AIMC Topic: Clinical Decision-Making

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Predicting Immunotherapy Response in Unresectable Hepatocellular Carcinoma: A Comparative Study of Large Language Models and Human Experts.

Journal of medical systems
Hepatocellular carcinoma (HCC) is an aggressive cancer with limited biomarkers for predicting immunotherapy response. Recent advancements in large language models (LLMs) like GPT-4, GPT-4o, and Gemini offer the potential for enhancing clinical decisi...

Enhancing clinical decision-making in closed pelvic fractures with machine learning models.

Biomolecules & biomedicine
Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML)...

Evaluating the novel role of ChatGPT-4 in addressing corneal ulcer queries: An AI-powered insight.

European journal of ophthalmology
PurposeChatGPT-4, a natural language processing-based AI model, is increasingly being applied in healthcare, facilitating education, research, and clinical decision-making support. This study explores ChatGPT-4's capability to deliver accurate and de...

Evaluation of the performance of large language models in clinical decision-making in endodontics.

BMC oral health
BACKGROUND: Artificial intelligence (AI) chatbots are excellent at generating language. The growing use of generative AI large language models (LLMs) in healthcare and dentistry, including endodontics, raises questions about their accuracy. The poten...

ChatGPT-4o outperforms gemini advanced in assisting multidisciplinary decision-making for advanced gastric cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND & AIMS: The treatment of advanced gastric cancer (GC) requires precise and comprehensive clinical decision-making. Artificial intelligence (AI) chatbots offer potential tools to enhance multidisciplinary team (MDT) discussions. This study ...

Evaluating the impact of explainable AI on clinicians' decision-making: A study on ICU length of stay prediction.

International journal of medical informatics
BACKGROUND: Explainable Artificial Intelligence (XAI) is increasingly vital in healthcare, where clinicians need to understand and trust AI-generated recommendations. However, the impact of AI model explanations on clinical decision-making remains in...

Advancing lung transplantation through machine learning and artificial intelligence.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome prediction, drug dosing, and the potential future uses and risks as the technology continues to evolve.

Protocol of the pilot study to test and evaluate the iCARE tool: a machine learning-based e-platform tool to make health prognoses and support decision-making for the care of older persons with complex chronic conditions.

BMJ open
INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challeng...

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...