Melania Trump urges parents to prepare their children for the growth of A.I. and argues the technology should be treated as if it were a child itself. First lady Melania Trump attends a meeting of the White House Task Force on Artificial Intelligence (AI) Education in the East Room at the White House in Washington, D.C., Sept. 4, 2025. FRONT AND CENTER: First lady Melania Trump hosted an artificial intelligence meeting with top industry leaders, including Google CEO Sundar Pichai Thursday, as she stressed the importance of managing AI's growth "responsibly." WORLD-CHANGING: If you were investing in the late 1990s, you'll remember the euphoria of the dot-com boom. Anything with a ".com" at the end of its name could raise millions in capital and see its stock price double or triple overnight.

Board-certified cardiothoracic surgeon Dr. Jeremy London, based in Savannah, Georgia, explains why VO2 max and muscle mass are the main indicators of longevity. The first artificial intelligence (AI) stethoscope has gone beyond listening to a heartbeat. Researchers at Imperial College London and Imperial College Healthcare NHS Trust discovered that an AI stethoscope can detect heart failure at an early stage. The TRICORDER study results, published in BMJ Journals, found that the AI-enabled stethoscope can help doctors identify three heart conditions in just 15 seconds. According to the British Heart Foundation (BHF), which partially funded the study, the researchers analyzed data from more than 1.5 million patients, focusing on people with heart failure symptoms like breathlessness, swelling and fatigue.

Doctors have successfully developed an artificial intelligence-led stethoscope that can detect three heart conditions in 15 seconds. Invented in 1816, the traditional stethoscope – used to listen to sounds within the body – has been a vital part of every medic's toolkit for more than two centuries. Now a team have designed a hi-tech upgrade with AI capabilities that can diagnose heart failure, heart valve disease and abnormal heart rhythms almost instantly. The new stethoscope developed by researchers at Imperial College London and Imperial College healthcare NHS trust can analyse tiny differences in heartbeat and blood flow undetectable to the human ear, and take a rapid ECG at the same time. Details of the breakthrough, which could boost early diagnosis of the three conditions, were presented to thousands of doctors at the European Society of Cardiology annual congress in Madrid, the world's largest heart conference.

No one doubts that our future will feature more automation than our past or present. The question is how we get from here to there, and how we do so in a way that is good for humanity. Sometimes it seems the most direct route is to automate wherever possible, and to keep iterating until we get it right. Here's why that would be a mistake: imperfect automation is not a first step toward perfect automation, anymore than jumping halfway across a canyon is a first step toward jumping the full distance. Recognizing that the rim is out of reach, we may find better alternatives to leaping--for example, building a bridge, hiking the trail, or driving around the perimeter. This is exactly where we are with artificial intelligence. AI is not yet ready to jump the canyon, and it probably won't be in a meaningful sense for most of the next decade. Rather than asking AI to hurl itself over the abyss while hoping for the best, we should instead use AI's extraordinary and improving capabilities to build bridges.

Auscultation is crucial for diagnosing lung diseases. The COVID-19 pandemic has revealed the limitations of traditional, in-person lung sound assessments. To overcome these issues, advancements in digital stethoscopes and artificial intelligence (AI) have led to the development of new diagnostic methods. In this context, our study aims to use smartphone microphones to record and analyze lung sounds. We faced two major challenges: the difference in audio style between electronic stethoscopes and smartphone microphones, and the variability among patients. To address these challenges, we developed a method called Patient Domain Supervised Contrastive Learning (PD-SCL). By integrating this method with the Audio Spectrogram Transformer (AST) model, we significantly improved its performance by 2.4\% compared to the original AST model. This progress demonstrates that smartphones can effectively diagnose lung sounds, addressing inconsistencies in patient data and showing potential for broad use beyond traditional clinical settings. Our research contributes to making lung disease detection more accessible in the post-COVID-19 world.

