtained 46 LORs for otolaryngology residency applicants written by faculty from 5 different institutions who regularly compose LORs. Prompts describing the candidate’s experiences using the exact phrasing as the letter writers were provided to ChatGPT4.0 in individual sessions. The writer-generated and AI-generated letters were compared using a gender-bias calculator (https://slowe.github.io/genderbias/) which reports the ratio of male-associated ‘ability’ words to female-associated ‘grindstone’ words. Results: Both the writer-generated and AI-generated letters exhibited male bias on average (18.7% and 37.2% respectively). We used a paired t-test to determine that the AI-generated letters exhibited significantly higher male bias (t-statistic: -4.27, p-value: 0.0001). Independent t-tests did not reveal a significant difference for male versus female applicants for either writer-generated (t-statistic: 1.54, p-value 0.131) or AI-generated letters (t-statistic: 0.14, p-value: 0.892). However, Levene’s test comparing variation in scores indicated AI had significantly lower variability than for writers (Levene’s statistic: 11.38, p-value: 0.0011), and notably, every single AI-generated letter was male biased. 54.3% of the LORs were written for male candidates. Conclusions: While the use of AI for letter drafting resulted in overall male-bias, there was not a significant difference between letters using male versus female names, and the results did not vary as much as human-written letters. This suggests that AI-drafts could help reduce gender discrepancies. Further research is necessary to explore the broader implications of AI-assisted letter writing in residency selection, particularly in non-technical contexts.
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    Portrait of a Surgeon: Artificial Intelligence Reflections
    (Sage, 2024-04-17) Farlow, Janice L.; Abouyared, Marianne; Rettig, Eleni M.; Kejner, Alexandra; Edwards, Heather A.; Patel, Rusha; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Text-to-image artificial intelligence (AI) programs are popular public-facing tools that generate novel images based on user prompts. Given that they are trained from Internet data, they may reflect societal biases, as has been shown for text-to-text large language model programs. We sought to investigate whether 3 common text-to-image AI systems recapitulated stereotypes held about surgeons and other health care professionals. All platforms queried were able to reproduce common aspects of the profession including attire, equipment, and background settings, but there were differences between programs most notably regarding visible race and gender diversity. Thus, historical stereotypes of surgeons may be reinforced by the public's use of text-to-image AI systems, particularly those without procedures to regulate generated output. As AI systems become more ubiquitous, understanding the implications of their use in health care and for health care-adjacent purposes is critical to advocate for and preserve the core values and goals of our profession.