AI Medical Compendium Journal:
The International journal of eating disorders

Showing 1 to 10 of 10 articles

From Skepticism to Support: Addressing Clinician and Patient Concerns About AI in Eating Disorder Care.

The International journal of eating disorders
While Linardon and colleagues reveal cautious attitudes toward artificial intelligence (AI) in eating disorder care, our recent empirical evidence suggests that specialized, expert-developed AI interventions can effectively support individuals at ris...

Perceived Barriers and Facilitators of Use of Artificial Intelligence in Eating Disorder Care: A Commentary on Linardon et al. (2025).

The International journal of eating disorders
Artificial intelligence (AI) has the potential to revolutionize mental health care, including for eating disorders, but there are still a number of concerns focused on ethics, governance, and regulation. As the authors found in their preliminary surv...

Using Artificial Intelligence to Advance Eating Disorder Research, Treatment and Practice.

The International journal of eating disorders
Artificial intelligence (AI) has the potential to revolutionize eating disorder research, treatment, and practice by assisting with complex problems such as predicting illness prognosis, supporting diagnostic decisions, tailoring treatment plans, and...

Current Practices and Perspectives of Artificial Intelligence in the Clinical Management of Eating Disorders: Insights From Clinicians and Community Participants.

The International journal of eating disorders
OBJECTIVE: Artificial intelligence (AI) could revolutionize the delivery of mental health care, helping to streamline clinician workflows and assist with diagnostic and treatment decisions. Yet, before AI can be integrated into practice, it is necess...

Weight gained during treatment predicts 6-month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning.

The International journal of eating disorders
OBJECTIVE: This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program.

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders.

The International journal of eating disorders
OBJECTIVE: To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research.

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.

The International journal of eating disorders
OBJECTIVE: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) te...

Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach.

The International journal of eating disorders
OBJECTIVE: Patterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to ...

Interactions between different eating patterns on recurrent binge-eating behavior: A machine learning approach.

The International journal of eating disorders
OBJECTIVE: Previous research has shown that certain eating patterns (rigid restraint, flexible restraint, intuitive eating) are differentially related to binge eating. However, despite the distinctiveness of these eating patterns, evidence suggests t...

Using person-specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones.

The International journal of eating disorders
OBJECTIVE: Emotional eating has been linked to ovarian hormone functioning, but no studies to-date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions o...