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IntroductionThe 2024 Voice AI Symposium, hosted by the Bridge2AI-Voice Consortium in Tampa, FL, featured two keynote speeches that addressed the intersection of voice AI, healthcare, ethics, and law. Dr. Rupal Patel and Dr. Nicholson Price provided insights into the advancements, applications, and challenges of AI-driven voice tools in healthcare. The symposium aimed to advance cross-disciplinary collaboration and establish frameworks for the ethical use of AI technologies in healthcare.MethodsThe keynote speeches, delivered on May 1st and 2nd, were 30 minutes each, followed by 10-minutes Q&A sessions. The audio was recorded and transcribed using Whisper (v7.13.1). Content summaries were generated with the aid of ChatGPT (v4.0), and the authors reviewed and edited the final transcripts to ensure accuracy and clarity.ResultsDr. Rupal Patel’s keynote, “Reflections and New Frontiers in Voice AI”, explored the potential of voice AI for early detection of health conditions, monitoring disease progression, and promoting non-invasive global health management. She highlighted innovative uses beyond traditional applications, such as examining menopause-related symptoms. Dr. Nicholson Price’s keynote, “Governance for Clinical Voice AI”, addressed the regulatory and ethical challenges posed by AI in healthcare. He stressed the need for context-aware systems and dynamic legal frameworks to address liability and accountability.ConclusionsThe 2024 Voice AI Symposium highlighted the transformative potential of voice AI for early detection, health monitoring, and reducing healthcare disparities. It also underscored the importance of dynamic governance to address the ethical and regulatory challenges of deploying AI in clinical settings.

IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0–17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.

Abstract Objectives Telemedicine can improve access to pediatric otolaryngology care by decreasing travel time and cost, and lowering the risk of viral transmission during the SARS‐CoV‐2 (COVID‐19) pandemic. This study aims to identify the clinical role and outcomes of telemedicine for tracheostomy‐dependent children before and during the COVID‐19 pandemic. Methods Retrospective chart review of 42 tracheostomy‐dependent pediatric patients who utilized telemedicine between October 2013 and April 2020 (pre‐COVID‐19), and 111 patients who utilized telemedicine between May 2020 and July 2021 (during COVID‐19) at a tertiary free‐standing children's hospital outpatient clinic. Results The majority of pre‐COVID‐19 telecommunication solely addressed tracheostomy stomal concerns as compared with during COVID‐19 (99% vs. 3%, p

There is a long history of conversations about integrating business and arts-based learning, but they are taking on more urgency today as technology-induced change and global interconnectivity are altering how humans learn, create, and construct new knowledge in unprecedented ways. However, there is much still to be learned about how the disciplines might be integrated and in what ways they can jointly serve the development not only of university students, but of how professional practice itself is defined. Over the past three years, faculty from the Theater and Dance Performance Studies, Art Practice, and Business disciplines at UC Berkeley have collaborated to create a course, Collaborative Innovation, that explores both collaboration and innovation at the intersection of these three fields. This paper presents a framework for a genuinely integrated interdisciplinary class that interweaves personal development and growth with problem framing and solving skills, and diverse-team participation and leadership. Quotes from student reflection papers bring alive the transformational experiences students went through in this course. The integration of socially engaged art, business, and theater/performance through collaborative teamwork tackling important and challenging social problems opens unexpected potential for student development as future contributors to society.

There is a long history of conversations about integrating business and arts-based learning, but they are taking on more urgency today as technology-induced change and global interconnectivity are altering how humans learn, create, and construct new knowledge in unprecedented ways. However, there is much still to be learned about how the disciplines might be integrated and in what ways they can jointly serve the development not only of university students, but of how professional practice itself is defined. Over the past three years, faculty from the Theater and Dance Performance Studies, Art Practice, and Business disciplines at UC Berkeley have collaborated to create a course, Collaborative Innovation, that explores both collaboration and innovation at the intersection of these three fields. This paper presents a framework for a genuinely integrated interdisciplinary class that interweaves personal development and growth with problem framing and solving skills, and diverse-team participation and leadership. Quotes from student reflection papers bring alive the transformational experiences students went through in this course. The integration of socially engaged art, business, and theater/performance through collaborative teamwork tackling important and challenging social problems opens unexpected potential for student development as future contributors to society.

