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Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside. Keywords: Artificial intelligence, clinical translation, digital innovation, guidelines

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

Introduction This study aims to share the experience of the Ministry of Health (Singapore) in deploying a centrally managed Telemedicine service to triage, and manage COVID-19 cases in the community during the COVID-19 Omicron wave. Material and methods Data from the deployed telemedicine system, Telemedicine Allocation and Reconciliation System, over a 14-week period (3 January 2022–10 April 2022) was analysed to determine the safety and efficacy of both the (i) National COVID-19 sorting logic algorithm and (ii) the use of a centralised telemedicine platform with privacy protection in a pandemic. Results There was a total of 47,754 children (aged 1–11 years old), 75,702 adolescents and adult patients (aged 12–69 years old) and 55,774 geriatric patients (aged > = 70 years old) who were directly managed by this platform. Amongst them, 64,961 were from the higher-risk special populations (pregnant, end-stage renal failure and immunocompromised and geriatric population). The total number of patients requiring escalations to public health institutions were 4212. This accounts for 1.32%, 3.00% and 2.35% of the children, teenagers and adults, and geriatric population respectively.

Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision-making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks, and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory., Comment: In press at Trends in Neurosciences