AIMC Topic: Clinical Decision-Making

Clear Filters Showing 11 to 20 of 686 articles

Leveraging ChatGPT and explainable AI for enhancing clinical decision support.

Scientific reports
Large language models (LLMs) excel in many natural language processing tasks. However, their direct application to tabular, domain-specific clinical data remains challenging, as they lack innate mechanisms for reasoning over structured numerical feat...

Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study.

JMIR mental health
BACKGROUND: Trauma exposure is highly prevalent and associated with various health issues. However, health care professionals can exhibit trauma-related diagnostic overshadowing bias, leading to misdiagnosis and inadequate treatment of trauma-exposed...

AI in primary care - a general practitioner's bucket list.

The European journal of general practice
While the development and use of Artificial Intelligence (AI) in health care have literally exploded in recent years, general practitioners (GPs) continue to struggle with a fragmented health care system and complex patients with multiple conditions ...

Comparison of clinical hysterectomy indications with ai-based recommendations: a prospective study.

Scientific reports
This prospective study evaluated ChatGPT-4 as a decision-support tool by comparing its treatment recommendations with clinical decisions for 87 women (aged 40-65 years) scheduled for hysterectomy. Demographic, clinical, and ultrasonographic data were...

Medicine Beyond Machines: Viewpoint on the Art of Thinking in the Age of AI.

JMIR formative research
The widespread adoption of large language models is increasingly shaping clinical decision-making by altering how physicians engage with data and reasoning. While these tools enhance diagnostic capacity, streamline workflows, and support learning, th...

Fostering trust and interpretability: integrating explainable AI (XAI) with machine learning for enhanced disease prediction and decision transparency.

Diagnostic pathology
Medical healthcare has advanced substantially due to advancements in Artificial Intelligence (AI) techniques for early disease detection alongside support for clinical decisions. However, a gap exists in widespread adoption of results of these algori...

Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.

Critical care explorations
OBJECTIVES: RBC transfusion in anemic patients with sustainable tolerance may cause harm, emphasizing the need for reliable metrics that quantify adequacy (oxygen delivery ≥ demand) and sustainability (oxygen delivery remains adequate without transfu...

Artificial intelligence in the prescription of acute medical treatments in primary healthcare - comparison of the performance of family physicians and ChatGPT.

BMC primary care
INTRODUCTION: Artificial intelligence (AI) is increasingly being recognized as a transformative force in healthcare, showing significant promise in supporting healthcare professionals. AI has many applications in healthcare, including providing real-...

Large Language Models' Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) are increasing in sophistication and are being integrated into many disciplines. The potential for LLMs to augment clinical decision-making is an evolving area of research.

Large Language Models in Neurology Treatment Decision-Making: a Scoping Review.

Journal of medical systems
This scoping review evaluates the expanding role of large language models (LLMs) in neurology, an area drawing growing interest of researchers and clinicians alike. A substantial existing body of literature supports the efficacy of LLMs for diagnosti...