Single-site review means protection and efficiency.
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Background: Multidisciplinary teams (MDTs) are essential for cancer care but are resource-intensive. Decision-making processes within MDTs, while critical, contribute to increased healthcare costs due to the need for specialist time and coordination. The recent emergence of large language models (LLMs) offers the potential to improve the efficiency and accuracy of clinical decision-making processes, potentially reducing costs associated with traditional MDT models.
View Article and Find Full Text PDFNavigating Artificial Intelligence in Scientific Manuscript Writing: Tips and Traps.
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It is being increasingly recognized that the strategic use of artificial intelligence (AI) can catalyze the process of manuscript writing. However, it is imperative that we recognize the hidden biases, pitfalls, and disadvantages of relying solely on AI, such as accuracy concerns and the potential erosion of nuanced human insight. With an emphasis on crafting effective prompts and inputs, this article reveals how to navigate the labyrinth of AI capabilities to create a good-quality manuscript.
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Environmental-Occupational Health Sciences and Non Communicable Diseases Research Centre, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand.
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View Article and Find Full Text PDFArtificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.
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Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, United States.
Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.
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Curr Res Transl Med
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Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
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