( continued from page 19)
cautiously given theoretical concerns in patients with restrictive eating pathology) alongside ketamine and esketamine, SAINT neuromodulation, repetitive TMS and broader metabolic psychiatry approaches; these remain emerging rather than first-line interventions.
Table 2 consolidates these pharmacologic and monitoring considerations alongside their corresponding surveillance requirements.
Discussion
The framework proposed here reframes eating disorder care around convergence rather than sequence: nutritional psychiatry, precision medicine, computational neuroscience and meaning-centered psychotherapy operate simultaneously rather than as discrete stages. This distinguishes AI-augmented clinical intelligence from consumer-facing tracking tools, which record behavior but do not generate biologically grounded, individualized hypotheses. The distinction matters clinically: a system that merely logs calories cannot anticipate relapse, whereas one that integrates inflammatory biomarkers, sleep architecture and mood trajectories may flag deterioration before it becomes clinically obvious.
This model also carries limitations. Much of the supporting evidence for AI-augmented inference remains preliminary, and clinical validation across diverse populations is limited. The SAINT Intelligence terminology requires careful distinction from established SAINT neuromodulation therapy to avoid conflation in the literature. Pharmacologic options such as GLP-1 receptor agonists warrant particular caution in patients with active or historical restrictive eating pathology, and enthusiasm for novel neuromodulation approaches should not outpace the evidence supporting them. Finally, technology-supported care must be positioned as an extension of clinical judgment and the therapeutic alliance, not a substitute for either. AI systems function best when they widen the clinician’ s field of view rather than narrow the patient’ s experience to a stream of biomarkers.
20 LOUISVILLE MEDICINE
Table 2. Comparative Efficacy and Monitoring Protocols
Conclusions & Future Directions
The future of eating disorder care is unlikely to be defined by a single medication, psychotherapy, dietary prescription or digital platform. It will instead emerge from the convergence of nutritional psychiatry, precision medicine, computational neuroscience, artificial intelligence and meaning-centered clinical care. In this model, AI does not replace clinical judgment. It amplifies it, transforming fragmented observations
Intervention Primary Indication Evidence Status Key Monitoring Parameters
Fluoxetine
Other SSRIs
Lisdexamfetamine
Topiramate
Bupropion / naltrexone
GLP-1 receptor agonists
Ketamine / esketamine, SAINT, rTMS
AI-augmented monitoring
Bulimia nervosa
Comorbid depression, anxiety, OCD traits, trauma symptoms
Moderate-to-severe bingeeating disorder
Adjunct for binge-eating disorder
Selected refractory cases
Investigational in metabolicpsychiatric overlap
Refractory mood symptoms
All stages
FDA-approved; reduces binge – purge frequency
Established, off-label for ED-specific symptoms
FDA-approved
Emerging / off-label
Emerging; contraindicated in active purging( bupropion)
Early / cautious; eating disorder-specific safety unclear
Emerging neuromodulation and rapid-acting options
Adjunctive across framework
Suicidality( early treatment), QTc if combined with other agents, weight trend
Mood response, GI tolerability, sexual side effects
Cardiovascular status, appetite suppression, misuse potential
Cognitive side effects, nephrolithiasis risk, weight trend
Seizure risk assessment, blood pressure
Nutritional adequacy, disordered-eating symptom trend
Dissociative effects, mood and cognitive monitoring
Biomarker trend concordance with clinical assessment into coherent, individualized insight while preserving the therapeutic alliance at the center of recovery. The clinician of the future will not simply measure weight or prescribe a diet. They will integrate biology, behavior, technology and human meaning to restore health through nourishment, treating recovery not as normalization of a number on a scale, but as restoration of the patient’ s relationship with food, body and self.
References
1. American Psychiatric Association.( 2023). Practice guideline for the treatment of patients with eating disorders( 4th ed.).
2. Arns, M., et al.( 2022). Stanford Accelerated Intelligent Neuromodulation Therapy( SAINT): Evidence and future directions. Brain Stimulation.
3. Cryan, J. F., O’ Riordan, K. J., Cowan, C. S. M., et al.( 2019). The microbiota – gut – brain axis. Physiological Reviews, 99( 4), 1877 – 2013.
4. Jacka, F. N., et al.( 2017). A randomised controlled trial of dietary improvement for adults with major depression( the SMILES trial). BMC Medicine, 15, 23.
5. Marx, W., et al.( 2021). Diet and depression: Exploring the biological mechanisms of action. Molecular Psychiatry, 26, 134 – 150.
6. Treasure, J., Duarte, T. A., & Schmidt, U.( 2020). Eating disorders. The Lancet, 395( 10227), 899 – 911.
Dr. Gupta is a psychiatrist who has practiced in Louisville for more than three decades. He completed psychiatric residency and fellowship training at the University of Rochester School of Medicine and served as Assistant Professor of Psychiatry and Family Medicine at LSU School of Medicine. Dr. Gupta has held academic appointments including Clinical Professor roles at Sullivan University College of Pharmacy and still fulfills a Clinical Professor role at University of Pikeville School of Osteopathic Medicine. His research interests include mood disorders, schizophrenia, the neurobiology of depression and innovative approaches to mental health treatment.