Understanding the AI Wave: Improving Diagnosis and Beyond

Course overview

Course Description: This course provides behavioral health and clinical professionals with foundational knowledge about artificial intelligence (AI) as it is applied across the healthcare diagnostic process. Drawing from an issue brief prepared for the Agency for Healthcare Research and Quality (AHRQ), the course introduces the three primary types of AI used in healthcare rule-based systems, traditional machine learning, and deep learning and examines how each functions within clinical settings. Participants will explore current applications of deep learning, including computer vision, biomedical pattern recognition, and natural language processing, and will examine how these technologies are already embedded in every stage of the diagnostic process. The course addresses key limitations of AI systems, including bias in training data, hallucinations, and lack of transparency, as well as cognitive biases such as automation complacency, automation bias, and confirmation bias that influence how clinicians interact with AI-generated outputs. Guidance for hospital leaders, clinicians, and patients on the safe and responsible adoption of AI is also examined. This course on understanding the AI wave in healthcare is designed for social workers, professional counselors, psychologists, nurses, and substance abuse counselors who do clinical work. This course is appropriate for intermediate and advanced level practitioners who wish to develop or increase their knowledge of how artificial intelligence is transforming healthcare diagnosis, documentation, and clinical decision-making. Authors: Biro, J., & Salvador, D Learning Objectives: This course will provide a professional with detailed information about the AI wave and its foundational role in improving diagnosis and healthcare delivery. Specifically, the practitioner will: 1. Differentiate among the three primary types of artificial intelligence used in healthcare and describe how deep learning applications are currently embedded in clinical diagnostic processes. 2. Identify key limitations of AI systems in healthcare and explain how cognitive biases can compromise a clinician's ability to critically evaluate AI-generated outputs. 3. Apply evidence-informed guidance for the safe and responsible adoption of AI in clinical settings. Citation: Biro, J., & Salvador, D. (2025). Understanding the AI wave: Foundational knowledge for improving diagnosis and beyond (AHRQ Publication No. 27-0060). Agency for Healthcare Research and Quality.