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Navigating Artificial Intelligence in Scientific Manuscript Writing: Tips and Traps.
Indian J Radiol Imaging
January 2025
Department of Radiodiagnosis and Imaging, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
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.
View Article and Find Full Text PDFIntegrating Artificial Intelligence in Medical Writing: Balancing Technological Innovation and Human Expertise, with Practical Applications in Lower Extremity Wounds Care.
Int J Low Extrem Wounds
January 2025
Environmental-Occupational Health Sciences and Non Communicable Diseases Research Centre, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand.
Artificial Intelligence (AI) is revolutionizing medical writing by enhancing the efficiency and precision of healthcare communication and health research. This review explores the transformative integration of AI in medical writing, highlighting its dual role of enhancing efficiency while maintaining the crucial elements of human expertise. AI technologies, including natural language processing and AI-driven literature review tools, have significantly advanced, facilitating rapid draft generation, literature summarization, and consistency in medical documentation.
View Article and Find Full Text PDFArtificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.
J Prev Alzheimers Dis
January 2025
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.
Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.
The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review.
Curr Res Transl Med
January 2025
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.
View Article and Find Full Text PDFUsing routine primary care data in research: (in)efficient case studies and perspectives from the Asthma UK Centre for Applied Research.
BMJ Health Care Inform
January 2025
College of Medicine and Veterinary Medic, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK.
Aim: We aimed to identify enablers and barriers of using primary care routine data for healthcare research, to formulate recommendations for improving efficiency in knowledge discovery.
Background: Data recorded routinely in primary care can be used for estimating the impact of interventions provided within routine care for all people who are clinically eligible. Despite official promotion of 'efficient trial designs', anecdotally researchers in the Asthma UK Centre for Applied Research (AUKCAR) have encountered multiple barriers to accessing and using routine data.
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