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Clinical Decision-Making

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Comparative evaluation of artificial intelligence models GPT-4 and GPT-3.5 in clinical decision-making in sports surgery and physiotherapy: a cross-sectional study.

BMC medical informatics and decision making
BACKGROUND: The integration of artificial intelligence (AI) in healthcare has rapidly expanded, particularly in clinical decision-making. Large language models (LLMs) such as GPT-4 and GPT-3.5 have shown potential in various medical applications, inc...

Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice.

Yearbook of medical informatics
OBJECTIVES: Despite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decis...

The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians.

Journal of primary care & community health
BACKGROUND: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to...

Evidence-based artificial intelligence: Implementing retrieval-augmented generation models to enhance clinical decision support in plastic surgery.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
The rapid advancement of large language models (LLMs) has generated significant enthusiasm within healthcare, especially in supporting clinical decision-making and patient management. However, inherent limitations including hallucinations, outdated c...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

Journal of Korean medical science
This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively...

AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study.

Scientific reports
Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this stu...

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individ...

Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.

World journal of gastroenterology
BACKGROUND: Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage. Endoscopy is the gold standard for EGV detection but it is invasive, costly and carries ...

The Role of ChatGPT in osteoporosis management: a comparative analysis with clinical expertise.

Archives of osteoporosis
UNLABELLED: This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% diagnostic accuracy and significantly faster response times compared to clinicians. The findings highlight the potential for artificial intelligence (A...

MIO: An ontology for annotating and integrating medical knowledge in myocardial infarction to enhance clinical decision making.

Computers in biology and medicine
As biotechnology and computer science continue to advance, there's a growing amount of biomedical data worldwide. However, standardizing and consolidating these data remains challenging, making analysis and comprehension more difficult. To enhance re...