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HealthEvidence-backed problem

OpenAI hack on Australian government reveals anxiety at heart of global artificial intelligence dilemma

When the Australian prime minister, Anthony Albanese, sat down for an interview in the heart of Silicon Valley at the weekend he had known for two days that his was the first government known to have been attacked by a rogue AI agent. He didn’t reveal the attack then, but he sounded a warning about the march of AI: “the risk is that AI develops in a way in which humans are no longer in control of what AI is producing.” But Albanese noted, too, that two countries – the US and China – would dominate the world’s re…

The Guardian

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'Just look at Kodak or Blockbuster': Former Disney and DreamWorks chief Jeffrey Katzenberg says Hollywood must embrace AI or risk irrelevance

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Folk or AI could be a winner for the UK at Eurovision | Letters

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OpenAI’s Altman and Anthropic’s Amodei address UN security council

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Agentic AI, medical morality and the transformation of the patient-physician relationship

Agentic artificial intelligence (AI) is an emerging development in the digital transformation of healthcare. Unlike conventional generative AI, agentic AI systems can perform autonomous, goal-directed actions and coordinate complex tasks. They promise to support or even collaborate with clinicians and patients in increasingly independent ways. While agentic AI raises familiar moral concerns regarding safety, accountability and bias, this article focuses on a less explored dimension: its capacity to transform the…

Evidence-backed problem

Leveraging AI for infectious disease modelling and public health decision making

Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions. The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness. Examples inclu…

$Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort$

Evidence-backed gain

Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort

Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children. Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and D…

Evidence-backed problem

Quantitative Assessment of Facial Expression Asymmetry in Parkinson's Disease

Objective Hypomimia, or reduced facial expressiveness, is a cardinal motor feature of Parkinson's disease (PD). Although limb motor symptoms in PD are characteristically asymmetric, whether facial hypomimia exhibits comparable asymmetry remains poorly understood. We investigated facial expression asymmetry in PD using AI-based computer vision and machine learning. Methods Videos of instructed emotional facial expressions were collected from 101 individuals with PD and 94 healthy controls (HCs) across two indepen…

Science

Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM res…

Health

AI-Driven precision targeted therapy for EGFR-mutant non-small cell lung cancer: From therapeutic evolution and resistance mechanisms to intelligent decision systems

ObjectiveTo critically review the current pharmacologic treatment landscape for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), focusing on clinical pharmacology, therapeutic evolution, and emerging challenges of EGFR-targeted therapies.Data SourcesPeer-reviewed literature in PubMed, Web of Science, and Embase databases (January 2000 to June 2026) was searched using keywords including "EGFR-mutant NSCLC," "EGFR-TKIs," "drug resistance," "artificial intelligence," and "precision…

Health

Big tech says AI can find a cure for cancer. So where is it?

Her name was June. June would be the last month she ever knew. I remember the way her bed made her so small, swallowed her up in the middle of the room, turned as it was to face the window. With her liver failing, her skin was a ghostly apricot. June – I called her Nan – had been stalked by the king of terrors, the emperor of all maladies: cancer. That son of a bitch. The cancer was in her pancreas. Odds of survival? “Not good” – putting it lightly. The maths, putting it darkly: it’s the 12th most common cancer…

Lifestyle

Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration

Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, fr…

Science

Code-multiplexed multi-frequency impedance cytometry with a unified deep-unfolding network

Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels. Existing methods, however, rely on multiple task-specific networks with template-based linear fitting, which loses accuracy under nonlinear or unstable conditions common…

Health

Methodological transitions in mental health research driven by machine learning

Machine learning is increasingly reshaping psychological and mental health research by complementing traditional theory-driven approaches with data-driven predictive modelling. This encompasses three key transitions: from hypothesis-testing to pattern discovery; from controlled experimental settings to naturalistic and multidimensional data ecosystems; and from explanatory theoretical models to prediction-informed theory-building and clinical translation. Nevertheless, this methodological reorientation remains i…

Health

From Answers to Agents: What Must Change Before Generative and Agentic AI Become Clinical Infrastructure in Hematology

Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory. In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with…