Background: Latarjet has become a common treatment option for patients with shoulder instability in the setting of bone loss. The coracoid is commonly secured with screws Methods: All patients who underwent Latarjet with suture-button fixation with minimum 1-year follow-up were eligible for inclusion. Preoperative demographic and clinical outcome data including American Shoulder and Elbow Surgeons (ASES), Single Assessment Numerical Evaluation (SANE), and Visual Analog Scale (VAS) were recorded and compared with postoperative scores. Radiographs were reviewed for signs of nonunion. Complications were recorded. Results: Overall 21 patients (76% male, average age: 30.4 ± 11.3 years) underwent Latarjet with suture-button fixation. Significant improvements at 1 year were seen in ASES (P < 0.001), SANE (P < 0.001), and VAS (P = 0.011) scores compared with preoperative scores. Of the 21 patients who had reached 1-year follow-up, 17 (81%) reached 2-year follow-up. For the 17 patients who reached 2-year follow-up, there were significant improvements in ASES (P = 0.001), SANE (P = 0.001), and VAS (P = 0.005) scores from preoperative values. When isolating the 17 patients with 2-year follow-up, there were no significant differences between their 1-year and 2-year ASES (P = 0.73), SANE (P = 0.17), and VAS (P = 0.37) scores. Overall, 3 patients (14%) sustained a complication (one redislocation, one with coracoid migration and a fibrous union, and one superior labral tear requiring biceps tenodesis and superior labral repair). Conclusion: Suture-button fixation of the coracoid during the Latarjet provides encouraging clinical and radiographic outcomes at 1 and 2 years.

Background: Total shoulder arthroplasty (TSA) is an effective treatment option for glenohumeral arthritis. Historically, this surgical procedure was performed on an inpatient basis. There has been a recent trend in performing TSA on an outpatient basis in the proper candidates. Methods: All patients who underwent outpatient TSA performed by a single surgeon between 2015 and 2017 were included. Demographic information and clinical outcome scores, as well as data on complications, readmissions, and revision surgical procedures, were recorded. This group of patients was then compared with a matched cohort of patients who underwent inpatient TSA over the same period. Results: Overall, 94 patients (average age, 60.4 years; 67.0% male patients) underwent outpatient TSA and were included. Patients who underwent outpatient TSA showed significant improvement in all clinical outcome scores at both 1 and 2 years postoperatively. The control group consisted of 77 patients who underwent inpatient TSA (average age, 62.6 years; 53.2% male patients). No significant differences in complications or improvements in clinical outcome scores were found between the inpatient and outpatient groups. Conclusion: TSA performed in an outpatient setting is a safe and reliable procedure that provides significant improvement in clinical outcome scores and no difference in complication rates compared with inpatient TSA.

Abstract LiDAR (light detection and ranging) allows for the quantification of three‐dimensional seascape structure, which is an important driver of coral reef communities. We hypothesized that three‐dimensional LiDAR‐derived covariables support more robust models of coral reef fish assemblages, compared to models using 2D environmental co variables. Predictive models of coral reef fish density, diversity, and biomass were developed using linear mixed effect models. We found that models containing combined 2D and 3D covariables outperformed models with only 3D covariables, followed by models containing only 2D covariables. Areas with greater three‐dimensional structure provide fish more refuge from predation and are crucial to identifying priority management locations that can potentially enhance reef resilience and recovery. Two‐dimensional seascape metrics alone do not adequately capture the elements of the seascape that drive reef fish assemblage characteristics, and the application of LiDAR data in this work serves to advance seascape ecology theory and practice in the third dimension.