Understanding the patterns and occurrence of influenza is crucial for effective public health responses. National sentinel surveillance systems provide invaluable data that can be used to track the prevalence, seasonality and evolution of influenza viruses, and to inform timely interventions and early warning systems for disease control. This study aimed to describe the trends in influenza-like illness (ILI) and influenza incidence based on data obtained from ILI surveillance conducted among patients presenting to health facilities with symptoms indicative of influenza in Cambodia during 2006–2023. A total of 27 098 samples were tested, of which 15.5% were positive for influenza. Of these, 63.6% were influenza A, 36.2% were influenza B, and 0.2% were both influenza A and B. The annual incidence of influenza among the outpatient population varied, averaging 8.2 cases (range: 0.05–23.8) per 1000 population. Incidence rates were highest in 2007, 2008 and early 2023. The overall positivity rate was 16%. Not all ILI surveillance sites were consistently active during the study period, primarily due to funding constraints. Given the constraints of sentinel site coverage and data collection inconsistency, the study emphasizes the vital need for ongoing and improved ILI surveillance to accurately assess influenza incidence. To gain a more detailed picture of influenza dynamics in Cambodia, surveillance systems should be strengthened and data collection should be expanded to include asymptomatic patients and other important patient characteristics. These results are needed to guide regional and national influenza preparedness, prevention and control initiatives.

Problem: Emergency medical teams (EMTs) responding to mass casualty incidents attend to casualties in a chaotic, high-pressure and resource-limited environment that is vastly different from their day-to-day work. The nature of mass casualty incidents and the work environment can impact psychological resilience, but the psychological resilience of members of EMTs has not been evaluated. Context: In Singapore, EMTs are deployed from public hospitals, polyclinics and the Singapore Red Cross to disaster sites, where they triage, stabilize and treat casualties before evacuating them to public hospitals for further management. Action: Twenty-four members of EMTs responded to a cross-sectional survey based on a psychological resilience tool developed for health-care rescuers involved in mass casualty incidents to evaluate their psychological resilience after a full-scale exercise involving an aviation accident. Respondents completed a psychological resilience tool that was developed by experts in disaster work and research using a modified Delphi approach. There were 27 items across eight domains: optimism, altruism, preparations for disaster rescue, social support, perceived control, self-efficacy, coping strategies and positive growth. Outcome: The key observations from the survey were that (i) staff demonstrated a strong sense of altruism and had good social support; (ii) staff were not confident about their preparedness, and this led to a lack of optimism, perceived control and ability to deal with emotions; and (iii) it was necessary for respondents to reflect on their experience to find meaning to support growth after the deployment. Discussion: Optimizing casualty survival and outcomes during mass casualty incidents requires not only excellent procedural training and robust standard operating procedures and work processes but also dedicated efforts to enhance the psychological resilience of members of EMTs.

INTRODUCTION: Ultrasonography (US) is the current standard of care for imaging surveillance in patients at risk of hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and dual-sequence non-contrast-enhanced MRI (NCEMRI) for HCC surveillance in the local setting. METHODS: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed up for 40 years to estimate the patients' disease status, direct medical costs and effectiveness. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio were calculated. RESULTS: Exactly 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios - no surveillance, US surveillance and NCEMRI surveillance - were SGD 1,193/7.460 QALYs, SGD 8,099/11.195 QALYs and SGD 9,720/11.366 QALYs, respectively. CONCLUSION Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, contrast-enhanced MRI) based on patients' risk profiles.
Humans ; Adult ; Carcinoma, Hepatocellular/diagnostic imaging* ; Liver Neoplasms/diagnostic imaging* ; Cost-Effectiveness Analysis ; Cost-Benefit Analysis ; Quality-Adjusted Life Years ; Magnetic Resonance Imaging/methods*

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care—respect for patient autonomy, beneficence, non-maleficence, and justice.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.
Humans ; Artificial Intelligence ; Machine Learning ; Algorithms ; Neural Networks, Computer ; Medicine