Shen Hong Law

Hearing loss is a major global health concern, yet many countries lack the necessary workforce to provide hearing care services. This study aimed to evaluate the global distribution, availability, and training of hearing healthcare providers, including audiologists and other professionals involved in hearing care. A cross-sectional electronic survey was conducted from June 2022 to March 2024 by the Global Otolaryngology-Head and Neck Surgery Initiative, collecting national-level data from 93 countries representing 77 percent of the world's population. Responses were analysed by income classification and geographic region based on World Bank and World Health Organization categories. The global density of audiologists was 1.48 per 100,000 people, with more than 70 percent of countries reporting fewer than one audiologist per 100,000. The overall hearing healthcare workforce density was 4.1 per 100,000 population. Training opportunities were limited in low-income countries, where 83 percent of audiologists were trained outside their home country. The African, Eastern Mediterranean, and South-East Asian regions were especially underserved. Workforce roles and service scope varied widely across regions and income levels. These findings highlight critical disparities and emphasise the need for targeted workforce planning and sustainable training programs to improve global hearing care.

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes.