+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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1045 published stories · page 2 of 70

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Can you futureproof your career by choosing an AI-resistant degree?

Futureproofing your career with an AI-resistant degree is a tough ask, says Charlie Ball, an expert on graduate employment for Jisc, the UK’s higher education digital, data and technology agency. “What you’re trying to do is futureproof a 45-year career in a time of rapid technological change – and that’s really quite hard to do. What’s likely is that jobs that hinge a lot on human, face-to-face interaction are unlikely to be replaced.” He warns however that the discussion should focus less on identifying specif…

The Guardian · Labor

Can you futureproof your career by choosing an AI-resistant degree?
Temporal and cross-site validation of an AI system for self-harm detection
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Temporal and cross-site validation of an AI system for self-harm detection

Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that an artificial intelligence (AI)-based system could effectively detect self-harm in emergency department triage notes. However, the system was developed using data from a single hospital, raising concerns about its generalisability. Here, we aim to validate the system prospectively and externally to better understand its portability across hospitals. We leveraged emergency department data from two Australi…

Health
Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support
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Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support

Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately higher mortality rates in low- and middle-income countries (LMICs) where diagnostic delays and limited specialist pathology capacity are widespread. While machine learning (ML) approaches achieve strong predictive performance for cancer classification, algorithmic opacity and absence of interpretability frameworks tailored to resource-constrained environments have impeded clinical adoption. This study bridg…

Health
Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status
Evidence-backed gain

Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status

Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impairment than the general population. The comorbidity of cognitive impairment and chronic diseases significantly impacts the lives of these patients. This study aims to develop machine learning models to identify depression risk among older adults with chronic illnesses across different levels of cognitive impairment. Methods Data were derived from the Chinese Longitudinal Healthy Longevity Survey (n = 5798…

Health
Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty
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Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty

Purpose To compare the information quality, accuracy, and readability of patient-directed responses generated by large language models (LLMs), including ChatGPT-o3, ChatGPT-5.2, Gemini 3, and DeepSeek, regarding robotic-assisted total knee arthroplasty (RA-TKA). Methods Thirty frequently asked patient questions were identified using LLM outputs and Google search queries. Responses were evaluated for information quality using the DISCERN and Quality Analysis of Medical Artificial Intelligence (QAMAI) instruments,…

Health
Systematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case
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Systematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case

Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical prediction frequently report ML as superior, but the methodological framework producing those comparisons has received limited scrutiny. We aimed to quantify the apparent discrimination advantage of ML over LR using trauma mortality prediction as an empirical case, and to characterise the evaluation practices that shape it. Study design and setting Systematic review and random-effects meta-analysis combined with…

Health
Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification
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Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification

Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-particularly the accurate prediction of conversion from mild cognitive impairment (MCI) to AD-is essential to enable timely and effective therapeutic interventions. Deep learning (DL) models have demonstrated substantial promise in this domain; however, critical challenges persist, including multiclass staging of disease progression, longitudinal data modeling, and effective multimodal data integration. Th…

Health

GENERATIVE AI FOR HIRSCHSPRUNG DISEASE: CAN SYNTHETIC FLUORESCENCE CONFOCAL MICROSCOPY IMAGES ENHANCE INTRAOPERATIVE DETECTION OF GANGLIONIC BOWEL?

Background Accurate identification of ganglionated bowel is essential during laparoscopic pull-through for Hirschsprung Disease (HD), yet intraoperative biopsy interpretation is time-sensitive and operator-dependent. Fluorescence confocal microscopy (FCM) provides rapid imaging of fresh tissue, and deep-learning (DL) has the potential to extract diagnostic patterns from these images automatically. However, DL development is limited by HD rarity and images scarcity. Generative-AI may address this gap by synthesiz…

Health
GENERATIVE AI FOR HIRSCHSPRUNG DISEASE: CAN SYNTHETIC FLUORESCENCE CONFOCAL MICROSCOPY IMAGES ENHANCE INTRAOPERATIVE DETECTION OF GANGLIONIC BOWEL?

Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and safety of solid-fuel boilers and gasifiers. However, conventional machine learning methods typically treat each characteristic temperature as an independent prediction target, resulting in temperature inversions that violate the required physical ordering of AFTs. This study aimed to develop a Categorical Chain Differential framework for the coupled and physically consistent prediction of the four AFTs ac…

Climate
Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

AI may be denting computer science graduates’ job prospects, UK data shows

AI may be warping the job prospects for students in the previously high-demand subjects of computer science and economics, according to a detailed look into the careers of recent UK graduates. Data obtained for the 2027 Guardian University Guide published on Saturday shows that coding and software development were the fastest-falling occupations for graduates last year, while employer demand for graduates in well-paid roles in financial categories such as economists and management consultants also declined. Expe…

Labor
AI may be denting computer science graduates’ job prospects, UK data shows

‘Immature playground boasting’: Mathematicians uneasy at OpenAI’s latest scalp

It was a week that left mathematicians reeling. Hot on the heels of a flurry of cases of artificial intelligence furthering the field, OpenAI declared a major scalp: its latest AI model had cracked a Millennium Prize Problem, a puzzle with a $1m reward that had defied human brains for decades. The achievement bore little resemblance to how mathematical problems normally fall. A near-trillion dollar private company had unleashed 10,000 agents – AI systems that carry out tasks autonomously – on the problem. The bi…

Labor
‘Immature playground boasting’: Mathematicians uneasy at OpenAI’s latest scalp

Suno admits it scraped audio from YouTube in a court filing.

We already kinda knew that thanks to a hack of the company’s data in July. And Suno admitted to training its models on publicly available music. But this is the first time it has explicitly copped to ripping audio from YouTube, saying in a court filing: > Suno admits that audio data was obtained from YouTube for use as training data using YT-DLP. [Link: Suno admits it obtained YouTube audio to train its AI | https://www.musicbusinessworldwide.com/suno-admits-it-obtained-youtube-audio-to-train-its-ai-but-challeng…

Media & Arts
Suno admits it scraped audio from YouTube in a court filing.