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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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[Artificial intelligence in hypertension: where do we stand?]

Artificial intelligence (AI) is entering the study of hypertension, serving both primarily clinical purposes - to assist practicing physicians - and research objectives. Hypertension presents certain specific characteristics that should be carefully considered when using AI. The measured value of blood pressure is an extremely variable parameter that is difficult to standardize and measure with precision. At present, AI is able to provide very simple, clear, and well-documented answers to clinical questions rega…

Giornale Italiano di Cardiologia · Health

[Artificial intelligence in hypertension: where do we stand?]

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Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study

Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated Inflammacheck, a point-of-care device utilizing exhaled breath condensate (EBC) H 2 O 2 and physiological parameters with machine learning for non-invasive lung cancer detection in a real-world screening population. Exhaled Hydrogen Peroxide for Early Lung Cancer Detection (ExPeL) study participants, from the UK Targeted Lung Health Check (TLHC) programme, included i…

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Evidence-backed gain

Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis

Sepsis, characterized by a rapid transition to systemic immune dysregulation and multiorgan failure, poses a formidable clinical challenge. The lack of spatiotemporally stable biomarkers severely impedes early diagnosis and risk stratification. By integrating large-scale transcriptomic profiling with machine learning algorithms, this study identified a robust three-gene diagnostic signature (TLR5, HMGB2, and C19orf59). Single-cell RNA sequencing precisely localized the sepsis-induced specific upregulation of the…

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Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study

Objective This study aimed to evaluate the quality of GPT-4-generated responses to commonly asked psychosis-related psychoeducational questions from patients, caregivers and relatives in a first-episode psychosis programme. Evaluation focused on accuracy, clarity, inclusivity, completeness, clinical utility and overall quality. Design This cross-sectional study employed a qualitative evaluation design. GPT-4, accessed via the ChatGPT interface, generated responses to 20 psychosis-related psychoeducational questi…

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Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principles to radiology trainees. However, the potential of AI to transform radiology education remains underexplored. The authors review how AI can be leveraged to enhance radiology education, from curriculum planning to its implementation and evaluation. Guided by Harden's 10-step framework for curriculum development, they systematically examine current and potential futu…

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Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. We systematically searched PubMed, Embase, Web of Science, and Cochrane Libra…

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Evidence-backed gain

Classification of tau status with machine learning models in amyloid-positive cohorts

Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)- and amyloid PET-derived features may serve as useful screening tools for tau pathology. Multiple machine learning models were developed to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (…

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Why bad health information can seem so convincing-and five ways to resist it

Recent reports have highlighted AI-generated "doctors" spreading dubious health advice to millions of social media users. Fabricated wellness trends can travel just as quickly. A supposed "pink jelly" weight-loss treatment, for example, reportedly fooled at least one celebrity.

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Black people in the US: have you used AI for health-related questions?

Artificial intelligence is growing in influence in the healthcare space, and more people are seeking guidance from AI tools, including AI-generated social media influencers. Black people in the US, who have been long subject to racism in the healthcare system, are among some of those who are leaning on AI for health-related matters and information. We would like to hear from Black respondents about their experiences with Black AI-generated health and spiritual influencers.

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Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program

Background: Identifying patients at risk of opioid overdose in healthcare settings is critical, yet evidence on predictive models and their performance to predict imminent opioid overdose remains limited. Objective: We compared classical and Machine Learning (ML) survival models to predict 30-day overdose risk following a first opioid-related diagnosis to determine whether algorithmic complexity improves clinical decision support. Methods: We conducted a prospective cohort study using longitudinal Electronic Hea…

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Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program

Educational Artificial Intelligence Software to Support Assessment of Atopic Dermatitis Severity

Accurate assessment of the severity of atopic dermatitis is crucial for guiding treatment. However, the Eczema Area and Severity Index (EASI), a widely used clinical assessment tool, relies on subjective visual scoring, which can lead to inter-rater variability. This study aimed to evaluate the performance of convolutional neural network-based artificial intelligence software in assessing atopic dermatitis severity from uncropped, unmasked skin images acquired under uncontrolled conditions. In this prospective,…

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Educational Artificial Intelligence Software to Support Assessment of Atopic Dermatitis Severity

[Use of artificial intelligence in clinical practice and hospitals]

Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications…

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Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients

Postoperative hydrocephalus is a common complication following posterior fossa tumor resection, affecting 7-40% of patients. Although preoperative cerebrospinal fluid (CSF) diversion may be required in selected patients with hydrocephalus, decisions remain individualized in routine neurosurgical practice. We therefore developed and externally validated a model to provide supplementary preoperative risk stratification using routinely available variables. We retrospectively analyzed 1,073 patients following resect…

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Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients

Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identifica…

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Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges

Typhoid fever, caused by Salmonella enterica subsp. enterica serovar Typhi (Salmonella Typhi), remains a significant global health challenge that is increasingly complicated by the emergence and spread of multidrug-resistant (MDR) and extensively drug-resistant strains. Growing limitations of antibiotic-centered treatment strategies have stimulated interest in anti-virulence approaches targeting bacterial regulatory networks rather than viability alone. Among these, quorum-sensing (QS), particularly the LuxS-med…

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Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges