As AI chatbots gain adoption in clinical medicine, developing effective frameworks for complex, emerging diseases presents significant challenges. We developed and evaluated six Retrieval-Augmented Generation (RAG) corpus configurations for Long COVID (LC) clinical question answering, ranging from expert-curated sources to large-scale literature databases. Our evaluation employed an LLM-as-a-judge framework across faithfulness, relevance, and comprehensiveness metrics using LongCOVID-CQ, a novel dataset of expert-generated clinical questions. Our RAG corpus configuration combining clinical guidelines with high-quality systematic reviews consistently outperformed both narrow single-guideline approaches and large-scale literature databases. Our findings suggest that for emerging diseases, retrieval grounded in curated secondary reviews provides an optimal balance between narrow consensus documents and unfiltered primary literature, supporting clinical decision-making while avoiding information overload and oversimplified guidance. We propose Guide-RAG, a chatbot system and accompanying evaluation framework that integrates both curated expert knowledge and comprehensive literature databases to effectively answer LC clinical questions.

How healthy am I? My immunome knows the score. Groundbreaking new tests reveal patterns in our immune systems that can signal underlying disease and tell us how well we might recover from our next cold. I got my results in a text message. It's not often you get a text about the robustness of your immune system, but that's what popped up on my phone last spring. Sent by John Tsang, an immunologist at Yale, the text came after his lab had put my blood through a mind-boggling array of newfangled tests. The result--think of it as a full-body, high-resolution CT scan of my immune system--would reveal more about the state of my health than any test I had ever taken. And it could potentially tell me far more than I wanted to know. "David," the text read, "you are the red dot." Tsang was referring to an image he had attached to the text that showed a graph with a scattering of black dots representing other people whose immune systems had been evaluated--and a lone red one.

Every video game is a miracle. Long hours, extraordinary technical and artistic requirements and cross-disciplinary collaboration: the very act of making games is difficult, and leaves room for catastrophic errors. It's a wonder any of them make it to release at all. Fulcrum Defender, the new Playdate exclusive from Jay Ma, the co-founder of indie darling Subset Games, is one such miraculous game. Ma began work on Fulcrum Defender following a life-changing Covid infection that has greatly diminished her quality of life and ability to do the thing she loves.

Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy Advocacy or Public Health Communication, and Online Communities and Social Support. This methodology achieved an average confidence of 0.7788, with the minimum and maximum confidence being 0.1566 and 0.9928, respectively. This model showcases the ability of advanced language models to categorize research papers without any training data or predefined classification labels, thus enabling a more rapid and scalable assessment of existing literature. This paper also highlights the multifaceted nature of Long COVID research by demonstrating how advanced computational techniques applied to social media conversations can reveal deeper insights into the experiences, symptoms, and narratives of individuals affected by Long COVID.

The COVID-19 pandemic has led to the emergence of Long COVID, a cluster of symptoms that persist after infection. Long COVID patients may also experience mental health challenges, making it essential to understand individuals' emotional and mental well-being. This study aims to gain a deeper understanding of Long COVID individuals' emotional and mental well-being, identify the topics that most concern them, and explore potential correlations between their emotions and social media activity. Specifically, we classify tweets into four categories based on the content, detect the presence of six basic emotions, and extract prevalent topics. Our analyses reveal that negative emotions dominated throughout the study period, with two peaks during critical periods, such as the outbreak of new COVID variants. The findings of this study have implications for policy and measures for addressing the mental health challenges of individuals with Long COVID and provide a foundation for future work.

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype presented in long COVID patients, it is difficult to predict their outcomes from their longitudinal data. In this study, we proposed a spatiotemporal attention mechanism to weigh feature importance jointly from the temporal dimension and feature space. Considering that medical examinations can have interchangeable orders in adjacent time points, we restricted the learning of short-term dependency with a Local-LSTM and the learning of long-term dependency with the joint spatiotemporal attention. We also compared the proposed method with several state-of-the-art methods and a method in clinical practice. The methods are evaluated on a hard-to-acquire clinical dataset of patients with long COVID. Experimental results show the Local-LSTM with joint spatiotemporal attention outperformed related methods in outcome prediction. The proposed method provides a clinical tool for the severity assessment of long COVID.

With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do not apply to catching subtle differences in factually consistent claims, which might still bias the readers, especially on contentious political or economic issues. Our underlying assumption is that among the trusted sources, one's argument is not necessarily more true than the other, requiring comparison rather than verification. In this study, we propose ClaimDiff, a novel dataset that primarily focuses on comparing the nuance between claim pairs. In ClaimDiff, we provide 2,941 annotated claim pairs from 268 news articles. We observe that while humans are capable of detecting the nuances between claims, strong baselines struggle to detect them, showing over a 19% absolute gap with the humans. We hope this initial study could help readers to gain an unbiased grasp of contentious issues through machine-aided comparison.

In the journal Nature today, my colleagues and I published an article on the future directions of generative A.I. (aka Large Language or Foundation models) for the practice of medicine. These new AI models have generated a multitude of new and exciting opportunities in healthcare that we didn't have before, along with many challenges and liabilities. I'll briefly explain how we got here and what's in store. Back in 2017, Google researchers published a paper ("Attention Is All You Need") describing a new model architecture, which they dubbed Transformer, that could give different levels of attention for multiple modes of input, and go faster, to ultimately replace recurrent and convolutional deep neural networks (RNN and CNN, respectively). Foreshadowing the future to Generative AI, they concluded: "We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video."

At the National Heart Lung and Blood Institute (NHLBI), CIO Alastair Thomson says machine learning (ML) played a major role in COVID-related research. The National COVID Cohort Collaborative (NC3) clearly demonstrates the value of securely bringing together data in one place, where it can be analyzed by thousands of researchers. Through collaboration with various healthcare and cloud service providers, the NC3 and ML helped NIH identify potential participants for the RECOVER initiative. "The utility of this really became clear when NIH launched the RECOVER initiative, which is dealing with post-acute COVID syndrome, or long COVID," Thomson said at the annual AFCEA's Health IT Summit last week. "They were able to use machine learning with that data to identify the key characteristics, what we call a phenotype, for long COVID."

A machine learning algorithm identifies four reproducible clinical subphenotypes of long COVID from the electronic health records of patients with post-acute sequelae of SARS-CoV-2 infection within 30–180 days of infection; these patterns have implications for the treatment and management of long COVID.