Ambient AI Documentation in Clinical Genetics: Perspectives on Implementation and Impact on Burnout

Objectives: To assess genetic counselors perspectives on ambient AI adoption and its impact on counselor burnout. Materials and Methods: We utilized a mixed methods approach, surveying burnout using the validated Stanford Professional Fulfilment Index (PFI) before and after ambient AI adoption and exploring adoption perspectives through semi-structured interviews. Results: 64% of participants (16/25) completed the pre-survey, with eleven completing post-surveys (69% response rate for completion of all three surveys). 14/25 participants completed interviews. Ambient AI use was associated with reduction in burnout after 90 days; respondents who reported using ambient AI (vs. non-use) had burnout scores 1.05 points lower, on average (p=0.008). Benefits of adoption included effective use with interpreters, memory aid, summarization of non-templated note sections (e.g. family/social history), and improved patient engagement. Challenges included template customization, variable accuracy, oversimplified medical language, and rapport disruption during consent. Ethical and regulatory considerations included data privacy, bias, awareness of training resources, and concerns about job displacement. Discussion: Ambient AI documentation can reduce documentation burden and burnout among genetic counselors. By evaluating both outcomes and real world implementation considerations, our study provides evidence to guide scalable integration of AI enabled documentation tools in clinical genomic medicine. Conclusion: Ambient AI can help support the sustainability of the clinical genetics workforce as genomic medicine initiatives are scaled across health systems. Addressing genetics-specific documentation needs while prioritizing patient trust, transparency, and provider oversight is essential for responsible ambient AI implementation.