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The study delves into the crucial role of standardization, collaboration, and education in the integration of artificial intelligence (AI) in otolaryngology. It emphasizes the necessity of large, diverse datasets for effective AI implementation in health care, particularly in otolaryngology, due to its reliance on medical imagery and diverse instruments. The text identifies current barriers, including siloed work in academia and sparse academic-industrial partnerships, while proposing solutions like forming interdisciplinary teams and aligning incentives. Moreover, it discusses the importance of standardizing AI projects through system reporting and advocates for AI education and literacy among otolaryngology practitioners., Competing Interests: Disclosure Y. Bensoussan is funded by the National Institutes of Health, United States through the Common Fund for the project “Voice as a biomarker of Health.” Award number: 3OT2OD032720-01S1. E. Evangelista has no disclosures., (Published by Elsevier Inc.)

Introduction: Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research., Objective: The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research., Methods: A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research., Results: Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%)., Conclusion: To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing., Level of Evidence: 5 Laryngoscope, 134:1333-1339, 2024., (© 2023 The American Laryngological, Rhinological and Otological Society, Inc.)

Objectives/hypothesis: An artificial intelligence (AI) tool was developed using audio clips of cis-male and cis-female voices based on spectral analysis to assess %probability of a voice being perceived as female (%Prob♀). This program was validated with 92% accuracy in cisgender speakers. The aim of the study was to assess the relationship of f o on %Prob♀ by a validated AI tool in a cohort of trans females who underwent intervention to feminize their voice with behavioral modification and/or surgery., Study Design: Cohort study., Methods: Fundamental frequency (f o ) from prolonged vowel sounds (f o /a/) and f o from spontaneous speech (f o -sp) were measured using the Kay Pentax Computerized Speech Lab (Montvale, NJ) in trans females postintervention. The same voice samples were analyzed by the AI tool for %Prob♀. Chi-square analysis and regression models were performed accepting >50% Prob♀ as female voice., Results: Forty-two patients were available for analysis after intervention. f o -sp post-treatment was positively correlated with %Prob♀ (R = 0.645 [P < .001]). Chi-square analysis showed a significant association between AI %Prob♀ >50% for the speech samples and f o -sp >160 Hz (P < .01). Sixteen of 42 patients reached an f o -sp >160 Hz. Of these, the AI program only perceived nine patients as female (>50 %Prob♀)., Conclusion: Patients with f o -sp >160 Hz after feminization treatments are not necessarily perceived as having a high probability of being female by a validated AI tool. AI may represent a useful outcome measurement tool for patients undergoing gender affirming voice care., Level of Evidence: 3 Laryngoscope, 131:2567-2571, 2021., (© 2021 The American Laryngological, Rhinological and Otological Society, Inc.)