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EducationBoth readings

Large language model use in dental education: a cross-sectional multi-country study

Background Large language models (LLMs) are increasingly used in higher education, but multi-country evidence on dental students' use, verification, and integrity practices is limited. Objective To compare senior dental students' LLM use, perceived time and academic impact, reliability judgements, verification practices, and integrity safeguards across five countries. Methods An anonymous cross-sectional online survey was administered to final-year dental students in the United Arab Emirates (UAE), Jordan, Malay…

Medical Education Online

Both readings

Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis

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Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning

Evidence-backed gain

Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions

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Safety fears as scientists make first viruses designed by AI

Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe. The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killed E coli bugs that were resistant to natural bacteriophages. Dr Brian Hie, a chemical engineer at Stanford Un…

Both readings

The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review

With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigor…

Both readings

Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research

Background/Objectives: Artificial intelligence (AI) has emerged as a transformative force in healthcare, with nutrition and dietetics becoming key areas of application. AI technologies are being employed to enhance dietary assessment, personalize nutrition plans, manage chronic diseases, deliver virtual coaching, and support public health nutrition. This review aims to critically synthesize the current literature on AI applications in nutrition, identify research gaps, and outline directions for future developme…

Evidence-backed problem

Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques

Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geogenic mineralisation, and localised anthropogenic contamination. In addition to hydrochemical indices and multivariate statistics that have been used over the years to assess coastal water quality, this study has incorporated nonlinear machine learning and probabilistic risk assessment to enhance source discrimination and uncertainty-based health risk characterisat…

Health

Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions

Artificial intelligence is increasingly used in health care and medical education. This study aimed to identify factors associated with medical students' attitudes towards artificial intelligence, with particular attention to digital literacy, emotional intelligence and artificial intelligence-related perceptions. This cross-sectional study was conducted between November 2025 and January 2026 among 358 medical students. Data were collected using an online questionnaire including sociodemographic items, the Trait…

Health

TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases

Enrollment in phase I oncology trials remains low largely because potentially eligible patients are not identified and evaluated quickly enough. Current clinical trial matching systems can identify candidate patients from the electronic health record, but cases with missing or uncertain eligibility data are often routed for offline manual review. This delay impedes clarification and prolongs the final eligibility determination. This study evaluated TrialTriage, a semiautonomous system built on the n8n platform a…

Health

Machine learning-based prediction of postoperative nausea and vomiting after spinal anesthesia: A retrospective observational study

Postoperative nausea and vomiting (PONV) is a frequent and serious complication after surgery. PONV also reduces patient satisfaction with surgery under spinal anesthesia and increases medical costs due to prolonged hospitalization. The purpose of this study is to apply artificial intelligence (AI) machine learning analysis to identify risk factors for PONV in patients undergoing surgery with spinal anesthesia. This retrospective study used artificial intelligence to analyze data of adult patients (aged ≥20 year…

Health

Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling

Incomplete immune reconstitution (IIR) is a serious complication affecting 10 to 40% of people living with HIV (PLWH) despite effective antiretroviral therapy, leading to increased morbidity and mortality. Current risk prediction models rely on single-time point measurements and lack dynamic assessment capabilities. We developed a dynamic joint prediction system for IIR risk (DJPSIIR) using Bayesian joint modeling to analyze longitudinal data from 21,862 PLWH across 31 Chinese provinces (2003-2024). The system i…

Health

Evaluating Artificial Intelligence Translation Tools for Language Equivalence of Oncology-Informed Consent Forms From English to Spanish

Approximately 8% of the US population speaks primary languages other than English. Limited English proficiency (LEP) contributes to under-representation of Hispanic patients in oncology clinical trials. Although certified translation services exist, they are time-consuming and costly. Artificial intelligence (AI)-generated translations of informed consent forms (ICFs) could provide low-cost alternatives, but data on accuracy and safety remain limited. We evaluated language equivalence of English-to-Spanish trans…

Health

AI as a Therapist, Companion, and Romantic Partner: Emerging Roles, Benefits, and Risks for Mental Health in Participatory Medicine

The line between tool and companion was once obvious, but conversational AI is blurring it in ways few researchers anticipated. Large language model chatbots and purpose-built AI companion agents are now used by millions of people every day. They are not being used to simply retrieve information but, instead, to offer emotional support, help process personal distress, and sustain what many describe as genuine relationships. Research puts the scale of this shift in sharp relief as nearly half (48.7%) of individua…

Media & Arts

AI makes a feature film in 14 days: Higgsfield shares its $500,000 filmmaking process with creators

Higgsfield AI has released the full production pipeline behind its 95-minute AI-generated action film, allowing creators to access the prompts, character models and workflows used to make the movie. The startup says it completed the film in 14 days for $500,000.