Fair-GNE : Generalized Nash Equilibrium-Seeking Fairness in Multiagent Healthcare Automation

January Ventures is focusing on funding underrepresented AI founders who possess deep expertise in traditional industries like healthcare, manufacturing, and supply chain. The firm aims to address the funding gap that exists in the AI startup ecosystem, particularly in San Francisco, where many promising companies are overlooked. By providing pre-seed checks, January Ventures seeks to empower these founders to innovate and transform their respective sectors.

The article discusses fairness in multi-agent reinforcement learning (MARL) within healthcare, emphasizing the need for equitable task allocation that considers both workload balance and agent expertise. It introduces FairSkillMARL, a framework that aims to align skill and task distribution to prevent burnout among healthcare workers. Additionally, MARLHospital is presented as a customizable environment for modeling team dynamics and scheduling impacts on fairness, addressing gaps in existing simulators.

This study explores a class of distributionally robust games where agents can choose their risk aversion levels in response to distributional shifts in uncertainty. By applying heterogeneous Wasserstein ball constraints through a Lagrangian formulation, the research formulates the distributionally robust Nash equilibrium problem. Under specific assumptions, this problem is shown to be equivalent to a finite-dimensional variational inequality problem. An approximate Nash equilibrium seeking algorithm is designed, demonstrating convergence of average regret through numerical simulations.

Large language models (LLMs) are known for their impressive text generation capabilities; however, they frequently produce factually incorrect content, a phenomenon referred to as hallucination. This issue is particularly concerning in critical fields such as healthcare and finance. Traditional methods for detecting these inaccuracies often require multiple API calls, leading to increased latency and costs. The introduction of CONFACTCHECK offers a new approach that checks for consistency in responses to factual queries, enhancing the reliability of LLM outputs without needing external knowled…

A new framework for summarizing consumer health questions (CHQs) has been proposed, aiming to improve communication in healthcare. This framework integrates TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs). Experiments with the LLaMA-2-7B model on the MeQSum and BanglaCHQ-Summ datasets showed significant improvements in quality and faithfulness metrics, with over 80% of summaries preserving critical medical information. This highlights the importance of faithfulness in medical summarization.

The article discusses a novel bi-level contextual bandit framework aimed at individualized resource allocation in high-stakes domains such as education, employment, and healthcare. This framework addresses the challenges of delayed feedback, hidden heterogeneity, and ethical constraints, which are often overlooked in traditional learning-based allocation methods. The proposed model optimizes budget allocations at the subgroup level while identifying responsive individuals using a neural network trained on observational data.