Yale epidemiologist Harvey Risch, who has entertained a connection between Covid vaccines and "turbo cancer" and promoted ivermectin, says he'll chair the President's Cancer Panel. An epidemiologist who has speculated about whether there is a connection between Covid-19 vaccines and "turbo cancer" in young people, and works as chief epidemiologist at a company that sells ivermectin alongside reviews that claim it has efficacy as a cancer treatment, has been appointed by president Donald Trump to a key position overseeing the National Cancer Program. Harvey Risch, a professor emeritus of epidemiology at the Yale School of Public Health, announced his appointment as chair of the President's Cancer Panel on X earlier this month. Risch's profile page on the Yale website has also been updated to read "In November 2025, President Trump appointed Dr. Risch to Chair the President's Cancer panel." No formal announcement was made by the president or the White House, and the Cancer Panel website's list of current members does not include Risch.

The COVID-19 infodemic calls for scalable fact-checking solutions that handle long-form misinformation with accuracy and reliability. This study presents SAFE (system for accurate fact extraction and evaluation), an agent system that combines large language models with retrieval-augmented generation (RAG) to improve automated fact-checking of long-form COVID-19 misinformation. SAFE includes two agents - one for claim extraction and another for claim verification using LOTR-RAG, which leverages a 130,000-document COVID-19 research corpus. An enhanced variant, SAFE (LOTR-RAG + SRAG), incorporates Self-RAG to refine retrieval via query rewriting. We evaluated both systems on 50 fake news articles (2-17 pages) containing 246 annotated claims (M = 4.922, SD = 3.186), labeled as true (14.1%), partly true (14.4%), false (27.0%), partly false (2.2%), and misleading (21.0%) by public health professionals. SAFE systems significantly outperformed baseline LLMs in all metrics (p < 0.001). For consistency (0-1 scale), SAFE (LOTR-RAG) scored 0.629, exceeding both SAFE (+SRAG) (0.577) and the baseline (0.279). In subjective evaluations (0-4 Likert scale), SAFE (LOTR-RAG) also achieved the highest average ratings in usefulness (3.640), clearness (3.800), and authenticity (3.526). Adding SRAG slightly reduced overall performance, except for a minor gain in clearness. SAFE demonstrates robust improvements in long-form COVID-19 fact-checking by addressing LLM limitations in consistency and explainability. The core LOTR-RAG design proved more effective than its SRAG-augmented variant, offering a strong foundation for scalable misinformation mitigation.

Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (7%) more than the general population, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use.

The COVID-19 pandemic created a deluge of questionable and contradictory scientific claims about drug efficacy -- an "infodemic" with lasting consequences for science and society. In this work, we argue that NLP models can help domain experts distill and understand the literature in this complex, high-stakes area. Our task is to automatically identify contradictory claims about COVID-19 drug efficacy. We frame this as a natural language inference problem and offer a new NLI dataset created by domain experts. The NLI framing allows us to create curricula combining existing datasets and our own. The resulting models are useful investigative tools. We provide a case study of how these models help a domain expert summarize and assess evidence concerning remdisivir and hydroxychloroquine.

Researchers have been investigating automated solutions for fact-checking in a variety of fronts. However, current approaches often overlook the fact that the amount of information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

Desperate to solve the deadly conundrum of COVID-19, the world is clamoring for fast answers and solutions from a research system not built for haste. The ironic, and perhaps tragic, result: Scientific shortcuts have slowed understanding of the disease and delayed the ability to find out which drugs help, hurt or have no effect at all. As deaths from the coronavirus relentlessly mounted into the hundreds of thousands, tens of thousands of doctors and patients rushed to use drugs before they could be proved safe or effective. "People had an epidemic in front of them and were not prepared to wait," said Dr. Derek Angus, critical care chief at the University of Pittsburgh Medical Center. "We made traditional clinical research look so slow and cumbersome."

Desperate to solve the deadly conundrum of COVID-19, the world is clamoring for fast answers and solutions from a research system not built for haste. The ironic, and perhaps tragic, result: Scientific shortcuts have slowed understanding of the disease and delayed the ability to find out which drugs help, hurt or have no effect at all. As deaths from the coronavirus relentlessly mounted into the hundreds of thousands, tens of thousands of doctors and patients rushed to use drugs before they could be proved safe or effective. "People had an epidemic in front of them and were not prepared to wait," said Dr. Derek Angus, critical care chief at the University of Pittsburgh Medical Center. "We made traditional clinical research look so slow and cumbersome."

There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.

It'd be foolish to base any major health policy on one scientific study and it's unclear if this study played a role in the country's fiasco over testing--widely regarded as a major failure of the administration's COVID-19 response--but it's nonetheless alarming that it was repeated as fact by the very people we're trusting to lead our country through the pandemic. That said, the mixup isn't entirely Birx's fault; after all, the study was published in a journal after peer review and it wasn't marked on PubMed as withdrawn until weeks after the retraction occurred. The real problem here is that this study even had the prominence it did. As the co-founders of Retraction Watch, a blog that tracks academic retractions, wrote in a recent article for Wired, the case involving Birx "is a particularly dismaying and consequential example of what happens when no one bothers to engage in scientific fact-checking." "But," they cautioned, "it will not be the last time that something we thought we knew about the coronavirus because it was in a published paper will turn out to be wrong."