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. …”
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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. …”
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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. …”
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A cluster randomized trial assessing the effect of a digital health algorithm on quality of care in Tanzania (DYNAMIC study). by Rainer Tan, Godfrey Kavishe, Alexandra V Kulinkina, Sabine Renggli, Lameck B Luwanda, Chacha Mangu, Geofrey Ashery, Margaret Jorram, Ibrahim Evans Mtebene, Peter Agrea, Humphrey Mhagama, Kristina Keitel, Marie-Annick Le Pogam, Nyanda Ntinginya, Honorati Masanja, Valérie D'Acremont

“…Digital clinical decision support tools have contributed to improved quality of care at primary care level health facilities. …”
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Pain Assessment in a Geriatric Psychiatry Program by Paul Stolee, Loretta M Hillier, Jacquelin Esbaugh, Nancy Bol, Laurie McKellar, Nicole Gauthier, Maggie C Gibson

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Generative artificial intelligence in graduate medical education by Ravi Janumpally, Suparna Nanua, Andy Ngo, Kenneth Youens

“…We review the existing literature and provide commentary on how GenAI could impact GME, including five key areas of opportunity: electronic health record (EHR) workload reduction, clinical simulation, individualized education, research and analytics support, and clinical decision support. We then discuss significant risks, including inaccuracy and overreliance on AI-generated content, challenges to authenticity and academic integrity, potential biases in AI outputs, and privacy concerns. …”
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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.…”
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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. …”
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Artificial intelligence in rheumatology research: what is it good for? by Diego Benavent, José Miguel Sequí-Sabater

“…In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support. This review explores the current applications and future potential of both discriminative and generative AI in rheumatology. …”
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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. …”
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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. …”
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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. …”
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Leveraging Cloud Technology for Personalized Multiple Sclerosis Care: A Comprehensive Data Management and Visualization Approach by Jose Manuel PINILLOS RUBIO, Minerva VIGUERA MORENO

“…Utilizing REDCap for data collection, we incorporated clinical outcomes and PROMs from 300 MS patients over 18 months, supporting a machine learning (ML) based clinical decision support system. Our cloud architecture, featuring segregated data handling and enhanced security protocols using AWS, ensures robust data integrity and confidentiality. …”
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Research on upper limb rehabilitation assessment model based on belief rule base by Dawei Jiang, Zixu Zhao, Lijun Wang, Chao Zhang, Meixuan He, Tiejun Ji

“…This work has laid both a theoretical and practical groundwork for developing a clinical decision support system based on the BRB for upper limb rehabilitation evaluations.…”
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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.…”
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The present and future of digital health, digital medicine, and digital therapeutics for allergic diseases by He Zhang, Yang Cao, Haibo Jiang, Qilin Zhou, Qintai Yang, Lei Cheng

“…It highlights key advancements, including telehealth, mobile health (mHealth), artificial intelligence, clinical decision support systems (CDSS), and digital biomarkers, with a focus on their relevance to allergic disease management. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. …”
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The integration of AI in nursing: addressing current applications, challenges, and future directions by Qiuying Wei, Songcheng Pan, Songcheng Pan, Xiaoyu Liu, Mei Hong, Chunying Nong, Weiqi Zhang

“…A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. …”
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An institutional framework to support ethical fair and equitable artificial intelligence augmented care by Steven Dykstra, Matthew MacDonald, Rhys Beaudry, Dina Labib, Melanie King, Yuanchao Feng, Jacqueline Flewitt, Jeff Bakal, Bing Lee, Stafford Dean, Marina Gavrilova, Paul W. M. Fedak, James A. White

“…Abstract Coordinated access to multi-domain health data can facilitate the development and implementation of artificial intelligence-augmented clinical decision support (AI-CDS). However, scalable institutional frameworks supporting these activities are lacking. …”
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A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit by Edwin F. Juarez, Bennet Peterson, Erica Sanford Kobayashi, Sheldon Gilmer, Laura E. Tobin, Brandan Schultz, Jerica Lenberg, Jeanne Carroll, Shiyu Bai-Tong, Nathaly M. Sweeney, Curtis Beebe, Lawrence Stewart, Lauren Olsen, Julie Reinke, Elizabeth A. Kiernan, Rebecca Reimers, Kristen Wigby, Chris Tackaberry, Mark Yandell, Charlotte Hobbs, Matthew N. Bainbridge

“…Abstract The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. …”
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