How Does AI Support Eating Disorder Treatment?

Author: Rasit Dinc

Introduction

Eating disorders (EDs) are complex and serious psychiatric conditions that significantly impact physical and psychological health. They are associated with low rates of early detection and high relapse rates, highlighting the need for improved diagnostic and therapeutic approaches [1]. In recent years, artificial intelligence (AI) has emerged as a promising tool to revolutionize the field of eating disorder research, treatment, and practice. AI offers the potential to address some of the most challenging aspects of ED care, from early identification to personalized treatment and ongoing support [2]. This article will explore the various ways in which AI is being utilized to support eating disorder treatment, the challenges and ethical considerations involved, and the future directions of this rapidly evolving field.

How AI is Used in Eating Disorder Treatment

AI and machine learning (ML) are being applied across the spectrum of eating disorder care, demonstrating potential in several key areas.

Early Detection and Diagnosis

Early detection of EDs is crucial for improving treatment outcomes, yet it remains a significant challenge. AI-powered models can analyze large datasets to identify individuals at risk for developing an eating disorder. For instance, a study on adolescents demonstrated that machine learning models could effectively predict the presence of EDs with a high degree of accuracy, using predictors such as gender, emotional symptoms, peer relationship problems, stress levels, and body dissatisfaction [3]. These models can be used to support clinical risk assessment and facilitate low-risk preventive interventions, enabling healthcare professionals to intervene earlier and more effectively.

Personalized Treatment Plans

AI has the potential to move beyond a one-size-fits-all approach to treatment by enabling the development of personalized care plans. By analyzing individual patient data, including genetic, psychological, and behavioral factors, AI algorithms can help clinicians tailor interventions to meet the specific needs of each patient. This can involve predicting treatment response, identifying the most effective therapeutic strategies, and adjusting treatment plans in real-time based on patient progress. The ability to personalize treatment in this way holds the promise of improving recovery rates and reducing the burden of these debilitating illnesses [2].

Support and Monitoring

AI-powered tools can also provide ongoing support and monitoring for individuals with eating disorders. Mobile applications and wearable devices can collect real-time data on a patient's behaviors, thoughts, and emotions, providing valuable insights for both the patient and their clinical team. This data can be used to identify triggers, monitor for signs of relapse, and provide just-in-time interventions. For example, a chatbot could offer coping strategies during a moment of crisis, or a wearable sensor could detect patterns of activity that may indicate a return to disordered eating behaviors. This continuous support can be particularly valuable in the periods between therapy sessions, helping individuals to stay on track with their recovery goals.

Challenges and Ethical Considerations

Despite the significant potential of AI in eating disorder treatment, there are also a number of challenges and ethical considerations that must be addressed. One of the primary limitations of current AI research is the lack of high-quality, large-scale datasets. The development of robust and reliable AI models requires access to diverse and representative data, and there is a need for greater collaboration between researchers, clinicians, and patients to build these resources [1].

Furthermore, the use of AI in mental healthcare raises important ethical questions about privacy, consent, and algorithmic bias. It is essential to ensure that patient data is handled securely and that individuals have control over how their information is used. Additionally, AI models must be carefully designed and validated to avoid perpetuating existing biases and to ensure that they are fair and equitable for all individuals.

The Future of AI in Eating Disorder Treatment

The future of AI in eating disorder treatment is bright, with ongoing research exploring new and innovative applications of this technology. As AI models become more sophisticated and the available data grows, we can expect to see even more powerful tools for early detection, personalized treatment, and ongoing support. The integration of AI with other technologies, such as virtual reality and telehealth, also holds the potential to further enhance the delivery of care.

To realize the full potential of AI in this field, it is crucial to foster collaboration between AI experts, eating disorder researchers, clinicians, and individuals with lived experience. By working together, we can ensure that the development and implementation of AI-powered tools are guided by the needs of patients and the principles of ethical and responsible innovation.

Conclusion

Artificial intelligence is poised to make a significant contribution to the field of eating disorder treatment. From improving early detection and diagnosis to enabling personalized care and providing ongoing support, AI has the potential to transform the way we approach these complex illnesses. While there are challenges and ethical considerations that must be addressed, the continued development and responsible implementation of AI-powered tools offer hope for a future where more individuals can achieve lasting recovery from eating disorders.

References

[1] Ghosh, S., Burger, P., Simeunovic-Ostojic, M., Maas, J., & Petković, M. (2024). Review of machine learning solutions for eating disorders. International Journal of Medical Informatics, 189, 105526. https://doi.org/10.1016/j.ijmedinf.2024.105526

[2] Linardon, J., & Fuller-Tyszkiewicz, M. (2025). Using Artificial Intelligence to Advance Eating Disorder Research, Treatment and Practice. International Journal of Eating Disorders, 58(5), 811–812. https://doi.org/10.1002/eat.24394

[3] Katsiferis, A., Joensen, A., Petersen, L. V., Ekstrøm, C. T., Olsen, E. M., Bhatt, S., ... & Larsen, K. S. (2025). “Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence”. npj Mental Health Research, 4(1), 65. https://doi.org/10.1038/s44184-025-00179-x