AIMC Topic: Randomized Controlled Trials as Topic

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Effects of Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: An Umbrella Review of Systematic Reviews.

Stroke
BACKGROUND: Robotic rehabilitation, which provides a high-intensity, high-frequency therapy to improve neuroplasticity, is gaining traction. However, its effectiveness for upper extremity stroke rehabilitation remains uncertain. This study comprehens...

GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines.

BMJ open
OBJECTIVES: Adherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produced mixed results. This exploratory study aimed to determine how accurate a large language model gen...

Intelligent Robot Interventions for People With Dementia: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Journal of medical Internet research
BACKGROUND: The application of intelligent robots in therapy is becoming more and more important for people with dementia. More extensive research is still needed to evaluate its impact on behavioral and psychological dementia symptoms, as well as qu...

AI-driven evidence synthesis: data extraction of randomized controlled trials with large language models.

International journal of surgery (London, England)
The advancement of large language models (LLMs) presents promising opportunities to enhance evidence synthesis efficiency, particularly in data extraction processes, yet existing prompts for data extraction remain limited, focusing primarily on commo...

Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.

Journal of medical Internet research
BACKGROUND: The surge in artificial intelligence (AI) interventions in primary care trials lacks a study on reporting quality.

Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

Epilepsy research
INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large language models (LLMs), for synthesizing information in a simulated randomized clinical trial (RCT) for an anti-seizure medication, cenobamate, demon...

Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.

BMC medical research methodology
BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the trea...

Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial.

European journal of epidemiology
Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemiological communities, offering potential breakthroughs in the realm of precision medicine by enablin...