Department of Otolaryngology—Head and Neck Surgery Works

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Browsing Department of Otolaryngology—Head and Neck Surgery Works 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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    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.