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Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearab…

European Journal of Medical Research · Health

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Evidence-backed problem

The Erosion of Cybersecurity Zero-Trust Principles Through Generative AI: A Survey on the Challenges and Future Directions

Generative artificial intelligence (AI) and persistent empirical gaps are reshaping the cyber threat landscape faster than Zero-Trust Architecture (ZTA) research can respond. We reviewed 10 recent ZTA surveys and 136 primary studies (2022–2024) and found that 98% provided only partial or no real-world validation, leaving several core controls largely untested. Our critique, therefore, proceeds on two axes: first, mainstream ZTA research is empirically under-powered and operationally unproven; second, generative-…

Crime

Evidence-backed problem

The sociocultural roots of artificial conversations: The taste, class and habitus of generative AI chatbots

Research on AI has extensively considered biases related to gender and race. However, much less attention has been dedicated to another sociological tenet: that of class. Inspired by Bourdieu’s work on cultural stratification and distinction, this work sheds light on the sociocultural roots of artificial sociality, and on how these become manifest as ‘habitus’ within the outputs of generative AI models. We conducted 39 interviews with three AI chatbots – ChatGPT, Gemini and Replika – after asking them to imperso…

Lifestyle

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Machine Learning for Quality Control in the Food Industry: A Review

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging-Sensors and Predictive QC; Supply Chain-Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Model…

Lifestyle

Both readings

Review of machine learning approaches for predicting mechanical behavior of composite materials

In recent years, machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of composite materials, offering a faster, more cost-effective alternative to traditional testing and simulation methods. This review explores how various ML techniques, including random forests, support vector machines, artificial neural networks, and deep learning models, are used to forecast key material properties such as tensile strength, hardness, fracture toughness, and fatigue life. From a broad s…

Science

Evidence-backed gain

Artificial intelligence in nursing practice: a qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications

BACKGROUND: The study aims to explore nurses' views on the effects of artificial intelligence (AI) in nursing, focusing on their understanding, practical applications, ethical considerations, and perceived opportunities and threats. METHODS: This qualitative study used semi[Formula: see text]structured interviews to gain comprehensive insights from clinical nurses, adhering to the Standards for Reporting Qualitative Research for methodological rigor. After obtaining ethical approval, researchers conducted semi[F…

Health

Evidence-backed gain

Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

Importance: While in short supply and high demand, ambulatory care clinicians spend more time on administrative tasks and documentation in the electronic health record than on direct patient care, which has been associated with burnout, intention to leave, and reduced quality of care. Objective: To examine whether ambient AI scribes are associated with reducing clinician administrative burden and burnout. Design, Setting, and Participants: This quality improvement study used preintervention and 30-day postinterv…

Health

An Introduction to Machine Learning Methods for Fraud Detection

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hy…

Crime

SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach

Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese university players), we constructed a comprehensive 18-feature evaluation system across four dimensions: basic information, training factors, physical fitness, and lifestyle habits. We systematically compared 10 machine learning algorithms. The Support Vector Machine…

Sports

SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach

Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping

Artificial intelligence (AI) has moved from being a specialized technological tool to an intimate presence in everyday life. Smart assistants organize our schedules, predictive systems anticipate our needs, and therapeutic chatbots promise to listen when no human is available (Zhang & Wang, 2024). The diffusion of AI into mental health care is often framed in highly optimistic terms: technologies that reduce stigma, democratize access, and provide affordable, always-on support (M & N, 2025;Sivasubramanian Balasu…

Health

Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m3 and additional annual carbon emissions from 24 to 44 Mt CO2-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as indus…

Climate

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions

Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81-0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particu…

Health

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions

An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

BACKGROUND/OBJECTIVES: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. METHODS: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: "cardiovascular disease spectrum", "multi-omics", "precision cardiology", "machine learning", and "artificial intel…

Health

An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

GDPVAL: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

We introduce GDPval, a benchmark evaluating AI model capabilities on realworld economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Activities for 44 occupations across the top 9 sectors contributing to U.S. GDP (Gross Domestic Product). Tasks are constructed from the representative work of industry professionals with an average of 14 years of experience. We find that frontier model performance on GDPval is improving roughly linearly over time, and that the current best…

Labor

The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density

Labor market forecasting relies heavily on economic time-series data, often overlooking the “health–wealth” gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state lev…

Labor