AI is employed in healthcare for various applications, including medical image analysis, disease diagnosis, personalized treatment planning, and patient monitoring. It utilizes machine learning, natural language processing, and data analytics to improve diagnostic accuracy, optimize treatment outcomes, and enhance healthcare delivery, leading to more efficient and effective patient care.
This perspective paper proposes a comprehensive, three-axis blueprint to integrate AI into water treatment at the technological, engineering, and industrial levels. By transforming rigid systems into intelligent, adaptive infrastructures, AI promises efficiency, resilience, and sustainability in water management.
Researchers at Rensselaer Polytechnic Institute and City University of Hong Kong propose a novel AI framework inspired by 3D brain-like neural structures and recursive loops. This vertically structured design could make AI systems more efficient, adaptive, and accessible while offering insights into human cognition.
Researchers at Huazhong University of Science and Technology have introduced Soft-GNN, a graph-based AI approach that dynamically evaluates and prioritizes reliable data, making artificial intelligence more robust against errors and manipulation.
Researchers at Georgia Tech developed a new framework to evaluate how well AI chatbots detect and respond to adverse psychiatric medication reactions in user conversations. The study found that while LLMs mimic human empathy, they often struggle to provide clinically accurate, actionable advice for mental health side effects.
Researchers have developed RiskPath, an open-source AI toolkit that uses explainable deep learning to predict chronic diseases years before symptoms emerge, achieving up to 99% accuracy. It identifies shifting risk factors across life stages, enabling earlier and more precise preventive care.
Nanjing University researchers unveiled a new AI training framework and benchmark that dramatically improve how AI collaborates with humans, especially when facing unexpected, real-world challenges. Their approach enables AI to communicate more effectively, adapt swiftly, and outperform traditional methods in human-AI teamwork.
Researchers from Nanjing University and Carnegie Mellon University have developed a new AI method that improves offline reinforcement learning by focusing on true cause-and-effect relationships in historical data. This breakthrough enables autonomous systems, like driverless cars and healthcare AI, to make safer and more accurate decisions without real-time interaction.
Mayo Clinic Platform_Accelerate has supported 15 global health tech startups in a 30-week program to refine and validate AI-driven solutions across clinical care and health administration. These innovations span diagnostics, mental health, clinical trials, and more, showcasing the future of precision medicine.
Researchers at UC San Diego Health have developed an AI-powered, non-invasive mapping system to accurately locate arrhythmia sources, revolutionizing treatment of ventricular arrhythmias like atrial fibrillation. This system improves ablation targeting by using advanced simulations of heart rhythms.
Ascertain has secured $10 million in Series A funding to scale its AI-powered case management platform, aiming to streamline administrative tasks and improve care delivery efficiency. With Northwell Health and Deerfield Management backing the effort, Ascertain targets major labor gaps in healthcare workflows.
Researchers at Wuhan University developed Fair Adversarial Training (FairAT), a technique that enhances the fairness and robustness of AI models by focusing on their most vulnerable data points. FairAT significantly improves both equity and security, outperforming existing state-of-the-art methods.
Researchers from NOVA IMS have developed Counterfactual SMOTE, an advanced oversampling method that improves minority class detection in imbalanced healthcare datasets. By generating boundary-focused, noise-free synthetic samples, it significantly enhances AI model accuracy, especially for rare but critical outcomes.
Researchers at Florida Atlantic University have developed a new unsupervised machine learning method to accurately label fraud in highly imbalanced financial and healthcare datasets, outperforming traditional techniques and reducing false positives. This scalable solution minimizes manual labeling, streamlining fraud detection in sectors where privacy and labeling costs are major concerns.
Researchers at the University of Maryland School of Medicine emphasize that combining artificial intelligence with traditional mathematical modeling yields the most reliable outcomes in predictive cancer medicine. They also advocate for ethical, open, and reproducible data sharing to advance precision healthcare and maintain patient privacy.
Rutgers Health researchers have developed an AI tool that transforms standard ECG data into synthetic heart motion signals similar to those from echocardiograms. This innovation enables earlier, cost-effective detection of heart dysfunction and may significantly reduce unnecessary imaging tests.
Researchers developed LR-GCN, an AI method that improves decision-making by identifying hidden patterns in incomplete data. It boosts predictive accuracy by up to 17%, making AI systems more reliable across real-world applications.
Digital strategist Giulio Toscani urges people to embrace meditation and critical thinking to counter AI’s seductive immediacy and addictive nature. His "prAIority" approach promotes deliberate human-AI synergy that enhances judgment, creativity, and autonomy.
Assist™, an AI-powered tool on Unbound Medicine’s Nursing Central platform, helps nurse educators create tailored teaching materials, saving them over 5 hours per week. Pilot programs show it boosts student engagement and enhances the quality and accuracy of educational content.
AI-generated treatment plans in a Cedars-Sinai virtual urgent care study were rated higher than physicians’ final decisions, especially for identifying antibiotic-resistant infections. However, physicians excelled at tailoring care through patient history-taking.
Researchers in Singapore have developed a pioneering imaging technique that combines multispectral optoacoustic tomography (MSOT) with AI to map basal cell carcinoma with precision. This breakthrough allows more accurate surgical planning, reducing repeat procedures and enhancing recovery.
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