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Clinical Decision-Making

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Explanatory argument extraction of correct answers in resident medical exams.

Artificial intelligence in medicine
Developing technology to assist medical experts in their everyday decision-making is currently a hot topic in the field of Artificial Intelligence (AI). This is specially true within the framework of Evidence-Based Medicine (EBM), where the aim is to...

Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.

Scientific reports
Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decis...

AI for BPH Surgical Decision-Making: Cost Effectiveness and Outcomes.

Current urology reports
PURPOSE OF REVIEW: Benign prostatic hyperplasia (BPH) is prevalent in nearly 70% of men over the age of 60, leading to significant clinical challenges due to varying symptom presentations and treatment responses. The decision to undergo surgical inte...

The Promise of Artificial Intelligence in Peyronie's Disease.

Current urology reports
PURPOSE OF REVIEW: The application of artificial intelligence (AI) to enhance clinical decision-making in Peyronie's disease (PD) has generated significant interest. This review explores the current landscape of AI in PD evaluation.

The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients.

Journal of orthopaedic surgery and research
PURPOSE: The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical ...

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: The study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making.

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice.

Journal of cardiovascular medicine (Hagerstown, Md.)
Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in...

RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning.

BMC medical informatics and decision making
BACKGROUND: The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell car...

Should AI models be explainable to clinicians?

Critical care (London, England)
In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to impro...

"Dr. AI Will See You Now": How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?

Clinical orthopaedics and related research
BACKGROUND: Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 ...