± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 2026
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877 published stories · page 19 of 59

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Trustworthy artificial intelligence for rural health care

Regional, rural and remote Australians experience poorer health outcomes and substantially higher rates of suicide and self-harm than those in major cities. Artificial intelligence could support earlier identification of distress, safer triage and more timely care alongside telehealth and clinical decision support, but only if it is treated as a health intervention with explicit safety nets and independent evaluation. We propose a minimum viable governance model, including Indigenous partnership, language safety…

Internal Medicine Journal · Health

Trustworthy artificial intelligence for rural health care
Artificial intelligence, work, and structural inequality: Why human-centric AI requires institutional architecture, not just ethics
Evidence-backed problem

Artificial intelligence, work, and structural inequality: Why human-centric AI requires institutional architecture, not just ethics

BackgroundThe rapid integration of artificial intelligence (AI) into labour markets, migration governance, and social protection systems is increasingly reshaping how institutional decisions are produced, delegated, and enforced. While human-centric and ethics-based AI frameworks have established important normative principles, concerns regarding inequality, opacity, and accountability in AI-mediated decision-making continue to persist across labour-related environments.ObjectiveThis article examines why ethical…

Policy
Mapping the Evolving AI Preferences and Care Needs in Orthopedic Transitional Care From Hospitals to Home: Cross-Sectional Study
Evidence-backed gain

Mapping the Evolving AI Preferences and Care Needs in Orthopedic Transitional Care From Hospitals to Home: Cross-Sectional Study

Enhanced recovery after surgery protocols have shortened orthopedic hospital stays but have shifted rehabilitation and safety-monitoring tasks to patients and families after discharge. In this study, AI refers to patient-facing digital systems for orthopedic transitional care, including large language model chatbots, computer vision or platform-based monitoring tools, and wearable sensor-enabled systems for education, rehabilitation guidance, motion correction, and risk alerts. However, patient-reported preferen…

Health
A scoping review of AI-mediated informal language learning: Mapping out the terrain and identifying future directions
Evidence-backed gain

A scoping review of AI-mediated informal language learning: Mapping out the terrain and identifying future directions

Abstract This scoping review directs attention to artificial intelligence–mediated informal language learning (AI-ILL), defined as autonomous, self-directed, out-of-class second and foreign language (L2) learning practices involving AI tools. Through analysis of 65 empirical studies published up to mid-April 2025, it maps the landscape of this emerging field and identifies the key antecedents and outcomes. Findings revealed a nascent field characterized by exponential growth following ChatGPT’s release, geograph…

Education
Between the hype and harm: does artificial intelligence in health offer solace or further exclusion for marginalised populations in Sub-Saharan Africa? A scoping review
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Between the hype and harm: does artificial intelligence in health offer solace or further exclusion for marginalised populations in Sub-Saharan Africa? A scoping review

Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential to improve health outcomes. In Sub-Saharan Africa (SSA), however, concerns remain that AI may either reduce or exacerbate existing health inequities depending on how it is developed, governed, and implemented. This scoping review aimed to map and synthesise the existing evidence on the implications of AI for health equity among marginalised populations in Sub-Saharan Africa. PubMed, Web of Science, Scopus,…

Health
Sleep Diagnostics and Monitoring Technology in Obstructive Sleep Apnea
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Sleep Diagnostics and Monitoring Technology in Obstructive Sleep Apnea

Objective This article helps neurologists understand modern approaches to diagnosing obstructive sleep apnea, including the clinical role and limitations of home sleep apnea testing, and learn how they can integrate wearable and noncontact technologies into patient care to improve diagnostic efficiency, monitor treatment, and reduce health disparities. Latest developments Advances in home sleep apnea testing have expanded beyond traditional type III monitors to include wearable devices such as wrist sensors and…

Health
Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning
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Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning

Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint particles (APPs) have been previously shown to affect microbial communities in sediment. Given that typical methods for monitoring for APP presence can be specialized and challenging, alternative methods using simple, standardized, and universal approaches, such as 16S rRNA amplicon sequencing, would be highly valuable. This study uses a field-based mesocosm approach to train a random forest-based (supervised)…

Climate

A rubric to assess generative AI-based feedback on student writing assignments

Generative artificial intelligence (GenAI) tools are an increasingly common resource used in the classroom and writing process. The landscape of available GenAI tools is rapidly evolving, so having a systematic and straightforward way to evaluate new tools for incorporation into the classroom is key. For example, in science writing education, GenAI tools can be used as a supplement to instructor feedback on student writing, allowing an additional opportunity for critique and revision by the student. Here, we des…

Education
A rubric to assess generative AI-based feedback on student writing assignments

Comparison of Machine Learning and Logistic Regression in Predicting Mortality from Acute Poisoning in Young Adults: A Multicenter Study Identifying Herbicide Exposure as the Predominant Risk Determinant

Objective Compare the effectiveness of machine learning algorithms and traditional logistic regression in predicting the mortality risk of young patients with acute poisoning, and establish a risk stratification nomogram. Methods This multicenter retrospective study derived a derivation cohort of 406 young adults with acute poisoning from Wenzhou and an external validation cohort of 150 patients from Lishui.LASSO regression was used to screen predictive factors from 43 candidate variables. Compare the predictive…

Health
Comparison of Machine Learning and Logistic Regression in Predicting Mortality from Acute Poisoning in Young Adults: A Multicenter Study Identifying Herbicide Exposure as the Predominant Risk Determinant

Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis

Objective Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. Methods A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligi…

Health
Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis

Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study

Background Traumatic cervical spinal cord injury (TCSCI) often causes severe neurological dysfunction. Accurate prediction of functional recovery is essential for clinical decision‑making and rehabilitation planning. Objective To develop an ensemble learning model integrating baseline clinical data, neurological assessments, and cervical MRI features to predict neurological recovery and functional outcomes at one year post‑injury in TCSCI patients. Methods We retrospectively collected data from 410 TCSCI patient…

Health
Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer-reviewed studies applying DL to predict KOA progression from medical imaging. Two reviewers independently screened studies, extracted data, and assessed risk of bias using PROBAST-AI. The primary outcome was…

Health
Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review