Scoring Key: 5=Strongly Agree 4= Somewhat agree 3=Neither agree nor disagree 2= Somewhat disagree 1=Strongly disagree NA=Not applicable
The skills learned in this CME course enhanced my professional competence.
The skills learned in this CME course will be applied in the treatment of my patients
Scoring Key: 5=Strongly satisfied 4= Somewhat satisfied 3=Neither satisfied nor dissatisfied 2= Somewhat dissatisfied 1=Strongly dissatisfied
Recognize use cases for generative artificial intelligence (AI) in medicine that could augment human clinicians, such as automated medical coding or care pathway optimization.
Assess public health burden and financial impact of diagnostic errors and misdiagnosis-related harms.
Discuss practical strategies to develop responsible and ethical generative AI solutions in healthcare.
List advantages that generative models may provide over other AI techniques for addressing complex challenges in areas like medical imaging, precision oncology insights and patient-provider conversations.
Apply techniques to help detect and reduce fabricated "hallucinated" content in generative AI model outputs in medicine, including human-in-the-loop validation and uncertainty-aware model architectures.
Identify potential pitfalls of applying AI for clinical diagnosis without adequate guardrails.
Implement prerequisites and systems of care essential to deploying AI to achieve diagnostic excellence.
Identify current and future AI projects for consideration at Baptist Health.
Implement strategies to optimize collaboration, communication and coordination among residents, fellows, students, physicians and healthcare professionals engaging in the acquisition of knowledge.