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.

This year's WIRED Health summit in Boston featured Moderna CEO Stéphane Bancel, CNN chief medical correspondent Sanjay Gupta, and a day's worth of insights and provocative conversations. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. At the WIRED Health summit in Boston on September 9, we hosted some of the leading experts in CRISPR, whole-genome sequencing, vaccines, and more for a series of eye-opening conversations and keynotes. If you weren't able to join us in person, no worries; you can watch them all right here.

Over the past few years, the field of artificial intelligence (AI) has shown great promise in various domains, including medicine. A potential use case for AI in medicine is its application in managing advanced-stage cancer treatment, where limited evidence often makes treatment choices reliant on the personal expertise of the physicians. The complex nature of oncological disease processes and the multitude of factors that need to be considered when making treatment decisions make it difficult to rely solely on evidence-based trial data, which is often limited and may exclude certain patient populations. This results in physicians making decisions on a case-by-case basis, drawing on their experience of previous cases, which is not always objective and may be limited by the small number of cases they have observed. In this context, the use of clinical decision support systems (CDSS) using similaritybased AI approaches can potentially contribute to better oncology treatment by supporting physicians in the selection of treatment methods [1, 2]. One approach is Case-Based Reasoning (CBR), a subfield of AI that deals with experience-based problem solving.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Personalized cancer treatment is revolutionizing oncology by leveraging precision medicine and advanced computational techniques to tailor therapies to individual patients. Despite its transformative potential, challenges such as limited generalizability, interpretability, and reproducibility of predictive models hinder its integration into clinical practice. Current methodologies often rely on black-box machine learning models, which, while accurate, lack the transparency needed for clinician trust and real-world application. This paper proposes the development of an innovative framework that bridges Kolmogorov-Arnold Networks (KANs) and Evolutionary Game Theory (EGT) to address these limitations. Inspired by the Kolmogorov-Arnold representation theorem, KANs offer interpretable, edge-based neural architectures capable of modeling complex biological systems with unprecedented adaptability. Their integration into the EGT framework enables dynamic modeling of cancer progression and treatment responses. By combining KAN's computational precision with EGT's mechanistic insights, this hybrid approach promises to enhance predictive accuracy, scalability, and clinical usability.

At the beginning of 2024, the American Cancer Society predicted that 2,001,140 new cancer cases and 611,720 cancer deaths would occur in the United States. Now, as the year draws to a close, experts are looking back and reflecting on the discoveries and advances that have been made in the field of cancer treatment and prevention. Fox News Digital spoke with four oncologists from the Sarah Cannon Research Institute in Nashville, Tennessee, about the most notable accomplishments of 2024 and what they see on the horizon for 2025. See the answers and questions below. Krish Patel, M.D., is director of lymphoma research at Sarah Cannon Research Institute in Nashville, Tennessee.

Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with patient portal messages to generate research ideas that prioritize important patient issues. We further quantified the quality of AI-generated research topics. To define patient clinical concerns, we analyzed 614,464 patient messages from 25,549 individuals with breast or skin cancer obtained from a large academic hospital (2013 to 2024), constructing a 2-staged unsupervised NLP topic model. Then, we generated research topics to resolve the defined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024 version) with prompt-engineering strategies. We guided AI to perform multi-level tasks: 1) knowledge interpretation and summarization (e.g., interpreting and summarizing the NLP-defined topics), 2) knowledge generation (e.g., generating research ideas corresponding to patients issues), 3) self-reflection and correction (e.g., ensuring and revising the research ideas after searching for scientific articles), and 4) self-reassurance (e.g., confirming and finalizing the research ideas). Six highly experienced breast oncologists and dermatologists assessed the significance and novelty of AI-generated research topics using a 5-point Likert scale (1-exceptional, 5-poor). One-third of the AI-suggested research topics were highly significant and novel when both scores were lower than the average. Two-thirds of the AI-suggested topics were novel in both cancers. Our findings demonstrate that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.

The control system in surgical robots must ensure patient safety and real time control. As such, all the uncertainties which could appear should be considered into an extended model of the plant. After such an uncertain plant is formed, an adequate controller which ensures a minimum set of performances for each situation should be computed. As such, the continuous-time robust control paradigm is suitable for such scenarios. However, the problem is generally solved only for linear and time invariant plants. The main focus of the current paper is to include m-link serial surgical robots into Robust Control Framework by considering all nonlinearities as uncertainties.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. A chemotherapy alternative called immunotherapy is showing promise in treating cancer -- and a new artificial intelligence tool could help ensure that patients have the best possible experience. Immunotherapy, first approved in 2011, uses the cancer patient's own immune system to target and fight cancer. While it doesn't work for everyone, for the 15% to 20% who do see results, it can be life-saving.

The future of cancer research is in good hands. Six high-school students in the U.S. are dedicated to making progress toward improving the diagnostics and treatment of the disease. The students were finalists in this year's Regeneron Science Talent Search, which is the country's oldest and most prestigious science and mathematics competition hosted by the Society for Science in Washington, D.C. "We are thrilled to honor these bright minds dedicated to making strides in cancer research," said Maya Ajmera, president and CEO of the Society for Science, a partner with Regeneron in the Science Talent Search. "These high-school students are not only advancing our understanding of the way cancer presents in the human body, but are paving the way for potential future therapies and helping unlock new possibilities in the fight against this formidable disease." Four of the six student finalists who specialized in cancer research are shown here.