Despite the impressive performance of large language models (LLMs), the process of endowing them with new capabilities--such as mathematical reasoning--remains largely empirical and opaque. A critical open question is whether reasoning abilities stem from the entire model, specific modules, or are merely artifacts of overfitting. In this work, we hypothesize that the reasoning capabilities in well-trained LLMs are primarily attributed to the output projection module (oproj) in the Transformer's multi-head self-attention (MHSA) mechanism. To support this hypothesis, we introduce Stethoscope for Networks (SfN), a suite of diagnostic tools designed to probe and analyze the internal behaviors of LLMs. Using SfN, we provide both circumstantial and empirical evidence suggesting that oproj plays a central role in enabling reasoning, whereas other modules contribute more to fluent dialogue. These findings offer a new perspective on LLM interpretability and open avenues for more targeted training strategies, potentially enabling more efficient and specialized LLMs.

Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood. This study presents a deep learning model designed to detect CHD using phonocardiogram (PCG) signals, with a focus on its application in global health. We evaluated our model on several datasets, including the primary dataset from Bangladesh, achieving a high accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%. The model also demonstrated robust performance on the public PhysioNet Challenge 2022 and 2016 datasets, underscoring its generalizability to diverse populations and data sources. We assessed the performance of the algorithm for single and multiple auscultation sites on the chest, demonstrating that the model maintains over 85% accuracy even when using a single location. Furthermore, our algorithm was able to achieve an accuracy of 80% on low-quality recordings, which cardiologists deemed non-diagnostic. This research suggests that an AI- driven digital stethoscope could serve as a cost-effective screening tool for CHD in resource-limited settings, enhancing clinical decision support and ultimately improving patient outcomes.

The increasing demand for healthcare workers, driven by aging populations and labor shortages, presents a significant challenge for hospitals. Humanoid robots have the potential to alleviate these pressures by leveraging their human-like dexterity and adaptability to assist in medical procedures. This work conducted an exploratory study on the feasibility of humanoid robots performing direct clinical tasks through teleoperation. A bimanual teleoperation system was developed for the Unitree G1 Humanoid Robot, integrating high-fidelity pose tracking, custom grasping configurations, and an impedance controller to safely and precisely manipulate medical tools. The system is evaluated in seven diverse medical procedures, including physical examinations, emergency interventions, and precision needle tasks. Our results demonstrate that humanoid robots can successfully replicate critical aspects of human medical assessments and interventions, with promising quantitative performance in ventilation and ultrasound-guided tasks. However, challenges remain, including limitations in force output for procedures requiring high strength and sensor sensitivity issues affecting clinical accuracy. This study highlights the potential and current limitations of humanoid robots in hospital settings and lays the groundwork for future research on robotic healthcare integration.

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitating intermittent abnormal sound capture.Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $\%$, Se: 44.47$\%$, and Score: 63.26$\%$ with a network model size of 38M, comparing to the current model, our method leads by nearly 7$\%$, achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system.

Given the global prevalence of cardiovascular diseases, there is a The cardiovascular disease screening process detects abnormalities pressing need for easily accessible early screening methods. Typically, such as heart murmur, which is an irregular sound audible during this requires medical practitioners to investigate heart auscultations the heartbeat cycle through a stethoscope. Detection of a heart for irregular sounds, followed by echocardiography and electrocardiography murmur suggests underlying cardiac issues, prompting further evaluation tests. To democratize early diagnosis, we present a through echocardiography and electrocardiography tests user-friendly solution for abnormal heart sound detection, utilizing to pinpoint the specific heart disease. To enhance the accessibility mobile phones and a lightweight neural network optimized for of early diagnosis, we introduce a novel system for detecting abnormal on-device inference. Unlike previous approaches reliant on specialized heart sounds using mobile phones and an on-device neural stethoscopes, our method directly analyzes audio recordings, network. Our system does not require extra equipment, a server, facilitated by a novel architecture known as IConNet. IConNet, an or a specific data preprocessing pipeline, which is an advantage Interpretable Convolutional Neural Network, harnesses insights compared to existing works.