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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.

Background: Cyclic mastalgia (CM) is premenstrual bilateral and diffuse breast pain that presents cyclically and affects women in their reproductive years. It may associate with latent hyperprolactinemia due to the insufficient inhibitory effect of dopamine on the pituitary gland. Vitex agnus-castus (VAC) is known for its dopaminergic activity and its possible actions on CM and latent hyperprolactinemia. However, the treatment effect of VAC on CM remains unclear. Materials and methods: To perform a systematic review and meta-analysis of clinical trials that report on the efficacy of VAC treatment in CM patients, literature search was performed in major research databases. Results: This review includes 25 studies (17 randomized control trials plus eight nonrandomized trials). VAC was effective in relieving breast pain intensity and lowering the increased serum prolactin level in reproductive age CM patients (18–45 years) with or without premenstrual syndromes. Typical dosage was 20–40 mg/day with a treatment duration of 3 months. A conservative meta-analysis included only six studies (n = 718: VAC – 356, placebo – 362) and revealed a moderate effect size (SMD 0.67, 95% CI 0.5–0.85) favoring VAC over a placebo. Seven trials demonstrated VAC to be a noninferior alternative to pharmaceutical therapies for CM, including dopamine agonists, nonsteroidal anti-inflammatory drugs, serotonin reuptake inhibitors, and hormonal contraceptives. VAC was safe and associated with only mild and reversible adverse events. However, the risk of bias in most studies was unclear due to insufficient information. Conclusion: VAC is a safe and effective treatment option for CM. More high-quality clinical trials are needed to strengthen the evidence base.

Sustainable consumption has the potential to hold firms accountable for the negative externalities they impose on society and the environment, but consumers are often unsure whether to believe that the products and companies promoted as being sustainable are truly sustainable. This research investigates novices’ credibility assessments of online sustainability ratings reports using a laboratory experiment and a dual-process theoretical lens. It identifies and operationalizes two new heuristic cues that theory suggests should be influential in this process: the For-profit status of the company that produced the expert reports, and its Strategic Ties. Each participant looked up companies’ sustainability ratings on two databases, one of which was perceived to be significantly easier to use and more credible than the other. Database Credibility and the For-profit status of the company producing the database both significantly affected perceptions of content usefulness. The impact of the Strategic Ties heuristic was inconclusive and merits further research. We are beginning to accumulate significant research on the effects of explicit labels and standards on consumer behavior. This research points to the need to understand the effects of available implicit heuristics as well, and offers many potential avenues for future research.