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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations

Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world depl…

IEEE Access · Health

Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations

Evidence-backed gain

CURL-AID: Automated Echocardiographic Motion Analysis for Quantitative Assessment of Posterior Systolic Curling

Objective Posterior systolic curling (PSC) is a morphofunctional abnormality of the posterior mitral annulus associated with malignant ventricular arrhythmias and sudden cardiac death. Current diagnosis is qualitative and operator-dependent, limiting reproducibility, objectivity and standardization. This study introduces CURL-AID (Curling Ultrasound-based Recognition and Labeling-Automated Intelligence-driven Diagnosis), a fully automated echocardiographic framework for PSC detection through quantitative analysi…

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Evidence-backed problem

Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro

Purpose Lumbar disc herniation is associated with substantial morbidity, including low back pain, radicular leg pain (sciatica), sensory disturbance, and motor deficit. Magnetic resonance imaging (MRI) is central to confirming the diagnosis in symptomatic patients and to planning surgical or interventional management. Recent advances in artificial intelligence (AI) raise the possibility of automating aspects of image interpretation to improve consistency and reduce radiologist workload. This study evaluates a ge…

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Advances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence

Artificial intelligence (AI) and machine learning are increasingly used in toxicological risk assessment to predict chemical toxicity, identify hazardous compounds, and support regulatory decision-making. However, the widespread adoption of these models is limited by their "black-box" nature, which reduces interpretability, transparency, and regulatory confidence. Explainable artificial intelligence (XAI) has emerged as a promising approach to address these challenges by revealing how input features, including c…

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Additional sectioning and AI-assisted diagnosis reveal underdiagnosis of serous (pre)malignancies in fallopian tube specimen from BRCA1/2 carriers

Aim Germline BRCA1/2 pathogenic variant carriers are at increased risk for high-grade serous carcinoma (HGSC) and are therefore advised to have a risk-reducing salpingo-oophorectomy (RRSO) around the age of 40. A risk of 0.9% to develop peritoneal HGSC (pHGSC) remains, which increases up to 27.5% when serous tubal intraepithelial carcinoma (STIC) is detected at RRSO. The relationship between the detection of STIC and the occurrence of pHGSC is still poorly understood. Here, we investigated the role of tissue sam…

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Evidence-backed gain

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns

Background Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. Objective To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction models incorporating oxidative stress index score. Design A novel liver enzyme indicator - oxidative stress index score…

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Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider

Introduction Patients with musculoskeletal complaints often search online to identify an appropriate healthcare provider. With the increasing availability of large language models (LLMs), these artificial intelligence (AI) tools can direct patients to providers. This study evaluated the ability of LLMs to recommend appropriate providers based on representative patient musculoskeletal queries. Methods Three LLMs (ChatGPT, DeepSeek, and Gemini) were prompted with standardized musculoskeletal queries for two US cit…

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The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Diagnostic errors are a substantial source of patient harm. As artificial intelligence (AI) integrates into clinical workflows, opportunities are emerging to assess their impacts on diagnostic excellence (DxEx). The Coordinating Center for Diagnostic Excellence (CODEX) at the University of California San Francisco established the Action Incubator to translate research advances in DxEx into tangible strategies for improving diagnosis. The September 2025 in-person inaugural Action Incubator convened 30 multidiscip…

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The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda

Introduction Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. Methods We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometri…

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Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study

Background Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not experience clinically significant symptom reduction. Identifying these patients prior to the initiation of ICBT can support treatment planning. Objective The aim of this study was to enhance baseline prediction of clinically meaningful improvement in patients treated with ICBT for common psychiatric disorders in routine care, which could ultimately inform treatment plan…

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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.…

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

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

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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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

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