Health

626 stories · page 9 of 42

HealthEvidence-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…

Journal of Movement Disorders

Evidence-backed problem

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

Evidence-backed problem

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

Evidence-backed gain

Methodological transitions in mental health research driven by machine learning

Evidence-backed problem

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…

Evidence-backed problem

Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians

Objective To evaluate the real-world diagnostic performance of a multimodal large language model (LLM) for image-based assessment of oral mucosal lesions compared with clinicians of varying expertise. Study design Prospective international multicenter diagnostic accuracy study. Setting Twenty university and tertiary head and neck centers in Italy, Belgium, France, Spain, and Israel. Methods We enrolled 350 consecutive patients (320 with oral lesions, 30 with normal mucosa). Clinical photographs and basic epidemi…

Evidence-backed problem

Teaching AI-Informed Diagnostic Reasoning in Dental Education: A Conceptual Framework

Artificial intelligence (AI) is increasingly present in dental education and clinical practice through systems that support image interpretation, lesion detection, risk estimation, treatment planning, documentation, and access to clinical information. Existing curriculum frameworks appropriately emphasize foundational knowledge, ethical use, governance, and human-centered practice. Broad AI literacy, however, does not by itself teach students how to reason when an algorithmic output influences diagnosis or manag…

Evidence-backed gain

Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder

The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD…

Health

An application of random forest regression for predicting healthcare costs using administrative databases

Background Accurate healthcare cost predictions are essential for health economic decision-making. We propose a bottom-up approach that aggregates individual-level predictions to estimate expenditures for specific population segments without relying on a priori segmentation. Methods This retrospective population-based study used administrative healthcare data (2011-2023) from the Health Protection Agency of Bergamo (Italy). We traced 5-year healthcare resource utilization to predict individual's healthcare costs…

Health

Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism

Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces. Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japan…

Health

Neural network analysis of the transmission dynamics of leptospirosis infection subject to vaccination and treatment interventions

The rapid spread of infectious diseases can lead to substantial casualties worldwide, with the greatest impact on economically disadvantaged and resource-deficient nations. The widespread transmission of infection disrupts social life and economic activities, amplifying their impact beyond the public health system. In this paper, we analyze the dynamics of leptospirosis disease considering treatment for humans and vaccination for the animal population along with environmental bacteria control. We analyzed the qu…

Health

[Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]

Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification. Yet strong statistical performance does not automatically translate into meaningful clinical benefit. This second part of Tailoring Care to the Mind: The Emerging…

Health

Multiscale model calibrated with a Bayesian algorithm predicts long-term outcomes of treatments for mitral valve regurgitation in animal models

In primary mitral valve regurgitation (MR), a failing valve induces volume overload (VO) of the left ventricle (LV), triggering neurohormonal responses and myocardial remodeling. Surgical valve repair is the most effective therapy for MR, improving symptoms and in some patients reversing cardiac hypertrophy. However, at least 20% of patients develop postoperative impairment of LV function with detrimental effects on long-term outcomes, with no known cause. Many MR patients receive pharmacological treatment to al…

Health

Smartphone AI-Enabled Lateral Flow Immunoassay Platform Using Advanced Quantum Dots for Intelligent Quantitative Diagnostics

Lateral flow immunoassays (LFIAs) are widely used in point-of-care diagnostics for their low cost and simplicity, but conventional formats rely on subjective visual readout and lack quantitative accuracy and sensitivity. Here, we present a smartphone-guided, artificial-intelligence-enhanced LFIA platform that unites a rationally engineered dual-modal nanoprobe with on-device machine learning, demonstrated for quantitative detection of Rift Valley fever virus (RVFV). The platform combines three synergistic princi…

Health

Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care

Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient awareness and limited nephrology resources. This study evaluated the potential of large language models (LLMs) to support CKD diagnosis in primary care using time-series electronic health record (EHR) data. Longitudinal data were extracted from the EHR system of Weinan, China. Among 29 963 adults meeting inclusion criteria, 2300 participants were randomly sampled to generate clinical vignettes. CKD status…