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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Artificial intelligence in drug discovery - what it is, where we stand and the path forward

Artificial intelligence (AI) in drug discovery has attracted increasing interest over the past decade. It is now time for a critical review of progress in the field: where did we advance - and where are we yet to see impact - when it comes to what matters in drug discovery, which is to deliver safer and more efficacious medicines to patients faster? Although a wide variety of AI methods have been developed, applied and benchmarked, evidence of their clinically relevant impact is, so far, disappointingly limited.…

Nature Reviews Drug Discovery · Health

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Sixteen AI-designed viruses offer a new route against drug-resistant bacteria

In a world first, scientists led by a team from Stanford University have created 16 viable viruses that do not exist in nature and were designed by AI. Their experiment, which is published in Science, could help in the fight against superbugs by allowing researchers to design customized viruses to kill drug-resistant bacteria. Thomas Inglesby and Moritz S. Hanke have published a Perspective piece on the work and its implications in the same edition of the journal.

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Mortality prediction of road traffic crash with artificial intelligence: a systematic review

Background Road traffic crashes cause substantial global mortality and disability. Conventional injury severity scores may not fully capture the complex interactions among demographic, clinical, crash and environmental factors. Artificial intelligence and machine learning may improve mortality prediction by modelling non-linear patterns in traffic crash data. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance. PubMed/MEDLINE, Web of S…

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Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer

Background Bladder cancer is the 11th most common cancer in the United Kingdom, with approximately 10,500 new cases annually. Diagnosis and surveillance typically involve cystoscopy, an expensive, time-consuming, and uncomfortable procedure which has encouraged efforts to identify biomarkers, particularly in urine, given its direct contact with malignant tissue. Methods Urine collected from 100 participants (50 bladder cancer patients, 50 controls) was subjected to solvent extraction followed by gas chromatograp…

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Quality assessment of artificial intelligence responses in erectile dysfunction: a comparative study based on EAU recommendations

Artificial intelligence (AI)-based language models are increasingly explored as tools for interpreting and applying clinical guideline recommendations. In urology, the European Association of Urology (EAU) recently introduced a guideline-specific chatbot; however, its comparative performance relative to contemporary general-purpose large language models (LLMs) remains unclear. In this structured comparative study, five AI systems-the EAU Guidelines Bot, ChatGPT-5, Gemini 2.5 Pro, Copilot - Smart GPT-5, and Perpl…

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AI-based augmentation of oncology clinical trials

Oncology clinical trials are often characterized by slow accrual, high failure rates and limited generalizability, reflecting both biological complexity and operational inefficiencies. Advances in artificial intelligence (AI) - enabled by large-scale electronic health record datasets and machine learning methods - offer new opportunities to address these challenges across the clinical trial lifecycle. In this Review, we discuss applications of AI across pre-trial design, trial conduct, and post-trial inference a…

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Deep learning-based physical exercise assessment of older adults using single-camera videos

Author summary Staying physically active is essential for older adults to maintain their independence, but many residents in care homes do not receive the individualized exercise supervision they need. We explored how artificial intelligence can help fill this gap. In our study, we developed a computer system that can watch a person exercise through a single video camera and automatically evaluate how well the exercises are performed. Specifically, the system identifies which exercise is being done and estimates…

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AI Creates 16 Brand-New Viral Genomes Never Seen in Nature, Scientists Call It a Turning Point

A Science study shows generative AI can move beyond analysing DNA to designing complete viral genomes, raising new questions about biological design and biosecurity.

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Patient Harm From Orthodontic Care (Part II): Incident Analysis of Contributory Factors and Perceived Avoidability of Harm

Background Patient safety, defined as the absence of avoidable harm, in orthodontics is still in its early stages of development. Knowledge on the subject is limited. Aim (1) To conduct an incident analysis, (2) to measure avoidability, (3) to investigate the relationship between contributory factors and perceived avoidability of harm from orthodontic care, and (4) to suggest methods to mitigate potential patient harm incidents. Materials and methods Data were retrospectively collected from consecutively filed o…

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Patient Harm From Orthodontic Care (Part II): Incident Analysis of Contributory Factors and Perceived Avoidability of Harm

Ethical considerations for multimodal artificial intelligence in healthcare

Multimodal artificial intelligence (MMAI) is transforming biomedicine by integrating heterogeneous data, e.g., images, speech, behavior, physiological signals, and text, into unified representational spaces. This enables powerful cross-modal inference and data synthesis, with potential gains in diagnostic accuracy, early detection, and patient support. However, these capabilities introduce ethical challenges that exceed existing AI governance frameworks. MMAI can infer sensitive information without patient aware…

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First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method

Aim This study represents the first methodological stage of a broader AI-assisted Cameriere European dental age estimation workflow. The aim of this first stage was to evaluate the performance of YOLOv8-based deep learning models in automatically detecting the anatomical landmarks and apical structures required for the Cameriere European method. Rather than directly estimating dental age, the proposed system was designed to automate the measurement-related inputs needed for subsequent Cameriere European-based ag…

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First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method

First AI to Design Brand New Viruses Leads Scientists to Warn of 'Urgent Biosafety' Concerns

Researchers have used AI to design complete viral genomes that function in labs, potentially advancing medical research but raising biosafety concerns. The breakthrough could aid in combating

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Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity

Advances in data science and medical artificial intelligence (AI) raise complex philosophical and ethical quandaries about what it means to know a person or a community through data and what kinds of people and societies we are becoming in this era of predictive data science. Drawing on four lightly fictional but reality-informed case studies in mental health, radiology, genomics and environmental public health, we reflect on how AI technologies, largely built on Western biomedical traditions, may conflict with…

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Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models

Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort. Cross-sectional data from 6509 children and adolescents aged 3-12 years with prior RTI…

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Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning

To make accurate orthodontic extraction decisions, various clinical and cephalometric variables must be evaluated. This study aims to evaluate ChatGPT-5.0's performance in distinguishing orthodontic extraction decisions and to compare it with five supervised machine learning (ML) algorithms. Of 550 retrospectively evaluated orthodontic records, 30 were reserved for calibration, leaving 520 for the main analysis. The reference standard was the consensus treatment decision of three expert orthodontists with more t…

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