Artificial Intelligence in Eating Disorder Treatment: A Qualitative Analysis of Clinical Opportunities, Barriers, and Ethical Considerations From Multi‐Disciplinary Focus Groups

Objective This study explored eating disorder and Artificial Intelligence (AI) professionals' perspectives on how AI might support eating disorder treatment. Successful implementation requires insight into implementation partners' perspectives. Method This study is an explorative qualitative analysis of two interdisciplinary focus groups (consisting of 22 eating disorder and AI professionals in total). Qualitative analysis with ATLAS.ti using a hybrid thematic analysis approach combined deductive coding with inductive theme development. The groups discussed (1) the opportunities and challenges—including ethical and safety considerations—of AI in eating disorder care, and (2) the types of evidence and evaluation frameworks required for adoption in practice. Results Themes were categorized into “opportunities,” “challenges,” “concerns,” “solutions,” and “evidence needed.” Opportunities focused on AI's potential to enhance efficiency, support treatment delivery and monitoring, and reduce human error. Challenges concerned barriers to adoption in clinical practice, responsibility, and explainability. Concerns included ethical and legal risks, also related to data sharing. Proposed solutions emphasized the need for human oversight, cross‐sector collaboration, and clinician training. With regard to evidence needed, participants mentioned safety and accuracy, and the need for scientific testing and validation. Discussion This study highlighted the potential and complexity of integrating AI into eating disorder care from the viewpoint of eating disorder and AI professionals. While there is value in AI in improving efficiency and clinical support, successful implementation requires addressing ethical concerns, legal uncertainty, and infrastructural barriers. Collaboration across disciplines, rigorous validation, and clinician involvement are essential to ensure that AI applications are safe, meaningful, and ethically sound.