AIMC Topic: Artificial Intelligence

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The Data Artifacts Glossary: a community-based repository for bias on health datasets.

Journal of biomedical science
BACKGROUND: The deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fa...

Inspired by the growth behavior of plants: biomimetic soft robots that just meet the requirements of use.

Bioinspiration & biomimetics
Soft robots are usually manufactured using the pouring method and can only be configured with a fixed execution area, which often faces the problem of insufficient or wasteful performance in real-world applications, and cannot be reused for other tas...

Evaluation of the potential value of artificial intelligence (AI) in public health using fluoride intake as the example.

Ecotoxicology and environmental safety
AIM: We aimed to test whether and how ChatGPT understood the epidemiological problems related to fluoride intake and whether ChatGPT could produce novel and feasible hypotheses to tackle the challenges in the research for the disorders caused by a de...

Green entrepreneurial leadership and AI-driven green process innovation: Advancing environmental sustainability in the Traditional Chinese Medicine industry.

Journal of environmental management
Despite much focus has been given on the relationship between leadership, innovation and firm performance, the impact of green entrepreneurial leadership (GEL) and Artificial Intelligence - driven green process innovation (AI-driven GPI) on firm perf...

Development of an artificial intelligence-based measure of therapists' skills: A multimodal proof of concept.

Psychotherapy (Chicago, Ill.)
The facilitative interpersonal skills (FIS) task is a performance-based task designed to assess clinicians' capacity for facilitating a collaborative relationship. Performance on FIS is a robust clinician-level predictor of treatment outcomes. Howeve...

Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis.

Current problems in cardiology
INTRODUCTION: AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction...

Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings.

Population health management
In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities ex...

Generative AI chatbots for reliable cancer information: Evaluating web-search, multilingual, and reference capabilities of emerging large language models.

European journal of cancer (Oxford, England : 1990)
Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible L...

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).

Clinica chimica acta; international journal of clinical chemistry
The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision...

Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study.

The Lancet. Digital health
BACKGROUND: Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed.