This workshop is provided by Fairfield University and is supported by the Business Higher Education Forum (BHEF), the New England Board of Higher Education (NEBHE), and Stamford Health. Those who completed the workshop have foundational knowledge in 1. Introduction to Artificial Intelligence (AI) - Definition of AI: Machines performing tasks that normally require human intelligence (e.g., pattern recognition, prediction, decision support, language understanding). - What AI is not: It is not a replacement for human judgment, empathy, or critical thinking. - Brief history of AI in healthcare. - Key differences: AI vs. traditional software, automation, and robotics. 2. Core Subfields and Technologies in AI - Machine Learning (ML): Systems that learn patterns from data. Nursing examples: sepsis prediction, fall risk, patient deterioration alerts. - Natural Language Processing (NLP): AI that understands and generates human language. Uses: charting summaries, discharge instructions, chatbots, note generation. - Computer Vision: AI that analyzes images and videos. Uses: wound assessment, radiology support, patient monitoring. - Generative AI (GenAI): Tools like ChatGPT that create text, plans, or images. Focus: basic prompt writing and healthcare applications. 3. Data Fundamentals in AI and Healthcare - Types of data: Structured (vital signs, labs) vs. unstructured (nursing notes). - EHRs and big data as fuel for AI. - How AI uses data: Learns patterns to make predictions. - Key concepts: Data quality, diversity, bias, and privacy (HIPAA). 4. How AI Works (High-Level) - Algorithms vs. learning from data (rules-based vs. pattern-based). - Basic process: Training, testing, and validation of models. - Black box vs. Explainable AI (why explanations matter for trust and safety). - Major limitations: AI has no common sense, empathy, or true understanding — it predicts based on past patterns and requires human oversight. 5. Integrated and Applied AI for Nurses - Foundational AI literacy. - Data stewardship in documentation. - Clinical decision support and judgment integration. - Human - AI interactions. - Ethical and legal responsibilities. - Quality and safety oversight. - AI leadership and advocacy. - AI-enabled innovation and transformation.