Fairness in multi-agent reinforcement learning (MARL) is often framed as a workload balance problem, overlooking agent expertise and the structured coordination required in real-world domains. In healthcare, equitable task allocation requires workload balance or expertise alignment to prevent burnout and overuse of highly skilled agents. Workload balance refers to distributing an approximately equal number of subtasks or equalised effort across healthcare workers, regardless of their expertise. We make two contributions to address this problem. First, we propose FairSkillMARL, a framework that defines fairness as the dual objective of workload balance and skill-task alignment. Second, we introduce MARLHospital, a customizable healthcare-inspired environment for modeling team compositions and energy-constrained scheduling impacts on fairness, as no existing simulators are well-suited for this problem. We conducted experiments to compare FairSkillMARL in conjunction with four standard MARL methods, and against two state-of-the-art fairness metrics. Our results suggest that fairness based solely on equal workload might lead to task-skill mismatches and highlight the need for more robust metrics that capture skill-task misalignment. Our work provides tools and a foundation for studying fairness in heterogeneous multi-agent systems where aligning effort with expertise is critical.

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.

England's South Central Ambulance Service (SCAS) is rolling out robotic paramedics to perform mechanical CPR, the BBC's Science Focus reports. The model, dubbed the LUCAS 3, can perform chest compressions on patients, freeing up paramedics to perform other urgent tasks. "These devices don't fatigue or change the delivery in any way, meaning high quality CPR can be delivered for as long as is required while freeing up the paramedic, keeping them seated and belted and able to focus on other critical aspects of patient care on a journey," John Black, the ambulance service's medical director, said in a statement. "It ultimately acts as a robotic third crew member for our teams." Rise of the robot paramedics: The administration of proper and consistent chest compressions can greatly improve the odds of a patient's survival, especially in cases of cardiac arrest.

As a hybrid physician/engineer, I spend a lot of time pondering how new platforms can empower doctors. I am particularly excited about the potential of smart speakers coupled with advances in A.I. and natural language processing (also looking at you, blockchain). I am bullish on conversational agents in general, previously building an iOS chatbot powered by Watson that simulates a human radiologist. Chatbots are cool and useful, but voice -- that might be magic. Sensing potential, I decided to hunker down with my trusty corgi, drink a bunch of coffee, and start building the cool voice tools I want to use in my own clinical practice.