# Modified Delphi

Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.

Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.

Using a Nominal Group Technique session (May 2025, n=6) and a two-round modified Delphi process (April–May 2026, expert panel n=13 across 12 academic medical centers, with 77% Round 1 and 100% Round 2 response rates), this study developed the first specialty-specific AI competency framework for a United States medical specialty, comprising 5 themes (Communicating about AI, Understanding appropriate use cases, Interacting with AI, AI risk management, Cognitive impacts of AI), 5 derived competencies (one-to-one theme-to-competency mapping endorsed by 12 of 13 panelists), 19 subthemes, and 10 retained clinical scenarios, with all themes, derived competencies, and individually rated subthemes meeting pre-specified consensus thresholds and 9 of 10 scenarios reaching consensus while 1 was retain

Using a Nominal Group Technique session (May 2025, n=6) and a two-round modified Delphi process (April–May 2026, expert panel n=13 across 12 academic medical centers, with 77% Round 1 and 100% Round 2 response rates), this study developed the first specialty-specific AI competency framework for a United States medical specialty, comprising 5 themes (Communicating about AI, Understanding appropriate use cases, Interacting with AI, AI risk management, Cognitive impacts of AI), 5 derived competencies (one-to-one theme-to-competency mapping endorsed by 12 of 13 panelists), 19 subthemes, and 10 retained clinical scenarios, with all themes, derived competencies, and individually rated subthemes meeting pre-specified consensus thresholds and 9 of 10 scenarios reaching consensus while 1 was retain