Fox News senior medical analyst Dr. Marc Siegel joins'Fox News Live' to discuss the peanut allergy study and his new book, 'The Miracles Among Us.' NEW You can now listen to Fox News articles! Early peanut introduction could help to curb allergies in kids, new research suggests. Exposing children to peanuts when they are 4 to 11 months old -- instead of waiting until they are 3, as previously recommended by the American Academy of Pediatrics -- appears to be making a dent in the number of peanut allergy diagnoses, the study published in Pediatrics suggests. New onset peanut allergy dropped by 43% in kids under 3, Dr. David Hill, M.D., Ph.D., from the Division of Allergy and Immunology at the Children's Hospital of Philadelphia, told Fox News Digital.

A 30-minute treatment can help reduce allergies from pet dander, dust, and more. Breakthroughs, discoveries, and DIY tips sent every weekday. While ultraviolet (UV) light is harmful for human skin, it could be a new tool in the fight against airborne allergies. A study recently published in the journal found that UV light can disarm common indoor allergens in only 30 minutes. "We have found that we can use a passive, generally safe ultraviolet light treatment to quickly inactivate airborne allergens," study-author Tess Eidem, a microbiologist at the University of Colorado Boulder, said in a statement .

Infecting roughly 1 in 2 individuals globally, it is the leading cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To investigate whether personalized treatments would be optimal for patients suffering from infection, we developed the H. pylori AI-clinician recommendation system. This system was trained on data from tens of thousands of H. pylori-infected patients from Hp-EuReg, orders of magnitude greater than those experienced by a single real-world clinician. We first used a simulated dataset and demonstrated the ability of our AI Clinician method to identify patient subgroups that would benefit from differential optimal treatments. Next, we trained the AI Clinician on Hp-EuReg, demonstrating on average the AI Clinician reproduces known quality estimates of treatment decision making, for example bismuth and quadruple therapies out-performing triple, with longer durations and higher dose proton pump inhibitor (PPI) showing higher quality estimation on average. Next, we demonstrated that treatment was optimized by recommended personalized therapies in patient subsets, where 65% of patients were recommended a bismuth therapy of either metronidazole, tetracycline, and bismuth salts with PPI, or bismuth quadruple therapy with clarithromycin, amoxicillin, and bismuth salts with PPI, and 15% of patients recommended a quadruple non-bismuth therapy of clarithromycin, amoxicillin, and metronidazole with PPI. Finally, we determined trends in patient variables driving the personalized recommendations using random forest modelling. With around half of the world likely to experience H. pylori infection at some point in their lives, the identification of personalized optimal treatments will be crucial in both gastric cancer prevention and quality of life improvements for countless individuals worldwide.

Simulated patient systems play a crucial role in modern medical education and research, providing safe, integrative learning environments and enabling clinical decision-making simulations. Large Language Models (LLM) could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, ensuring the effectiveness and trustworthiness of these systems remains a challenge, as they require a large, diverse, and precise patient knowledgebase, along with a robust and stable knowledge diffusion to users. Here, we developed AIPatient, an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and the Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow as the generation backbone. AIPatient KG samples data from Electronic Health Records (EHRs) in the Medical Information Mart for Intensive Care (MIMIC)-III database, producing a clinically diverse and relevant cohort of 1,495 patients with high knowledgebase validity (F1 0.89). Reasoning RAG leverages six LLM powered agents spanning tasks including retrieval, KG query generation, abstraction, checker, rewrite, and summarization. This agentic framework reaches an overall accuracy of 94.15% in EHR-based medical Question Answering (QA), outperforming benchmarks that use either no agent or only partial agent integration. Our system also presents high readability (median Flesch Reading Ease 77.23; median Flesch Kincaid Grade 5.6), robustness (ANOVA F-value 0.6126, p>0.1), and stability (ANOVA F-value 0.782, p>0.1). The promising performance of the AIPatient system highlights its potential to support a wide range of applications, including medical education, model evaluation, and system integration.

Writing clinical notes and documenting medical exams is a critical task for healthcare professionals, serving as a vital component of patient care documentation. However, manually writing these notes is time-consuming and can impact the amount of time clinicians can spend on direct patient interaction and other tasks. Consequently, the development of automated clinical note generation systems has emerged as a clinically meaningful area of research within AI for health. In this paper, we present three key contributions to the field of clinical note generation using large language models (LLMs). First, we introduce CliniKnote, a comprehensive dataset consisting of 1,200 complex doctor-patient conversations paired with their full clinical notes. This dataset, created and curated by medical experts with the help of modern neural networks, provides a valuable resource for training and evaluating models in clinical note generation tasks. Second, we propose the K-SOAP (Keyword, Subjective, Objective, Assessment, and Plan) note format, which enhances traditional SOAP~\cite{podder2023soap} (Subjective, Objective, Assessment, and Plan) notes by adding a keyword section at the top, allowing for quick identification of essential information. Third, we develop an automatic pipeline to generate K-SOAP notes from doctor-patient conversations and benchmark various modern LLMs using various metrics. Our results demonstrate significant improvements in efficiency and performance compared to standard LLM finetuning methods.

A new report from the Milken Center for Public Health suggests "panic buying" of medications by patients and providers caused drug shortages. TRIPLE THREAT – Amid COVID, flu and RSV, households and hospitals stockpiled meds, says new research. MORE MPOX – CDC warns of new case clusters in Chicago. UNREALISTIC IDEALS – Here's how AI defines the "perfect body." A new study by The Bulimia Project, a Brooklyn, New York-based website that publishes content and research related to eating disorders, investigated how AI perceived the "ideal" body based on social media data.

Spring is here and ready to party. Not only does the season usher in allergies and mild temperatures, but also the urge to clean one's house. If you're looking to do a deep clean of your carpets and hardwood floors, you're in luck, as we've got a great deal for you today. The RoboVac G30 features 2000Pa of suction power to pick up sizable debris, a path tracking sensor, app control (with a comprehensive cleaning history), and more. The path tracking sensor helps the vacuum adapt to different types of floors like carpet and hardwood, resulting in a more effective clean across all surfaces.

A professor says AI chatbot software, such as ChatGPT, could restructure postsecondary education by replacing some textbooks and promoting critical thinking. As artificial intelligence has made its way into everything from content creation to health care, could "robo recipes" be next on the menu? Researchers from the Poznań University of Economics and Business in Poland entered prompts into ChatGPT -- the AI-powered large language model (LLM) developed by OpenAI -- to get meal recommendations for specific food allergies. "ChatGPT -- at least in the version that was tested in January 2023 -- generally produced balanced diet plans for people with food allergies, but not all of them were safe," Paweł Niszczota, lead researcher of the study, which was published in the journal Nutrition, told Fox News Digital. Each year, some 30,000 people visit the emergency room with food allergy reactions and 150 to 200 die from them, studies have shown.

For many people, owning a pet can be a source of joy and comfort. Pets can provide companionship, reduce stress levels, and even improve physical health. However, not everyone is able to own a real pet due to various reasons such as allergies, financial constraints, or living situations that don't allow for pets. Enter lifelike robo pets, the latest innovation in the world of artificial intelligence and robotics. These pets are designed to look and act like real animals, offering many of the same benefits as living pets without the challenges of pet ownership. What are lifelike robo pets?

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.