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Evidence-backed gain

ChatGPT AI 80s trend: How to make Indian cricketer-style retro photos - step by step guide

hatGPT AI 80s Trend: How to Make Indian Cricketer-Style Retro PhotosIf you've been scrolling through Instagram or other social media lately, you may have noticed those old-school cricket photos popping up everywhere.The pictures have that very 80s feel - faded colours, white cricket uniforms, wooden bats, simple stadiums and a little bit of film grain. Some even look like they've been pulled straight out of an old cricket album.And yes, you can make something similar with ChatGPT.You don't need Photoshop or any…

Times of India · Lifestyle

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Gavin Newsom imposes strict new rules on AI, social media and chatbots for children

California has adopted what Gavin Newsom described as “nation-leading” legislation that bans addictive social media features for young users, regulates companion chatbots and implements new safeguards around AI. The laws, signed by the Democratic California governor on Thursday, include AB 1709, which bars social media companies from using infinite scroll and autoplay for users under 16. “This is about actually addressing the problem, the scrolling, the algorithms. This is about addressing the substantive issue…

Policy

Evidence-backed gain

UK economy unexpectedly grows 0.4% in July boosted by AI

The UK economy grew in July as the rapid expansion of AI appeared to outweigh the economic damage from the Iran war, in a welcome boost for John Healey. Figures from the Office for National Statistics (ONS) showed a surprise 0.4% increase in gross domestic product (GDP), compared with 0.3% growth in June. City economists had forecast zero growth. The figures suggested the economy continued to be robust despite the fallout from the Iran war, which has raised energy costs and led to higher interest rates than were…

Business

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The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review

In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have become integral components of our daily lives, profoundly intertwined with our daily activities. These technologies have undoubtedly brought convenience, connectivity, and speed, making our lives easier and more efficient. However, their influence on our brain function and cognitive abilities cannot be ignored. This review aims to explore both the positive and negative impacts of these technologies on cruc…

Health

Evidence-backed gain

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across va…

Climate

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Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation

Objectives This study aim to develop, compare and internally validate machine-learning models for predicting urine-culture positivity in patients who had both urinalysis and culture ordered and to explore descriptive probability strata. Post hoc secondary analyses examined age subgroups, the incremental contribution of text-derived features, simpler comparators and calibration. Patients and methods Urine culture results are typically unavailable for 24-72 h, creating uncertainty during initial assessment, and ma…

Health

Evidence-backed gain

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized therapeutic guidance. This research work introduces FedMediFormer-XAI, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks (GNNs) for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence. The system utilizes diverse healthcar…

Health

Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstruction, serving as a link between skeletal morphology and facial appearance. However, most existing studies rely on mean CFSTT values without considering the contribution of subcutaneous fat layer thickness, limiting the accuracy of reconstruction models. Objective This study aims to develop an artificial intelligence (AI)-assisted, CT-based framework for the comparative evaluation of craniofacial soft tissue…

Science

Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative precision through robotics, navigation, and implant design, while preoperative decision-making remains comparatively underdeveloped. Accurate estimation of patient-specific risk is central to surgical indications, yet existing tools provide limited resolution for consequential outcomes such as 1-year mortality. Methods A machine learning model was developed using the TriNetX Research Network. Patients unde…

Health

Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis

Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical procedures and antibiotic therapy, and conventional scoring tools show poor generalizability. Machine learning (ML) addresses these limitations by capturing complex, nonlinear clinical relationships, outperforming conventional scores in predictive accuracy, though prior ML work in IE has focused on diagnosis. A PRISMA-compliant systematic review and meta-analysis of PubMed (Supplemental Digital Content,…

Health

Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis

Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of disease-related mortality in systemic sclerosis and the connective tissue disease-associated ILD (CTD-ILD) in which artificial intelligence imaging has advanced most rapidly. Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making. This review summarizes clinically relevant artificial intelligence and radiomics publications from approximately the last 18 months, foc…

Health

Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases

Artificial Intelligence (AI) chatbots are becoming an efficient option to understand medical data and also help with clinical reasoning. There has been a recent progression in research of large language models and their ability to be used in the healthcare sector, such as radiological image analysis, and diagnostic support. There is however, little evidence supporting their ability to accurately understand abnormal anatomical conditions, such as congenital anomalies and tumor related changes in anatomy. To asses…

Health

Stakeholder perspectives on artificial intelligence in schizophrenia care

Background Artificial intelligence (AI) is explored as a tool to expand access to mental health care, offering support and self-disclosure. To date, the perspectives of individuals with schizophrenia on AI have been absent from the medical literature. This exploratory study represents an effort to report schizophrenia patient perspectives on AI chatbots through a focus group. Methods We conducted an exploratory, cross-sectional, qualitative study using semi-structured focus groups at a large academic health cent…

Health

Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review

Background Artificial intelligence (AI) and complementary digital health technologies are increasingly being applied to improve prevention, diagnosis and surveillance in travel medicine. This narrative review evaluates current applications of these technologies across the pre-travel, peri-travel and post-travel phases of the travel continuum. Methods A narrative literature review was conducted using a clinically oriented three-phase framework encompassing pre-travel preparation, peri-travel monitoring and post-t…

Health

Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study

Background Medical history taking (MHT) is a foundational clinical competency for medical students; however, traditional training models using standardized patients face challenges such as resource constraints. Large language model-powered virtual standardized patients (LLM-VSPs) offer a safe, repeatable platform for self-directed practice with AI-automated feedback. Nevertheless, their effectiveness in authentic teaching environments and underlying learning mechanisms require further investigation. Objective Th…

Health