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Clinical Decision-Making

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Causal machine learning for predicting treatment outcomes.

Nature medicine
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating in...

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

Surgical endoscopy
BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surg...

How artificial intelligence could transform emergency care.

The American journal of emergency medicine
Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare appl...

Can GPT-4 revolutionize otolaryngology? Navigating opportunities and ethical considerations.

American journal of otolaryngology
Otolaryngologists can enhance workflow efficiency, provide better patient care, and advance medical research and education by integrating artificial intelligence (AI) into their practices. GPT-4 technology is a revolutionary and contemporary example ...

Harnessing artificial intelligence for prostate cancer management.

Cell reports. Medicine
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging e...

Effects of explainable artificial intelligence in neurology decision support.

Annals of clinical and translational neurology
OBJECTIVE: Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how t...

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Thrombosis and haemostasis
BACKGROUND:  Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations....

Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.

Annals of vascular surgery
Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various appli...

Algor-ethics: charting the ethical path for AI in critical care.

Journal of clinical monitoring and computing
The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particula...

Physicians, know thy patient.

The journal of the Royal College of Physicians of Edinburgh
Person-centered care is presently the standard healthcare model, which emphases shared clinical decision-making, patient autonomy and empowerment. However, many aspects of the modern-day clinical practice such as the increased reliance on medical tec...