Staffing & Scheduling

Tumor-specific draining lymph node CD8 T cells orchestrate an anti-tumor response to neoadjuvant PD-1 immune checkpoint blockade.

Elucidating the anti-tumor role of tumor-draining lymph nodes (tdLNs) in patients could offer critical mechanistic insight and shift therapeutic strategies from a tumor-centric approach to one that considers tumor-immune system interplay. Our study characterizes benign tdLNs T cell anti-tumor responses beyond initial T cell priming in patients with resectable non-small cell lung cancer. We further...

Advancing Musculoskeletal Care Using AI and Digital Health Applications: A Review of Commercial Solutions.

Artificial intelligence (AI) and digital health (DH) solutions are reshaping musculoskeletal (MSK) care across diagnostics, treatment planning, workflow optimization, and administrative burden reduction. AI-enabled triage systems enhance patient flow efficiency, while automated scheduling, symptom checkers, and AI-powered virtual assistants streamline pre-visit interactions. In MSK radiographic di...

Personality and job performance: A review of trait models and recent trends.

Personality traits are among the strongest non-cognitive predictors of job performance, but many trait models exist that are used to predict different...

ASight: Fine-tuning Auto-Scheduling Optimizations for Model Deployment via Visual Analytics.

Upon completing the design and training phases, deploying a deep learning model to specific hardware becomes necessary prior to its implementation in ...

Bioprocess modeling based on ASM1-SMP model: hybrid modeling approach integrating projection to latent structures (PLS) and artificial neural networks (ANN).

This work presents a new activated sludge model based on ASM1 and soluble microbial product (SMP) kinetics designed to better control fouling and to f...

A Review of Software in Clinical Trials: FDA Regulatory Frameworks and Addressing Challenges.

An essential tool for assessing the efficacy and safety of novel therapies and interventions is the clinical trial. They are crucial for understanding...

Comparison of quality, empathy and readability of physician responses versus chatbot responses to common cerebrovascular neurosurgical questions on a social media platform.

BACKGROUND: Social media platforms are utilized by patients prior to scheduling formal consultations and also serve as a means of pursuing second opin...

A Reinforcement Learning Control Framework Based on Scalable Graph Transformer for Large-Scale Fuzzy Job Shop Scheduling Problems.

The job shop scheduling problem (JSSP) is a classic NP-hard problem. This article focuses on a realistic variant of the JSSP incorporating fuzzy proce...

Artificial Intelligence in Value-Based Health Care.

Artificial intelligence (AI) presents new opportunities to advance value-based healthcare in orthopedic surgery through 3 potential mechanisms: agency...

Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disease characterized by demyelinating lesions in the central nervous system. Cross-sectional measur...

Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum.

: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primar...

Evaluating artificial intelligence bias in nephrology: the role of diversity, equity, and inclusion in AI-driven decision-making and ethical regulation.

BACKGROUND: The integration of Artificial Intelligence (AI) in nephrology has raised concerns regarding bias, fairness, and ethical decision-making, p...

Optimizing MRI sequence classification performance: insights from domain shift analysis.

BACKGROUND: MRI sequence classification becomes challenging in multicenter studies due to variability in imaging protocols, leading to unreliable meta...

AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis.

In the context of smart homes, efficiently managing temperature control while optimizing energy consumption and ensuring data security remains a signi...

Commercial Products Using Generative Artificial Intelligence Include Ambient Scribes, Automated Documentation and Scheduling, Revenue Cycle Management, Patient Engagement and Education, and Prior Authorization Platforms.

The integration of artificial intelligence into clinical practice is rapidly transforming health care workflows. At the forefront are large language m...

Shining the Beam on the Next Generation: A Program Evaluation of a National Workshop Focusing on Medical Student Engagement in Radiation Oncology.

The rising cancer incidence has increased demand for radiation oncologists, surpassing current staffing expansion estimates. Enhancing radiation oncol...

Incorporating Pre-Training Data Matters in Unsupervised Domain Adaptation.

In deep learning, initializing models with pre-trained weights has become the de facto practice for various downstream tasks. Many unsupervised domain...

Ambient technology in epilepsy clinical practice.

The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance ...

A demand-centered scheduling framework for shared supercomputing resources: modeling, metrics, and case insights.

The exponential growth of artificial intelligence and data-intensive applications has led to a significant surge in demand for supercomputing resource...

Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models.

The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chem...