All stories

877 published stories · page 37 of 59

Both readings

Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges

BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review synthesizes curre…

International Journal of Medical Informatics · Health

Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges

Both readings

The simple macroeconomics of AI

SUMMARY This paper evaluates claims about the large macroeconomic implications of new advances in Artificial intelligence (AI). It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: Gross Domestic Product (GDP) and aggregate productivity gains can be estimated by what fractio…

Business

Evidence-backed gain

Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)

Abstract This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduc…

Climate

Evidence-backed problem

The AI-IARA framework: How to cultivate human agency before artificial intelligence optimizes it a(ny)way

We are the last generation that will influence what wellbeing means before AI systems optimize that definition for us. While AI promises unprecedented scalability in mental health support, emerging evidence reveals a troubling pattern: the systems designed to enhance human flourishing may systematically erode the capacities required to achieve. This paper introduces the AI-IARA framework, identifying six irreducible human capacities essential for wellbeing under algorithmic conditions: Awareness (the ability to…

Health

Evidence-backed problem

Hallucinating with AI: Distributed Delusions and “AI Psychosis”

Abstract There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed “AI hallucinations”. However, deeming these AI outputs “hallucinations” is controversial, with many claiming this is a metaphorical misnomer. Nevertheless, in this paper, I argue that when viewed through the lens of distributed cognition theory, we can better see the dynamic ways in which inaccurate beliefs, distorted memories…

Health

Both readings

Responsible use of large language models in manuscript authorship, peer review, and editorial processes: a Delphi consensus among editors-in-chief of anaesthesia and pain medicine journals (RULE-AP)

This article presents a Delphi consensus developed by a panel of editors-in-chief of anaesthesiology and pain medicine journals to guide the responsible use of large language models (LLMs) in academic publishing. LLMs offer potential benefits for scientific writing, including language editing, summarisation, translation, information organisation, and support for non-native English speakers, but their misuse raises concerns about accuracy, transparency, confidentiality, and research integrity. Through a three-rou…

Science

Evidence-backed gain

Innovative Teaching Methods Supported by Artificial Intelligence and Students’ Mathematical Problem-Solving: The Mediating Role of Student Engagement

The study investigated how innovative, AI-supported teaching methods relate to the mathematics problem-solving ability of Senior High School (SHS) students in Ghana. This study examined how teachers’ AI-supported pedagogical practices relate to students’ problem-solving ability, the extent to which student engagement serves as an explanatory variable for that relationship, and whether students’ mathematical self-belief (MSB) moderates that relationship. A quantitative cross-sectional design was employed; partici…

Education

Evaluating AI-powered learning assistants in engineering higher education with implications for student engagement, ethics, and policy

As generative AI becomes increasingly integrated into higher education, understanding how students engage with these technologies is essential for responsible adoption. This study evaluates the Educational AI Hub, an AI-powered learning framework, implemented in undergraduate civil and environmental engineering courses at a large R1 public university. Using a mixed-methods design combining pre- and post-surveys, system usage logs, and qualitative analysis of students' AI interactions, the research examines perce…

Education

Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and prote…

Health

Artificial intelligence for geoscience: Progress, challenges, and perspectives

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertaintie…

Science

Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines…

Policy

Artificial intelligence-driven green education for sustainable development goals: A review

The intersection of environmental education and artificial intelligence signifies a paradigmatical paradigm shift towards attainment of the United Nations Sustainable Development Goals, and specifically in SDG 4 (Quality Education) and SDG 13 (Climate Action). This is despite the fact that despite considerable growths in technology, the traditional methods used to dispense education are challenged by the need to be able to offer scalable, personalized and beneficial green education that will communicate environm…

Education

AI-driven corporate governance: a regulatory perspective

The use of AI-driven or AI-assisted systems in corporate compliance is a novel topic in the business and management literature, only marginally considered by legal academia. While some scholarly pieces have explored the use of artificial intelligence (AI) systems to support compliance in the financial sector (eg, for anti-money laundering purposes), the legal implications of deploying AI systems to fulfil corporate compliance tasks has not yet received the attention it deserves. This article approaches the use o…

Policy

AI-driven corporate governance: a regulatory perspective

Financial fraud detection through the application of machine learning techniques: a literature review

Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham methods were applied, and 104 articles published between 2012 and 2023 were examined. These articles were selected based on predefined inclusion and exclusion criteria and were obtained from databases suc…

Crime

Financial fraud detection through the application of machine learning techniques: a literature review

Two peas in two pods? Comparing Sweden and Singapore’s lifelong learning policy and practice response to AI skills demands

Singapore and Sweden invest substantial resources in upskilling their citizens for a digital transition fuelled by advanced technologies such as artificial intelligence (AI). There, the digital transition faces skills shortages and mismatches, leading to increased pressure on lifelong learning (LLL) systems to address these shortages. In this comparative case study, we examine how respective LLL policies and practices address the demands for basic and advanced AI skills. Comparisons are discussed on three levels…

Policy