AI in Health

The improved next generation (gen-2B) of the CLNQ platform was just released.  CLNQ Gen-2B is an advanced AI-supported clinical decision-making platform that utilizes enhanced human phenotype ontologies and sophisticated biomedical knowledge bases. CLNQ Gen-2B can be used for education of health professionals, training of nurses, doctors, and clinicians, for health science research, for patient education, health policy decision-making, inter-professional collaborations, and translational sciences. CLNQ Gen-2B features include: • 61,540 pre-computed clinical embeddings (512D) for optimized similarity computation • Optimized tensor operations with WebGL acceleration • Memory-efficient processing with automatic cleanup • Intelligent filtering and context-aware analysis • Sub-second analysis for real-time clinical decision support CLNQ Gen-2B is designed, developed, deployed, and supported by UMSN AI in Health Initiative , SOCR , and GrayRain . 

On June 28, 2025, SOCR , UMSN AI in Health Initiative , and GrayRain released a revolutionary second-generation (Gen-2) Augmented Intelligence Agent (AIA) . This general AIA platform provides a powerful and domain-agnostic infrastructure to train AI agents for a wide range of task-specific operations. The two core AIA operations include training an AI model (using task-specific human knowledge bases) and testing the generative AI model (GAIM) for inference, prediction, agentic AI responses, and forecasting tasks.  

The UMSN AI in Health Initiative released a novel MedsDosage Training Academy webapp , which offers a powerful learning module coupled with interactive master Medication Dosage calculations. The  MedsDosage2 app covers a wide range of clinical settings, treatment conditions, and patient phenotypes. This interactive educational platform is designed for healthcare trainees and clinical professionals to learn, refresh, and improve medication safety and dosage competency.  

The VirtualHospital is an AI-enhanced hospital simulation platform that provides realistic health data management and analytics for clinical training, research, and decision-making without compromising patient privacy. It includes an integrated dashboard, data desensitization, simulation, and analysis tools, and provides hands-on Try-It-Now Demo functionality.  

CLNQ is an advanced (Gen-2) AI-supported clinical decision-making platform utilizing enhanced human phenotype ontologies and a sophisticated biomedical knowledgebase engine. The multiple generations of the  CLNQ app can be used for education of health professionals, training of nurses, doctors, and clinicians, for health science research, for patient education, health policy decision-making, inter-professional collaborations, and translational sciences. Key  CLNQ features include: • Clinical Intelligence Engine • HPO Ontology Integration • Statistical Treatment Modeling • Comprehensive Cost Analysis • Evidence-Based Recommendations CLNQ is designed, developed, deployed, and supported by UMSN AI in Heath Initiative , SOCR , and GrayRain . 

The UMSN AI in Health Initiative launched a new app for simulating synthetic Electronic Health Record data. The GR-EHR-Sim app is a comprehensive platform for simulating realistic electronic health records data with full control over clinical parameters and data characteristics. Key GR-EHR-Sim features include: Data Simulation: Configure and generate synthetic EHR data. Create customized data with control over sample size, clinical phenotypes, and more. Data Visualization: Explore data through interactive charts. Visualize your data with multiple types of quantitative summaries and SVG graphs. Data Analytics: Perform analysis on your data. Apply statistical methods and machine learning algorithms to gain insights. Reports: Generate comprehensive reports. Export and print reports including all your data, visualizations, and analyses.  

The UMSN AI in Health Initiative deployed NCLEXer , a new service offering a free and comprehensive platform for NCLEX exam preparation, designed to help nursing students succeed in their licensure examination with confidence. NCLEXer provides nursing students with a comprehensive, accessible, and effective platform for NCLEX preparation. We believe every nursing student deserves the tools and resources needed to succeed in their professional journey. NCLEXer includes: • Comprehensive question bank with detailed explanations • Questions covering all NCLEX categories • Customizable practice sessions • Progress tracking and performance analytics • Evidence-based content aligned with current standards NCLEXer features include: • Quality Content : All questions are carefully crafted and reviewed by nursing education experts • Focused Preparation : Practice questions target key areas tested on the NCLEX examination • Comprehensive Learning : Detailed explanations help you understand the...

