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AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot
LifestyleEvidence-backed problem

AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot

Advancements in artificial intelligence (AI) have led to the increase of conversational agents like Replika, designed to provide social interaction and emotional support. However, reports of these AI systems engaging in inappropriate sexual behaviors with users have raised significant concerns. In this study, we conducted a thematic analysis of user reviews from the Google Play Store to investigate instances of sexual harassment by the Replika chatbot. From a dataset of 35,105 negative reviews, we identified 800…

Proceedings of the ACM on Human-Computer Interaction
The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis
Evidence-backed gain

The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis

Manual evaluation of non-contrast CT scans (NCTS) for detecting subdural hematoma (SDH) is time consuming, potentially inaccurate, and subjective to the expert analyzing them. In recent years, two deep learning (DL) algorithms have been popularly studied in this respect, namely convolutional neural networks (CNN) and U-Net architectures, the latter being a specialized type of CNN. We performed the first meta-analysis comparing various DL models for SDH detection. MEDLINE, Cochrane, Scopus, and Embase databases w…

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

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

Objective To systematically evaluate the diagnostic accuracy and methodological quality of machine learning (ML) prediction models for pregnancy outcomes after assisted reproductive technology (ART). Methods PubMed, Embase, the Cochrane Library, IEEE Xplore, MEDLINE, ClinicalTrials.gov, CNKI, Wanfang, and VIP were searched from inception to July 2026. Eligible studies developed or validated ML models to predict clinical pregnancy or live birth after ART. For studies reporting complete 2 × 2 contingency data, poo…

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

Purpose Clinical decision-making in glaucoma is complex and requires integration of heterogeneous information, including patient history, examination findings, and risk stratification. While artificial intelligence (AI) has shown strong performance in image-based ophthalmic tasks, its capability in specialty-specific clinical reasoning remains insufficiently explored. Methods Performance was evaluated by glaucoma specialists using a predefined rubric across three clinically oriented domains: medical accuracy (40…

Health

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

Introduction Infection is a common and potentially fatal complication during the treatment of hematological diseases, particularly in the context of chemotherapy-induced immunosuppression. The nonselective use of antibiotic prophylaxis in patients with neutropenia in China has persistently accelerated antimicrobial resistance. Early identification of patients at high risk for infection before clinical symptom onset could enable targeted preventive strategies; however, reliable and biologically informed screening…

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

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…

Safety fears as scientists make first viruses designed by AI
Health

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…

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

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…

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

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…

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

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

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