Medicare

From Guidelines to Clicklists: GPT-5-Generated ERAS Checklists Improve Guideline Coverage for Bariatric and Gastrointestinal Cancer Surgery-A STROBE-Compatible Cross-Sectional Evaluation.

BACKGROUND: Enhanced Recovery After Surgery (ERAS) pathways improve outcomes after bariatric and gastrointestinal (GI) cancer surgery, yet real-world adherence remains inconsistent. Digital tools and checklists can support implementation, but their maintenance and completeness may be limited. Large language models (LLMs) could rapidly generate structured ERAS checklists; however, their coverage, c...

Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.

BACKGROUND: Traditional manual MedDRA coding in clinical data management (CDM) faces persistent challenges, including suboptimal site data quality, terminology complexity, low efficiency, inconsistent outcomes, frequent dictionary updates, and regulatory timeliness pressures-all of which hinder trial progress and compliance. OBJECTIVE: This study aimed to identify applicable artificial intelligenc...

Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective.

The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have reveal...

Comparison of Written and Visual Patient Education Methods Before Urodynamic Studies: A Randomized Controlled Trial.

PURPOSE: This study compared the effects of written versus visual patient education methods on patient anxiety, procedural comprehension, hemodynamic ...

DispatchMAS: fusing taxonomy and artificial intelligence agents for emergency medical services.

BACKGROUND: Emergency medical dispatch is a critical, high-stakes process where dispatcher decisions directly impact patient outcomes. While standardi...

Predicting progression of Mycobacterium avium complex pulmonary disease with treatment using explainable artificial intelligence techniques.

OBJECTIVES: The incidence of nontuberculous mycobacterial pulmonary disease (NTM-PD) is rising; however, only certain patients experience disease prog...

Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.

The integration of artificial intelligence (AI) into healthcare is accelerating and maternity care is at a pivotal moment for the strategic implementa...

Automated daily adaptation for breast cancer radiotherapy: benefits of deep learning-driven dose prediction workflow for CBCT-based adaptation.

Objective Daily anatomical variations can jeopardize the quality of modern radiotherapy (RT). Adaptive radiotherapy (ART) aims to address this by ...

Evidence-based AI clinical decision support system for acute burn care and complex reconstruction.

BACKGROUND: Burn surgery requires rapid, evidence-based decision-making across acute resuscitation, operative management, and reconstruction. Despite ...

A comprehensive European Colorectal Cancer Cohort dataset.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The Biobanking and Biomolecular Resources European Research Infrastruct...

Design of an optimal planning framework for cryosurgical treatment of brain tumor using CNN segmentation of MRI images.

This study presents an integrated methodology for pre-operative cryosurgical planning of irregularly shaped brain tumors using two-dimensional MRI dat...

Deep learning-based 4D robust optimization of intensity-modulated proton therapy for lung cancer radiotherapy.

Intensity-modulated proton therapy (IMPT) provides steep dose gradients but is vulnerable to range uncertainties and respiratory motion, leading to in...

Non-destructive monitoring of root biomass in hydroponically grown leafy vegetables: comparison between machine learning-based RGB and hyperspectral imaging.

BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring re...

Identifying and timing patient outcomes in clinician notes using large language models.

BACKGROUND: Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To addres...

Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.

Volumetric-modulated arc therapy (VMAT) planning for locally advanced non-small cell lung cancer (NSCLC) is an iterative and planner-dependent process...

[Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection].

The traditional paradigm of pathology diagnosis faces challenges like data fragmentation and inefficiency in the era of big data and artificial intell...

DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insuf...

Applying machine learning to predict stunting in children under 5 years old based on water, sanitation and hygiene behaviors and infrastructure.

OBJECTIVE: Child stunting continues to pose a substantial global health challenge, requiring multifaceted strategies that combine conventional epidemi...

Evaluation of Otolaryngology Provider Bias in Portal Messaging.

BACKGROUND/OBJECTIVES: Patient messaging portals are widely used in clinical practice and are linked to improved patient outcomes, but they are also a...

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes.

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large ...