The UMSN AI in Health Initiative released a new SOCR Statistical Power Analyzer (SPA) , a tool for calculating statistical power, sample size, effect size, and significance level. The key SPA advantage is the direct, holistic, and efficient exploration of concepts and relationships between a wide range of relevant parameters. The SPA provides a rigorous framework to help researchers determine the sample size needed to detect an effect of a given size with a specified level of confidence. The SPA provides full control over the 5 power-analysis components -- selection of a specific statistical test, sample size, effect size, significance level (α), and power (1-β). 

The UMSN AI in Health Initiative launched the Statistical Data Analyzer (SDA) , a powerful browser-based platform for comprehensive data analysis and visualization. Built with modern AI and Cloud technologies, SDA enables researchers, analysts, and students to perform sophisticated statistical analyses without the need for specialized software installation. Key SDA features include: Multi-Format Data Support : CSV, Excel, JSON, XML, FHIR, and more data formats supported A Wide Range of Statistical Analyses : Comprehensive descriptive statistics, correlation analysis, and hypothesis testing Interactive Visualizations : Dynamic charts, plots, and graphs with real-time data binding Report Generation : Professional reports with methodology, results, and conclusions Lightweight Browser-Based : No installation required - runs entirely in your web browser Data Privacy : All processing happens locally - your data never leaves your browser.  

The UMSN AI in Health Initiative released a new Interactive Learning Platform (ILP) , which revolutionizes health, nursing, and inter-professional education through AI and Cloud technologies bringing active learners and innovative educators together in an engaging and effective environment.   The ILP democratizes access to high-quality, interactive education by providing educators with powerful tools to create engaging learning experiences and students with platforms that make learning collaborative, fun, and effective. It connects learners and educators across the world, breaking down geographical barriers to quality education. The main ILP features include: Student-Centered : Every feature is designed with learners in mind, prioritizing engagement, accessibility, and educational outcomes. Privacy First : We maintain the highest standards of data protection and privacy, ensuring a safe learning environment for all. What Makes Us Different : Our platform combines cutting-edge tec...

The UMSN AI in Health Initiative released a new Nurse AI Trainer (NAIT) app , which empowers innovative healthcare with Artificial and Augmented Intelligence and provides comprehensive resources and training modules designed to equip healthcare professionals with the knowledge and skills to effectively integrate AI into clinical practice.  

The UMSN AI in Health Initiative piloted the novel Michigan Intelligent Teaching Assistant (MITA) , which represents a comprehensive (augmented intelligence) AI-powered teaching assistant platform for enhancing educational experiences and active learning. SWOT Analysis: Human TAs vs. MITAs Strengths 24/7 availability and scalability Consistency in explanations and grading Personalized learning support Cost-effectiveness (long-term) Data-driven insights for course improvement Weaknesses Lack of genuine empathy and human nuance Limitations with novel or abstract queries Potential for algorithmic bias High initial development costs Data security and privacy concerns Opportunities Democratized access to quality support Freeing human educators for higher-value tasks Development of innovative pedagogical approaches Global collaboration in education Enhanced accessibility features Threats Student over-reliance and reduced critical thinking Potential for academic dishonesty Job displacement f...

In May 2025, the University of Michigan School of Nursing (UMSN) launched an "AI in Health" Forum , a comprehensive initiative exploring the integration of artificial and augmented intelligence (AI) in nursing education, clinical practice, and healthcare research. This grassroots effort brings together faculty, students, and healthcare professionals to advance AI literacy and foster innovative applications across the nursing profession. This blog tracks the rapid progress of AI development and immersion in the UMSN community and the entire world .