AI Medical Compendium Topic

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Clinical Decision-Making

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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 ...

Is artificial intelligence for medical professionals serving the patients?  : Protocol for a systematic review on patient-relevant benefits and harms of algorithmic decision-making.

Systematic reviews
BACKGROUND: Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superio...

Five steps in performing machine learning for binary outcomes.

The Journal of thoracic and cardiovascular surgery
BACKGROUND: The use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and...

Advances in critical care nephrology through artificial intelligence.

Current opinion in critical care
PURPOSE OF REVIEW: This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology.