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Evidence-backed gain

Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare

Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, a…

Nanomaterials · Health

Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare

Evidence-backed gain

Current AI technologies in cancer diagnostics and treatment

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated im…

Health

Evidence-backed gain

Large Language Models in Healthcare and Medical Applications: A Review

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs' promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for resp…

Health

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Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges

Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the t…

Policy

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Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when op…

Policy

Both readings

A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing di…

Health

Both readings

A Review of Water Quality Forecasting and Classification Using Machine Learning Models and Statistical Analysis

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy an…

Climate

Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions

Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers ( e.g ., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the i…

Lifestyle

Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions

AI and employee wellbeing in the workplace: An empirical study

The integration of artificial intelligence (AI) in workplace settings significantly affects employees, particularly their wellbeing. Despite its growing relevance, the impact of AI on employee wellbeing remains underexplored. To address this gap, we conducted a survey and employed structural equation modeling to analyze data from Finnish and international companies headquartered in Finland (n = 207). We found that AI adoption does not directly impact employee wellbeing but indirectly influences it through work-r…

Labor

AI and employee wellbeing in the workplace: An empirical study

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further ad…

Crime

The Impact of Artificial Intelligence on Public Sector Decision- Making: Benefits, Challenges, and Policy Implications

Artificial intelligence (AI) is increasingly transforming government decision-making processes. This article presents a systematic literature review of 43 studies (2020-2025) examining AI’s impact on public-sector decision-making, delineating its advantages, disadvantages, policy implications, technical aspects, and ethical concerns. The findings indicate that AI technologies offer significant benefits for government decision-making, including improved efficiency, data-driven insights, and enhanced service deliv…

Policy

Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention

This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature…

Sports

The AI Art Paradigm: Disruptions in the Digital Art Ecosystem and Future Trends

Art has been integral to human life and evolution since the dawn of time, merging creativity with technological advances. In today's digital art domain, generative AI models such as text-to-image generators create high-quality art in seconds, challenging the current digital art ecosystem. Human artists fear displacement. Consumers and galleries criticize AI art. Policies and legal laws continue to address AI art's ethical, social, and economic implications. Anti-AI groups see AI art as a threat to anthropocentri…

Media & Arts

The AI Art Paradigm: Disruptions in the Digital Art Ecosystem and Future Trends

AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond

Abstract The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise an…

Health

A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of param…

Science