Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules by Yunguo Yu, Cesar A. Gomez-Cabello, Svetlana Makarova, Yogesh Parte, Sahar Borna, Syed Ali Haider, Ariana Genovese, Srinivasagam Prabha, Antonio J. Forte

Subjects: Get full text

The Role of ChatGPT and AI Chatbots in Optimizing Antibiotic Therapy: A Comprehensive Narrative Review by Ninel Iacobus Antonie, Gina Gheorghe, Vlad Alexandru Ionescu, Loredana-Crista Tiucă, Camelia Cristina Diaconu

Subjects: Get full text

Empowering personalized oncology: evolution of digital support and visualization tools for molecular tumor boards by Cosima Strantz, Dominik Böhm, Thomas Ganslandt, Melanie Börries, Patrick Metzger, Thomas Pauli, Andreas Blaumeiser, Alexander Scheiter, Ian-Christopher Jung, Jan Christoph, Iryna Manuilova, Konstantin Strauch, Arsenij Ustjanzew, Niklas Reimer, Hauke Busch, Philipp Unberath

Subjects: Get full text

Examining the relationships between patients’ multimorbidity trajectories and prognostic outcomes after the initial hip fracture by Jessica Qiuhua Sheng, Da Xu, Paul Jen-Hwa Hu, Liang Li, Ting-Shuo Huang

Subjects: Get full text

Benchmarking LLM chatbots’ oncological knowledge with the Turkish Society of Medical Oncology’s annual board examination questions by Efe Cem Erdat, Engin Eren Kavak

Subjects: Get full text

Efficacy and safety of an algorithm using C-reactive protein to guide antibiotic therapy applied through a digital clinical decision support system: a study protocol for a randomised controlled clinical trial by Vandack Nobre, Pedro R Povoa, Vitoria Moura Leite Rabelo Rezende, Isabela Nascimento Borges, Cecilia Gomez Ravetti, Renan Pedra De Souza, Paula Frizera Vassalo, Ana Clara de Paula Caldas, Felipe Rodrigues Gatto, Getulio Hideyoshi Okamura, Raquel Lopes de Brito Lacerda

“…It will be applied through a mobile application as a digital clinical decision support system. The primary goal will be to assess the algorithm’s effectiveness in reducing treatment duration compared with standard care based on current guidelines, while ensuring patient safety by monitoring the occurrence of adverse events.Ethics and dissemination Only patients who agree to participate in the study after reading the informed consent form will be included. …”
Get full text

Acceptance of artificial intelligence clinical assistant decision support system to prevent and control venous thromboembolism among healthcare workers: an extend Unified Theory of... by Jingxian Wang, Yun Zhou, Yun Zhou, Kai Tan, Zhigang Yu, You Li

Subjects: “…clinical decision support system…”
Get full text

Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation by Veena K.M., Veena Mayya, Rashmi Naveen Raj, Sulatha V. Bhandary, Uma Kulkarni

Subjects: Get full text

Advancements in Predictive Analytics: Machine Learning Approaches to Estimating Length of Stay and Mortality in Sepsis by Houssem Ben Khalfallah, Mariem Jelassi, Jacques Demongeot, Narjès Bellamine Ben Saoud

Subjects: Get full text

Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. by Fiona Leonard, Dympna O'Sullivan, John Gilligan, Nicola O'Shea, Michael J Barrett

“…<h4>Introduction</h4>Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. …”
Get full text

Large Language Models lack essential metacognition for reliable medical reasoning by Maxime Griot, Coralie Hemptinne, Jean Vanderdonckt, Demet Yuksel

“…Abstract Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. …”
Get full text

Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare by Jessica D. Workum, Jessica D. Workum, Jessica D. Workum, Davy van de Sande, Davy van de Sande, Diederik Gommers, Diederik Gommers, Michel E. van Genderen, Michel E. van Genderen

“…Large Language Models (LLMs) offer considerable potential to enhance various aspects of healthcare, from aiding with administrative tasks to clinical decision support. However, despite the growing use of LLMs in healthcare, a critical gap persists in clear, actionable guidelines available to healthcare organizations and providers to ensure their responsible and safe implementation. …”
Get full text

Exploring information technology utilization and needs in community pharmacies: a cross-sectional survey in Shanghai, China by Qin Li, Zhao Jin, Yun Liao, Huihua Dai, Jie Meng, Ling Li

“…However, in many countries, including China, challenges such as limited access to real-time patient data, fragmented communication systems, and underdeveloped clinical decision-support tools hinder the effectiveness of pharmacy services. …”
Get full text

Pain Assessment in a Geriatric Psychiatry Program by Paul Stolee, Loretta M Hillier, Jacquelin Esbaugh, Nancy Bol, Laurie McKellar, Nicole Gauthier, Maggie C Gibson

Get full text

Disseminated Chickenpox Following Live Varicella Vaccination in a Crohn’s Disease Patient on Combination Immunosuppression by Quintin Solano, Sarah Uttal, Peter D. R. Higgins, Jeffrey A. Berinstein

“…To improve safety, healthcare facilities should develop protocols that use electronic medical records enhanced with clinical decision support systems to identify and protect immunocompromised patients from inappropriate live vaccinations.…”
Get full text

Integrating artificial intelligence into healthcare systems: opportunities and challenges by Bongs Lainjo

“…Research indicates that AI has transformed healthcare innovation, particularly in clinical decision support and personalized treatment. However, the adoption of AI is not without challenges. …”
Get full text

How data science and AI-based technologies impact genomics by Jing Lin, Kee Yuan Ngiam

“…The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. …”
Get full text

Advances and utility of digital twins in critical care and acute care medicine: a narrative review by Gabriele A. Halpern, Marko Nemet, Diksha M. Gowda, Oguz Kilickaya, Amos Lal

“…The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. …”
Get full text

Effectiveness of Electronic Quality Improvement Activities to Reduce Cardiovascular Disease Risk in People With Chronic Kidney Disease in General Practice: Cluster Randomized Trial... by Jo-Anne Manski-Nankervis, Barbara Hunter, Natalie Lumsden, Adrian Laughlin, Rita McMorrow, Douglas Boyle, Patty Chondros, Shilpanjali Jesudason, Jan Radford, Megan Prictor, Jon Emery, Paul Amores, An Tran-Duy, Craig Nelson

“… BackgroundFuture Health Today (FHT) is a program integrated with electronic medical record (EMR) systems in general practice and comprises (1) a practice dashboard to identify people at risk of, or with, chronic disease who may benefit from intervention; (2) active clinical decision support (CDS) at the point of care; and (3) quality improvement activities. …”
Get full text

An Automated Clinical Laboratory Decision Support System for Test Utilization, Medical Necessity Verification, and Payment Processing by Safedin Beqaj, Rojeet Shrestha, Tim Hamill

“…This viewpoint lays out the potential use of an automated laboratory clinical decision support system that helps providers order the right test for the right disease and documents the right reason or medical necessity to pay for the testing.…”
Get full text