Case Report: Intranasal esketamine combined with a form of generative artificial intelligence in the management of treatment-resistant depression.
Alexandre Fraichot, Sophie Favre, Hélène Richard-lepouriel
Frontiers in psychiatry January 1, 2025 Peer reviewed DOI: 10.3389/fpsyt.2025.1536232 via PubMed
Summary
A 37-year-old patient with treatment-resistant depression achieved remission after receiving intranasal Esketamine (84 mg) and using artificial intelligence (ChatGPT-4) to generate images of his experiences. His Montgomery-Åsberg Depression Rating Scale (MADRS) scores declined by 50% in the third session, indicating mild depression or euthymia in subsequent sessions. The patient found the AI-generated content helpful for understanding his experiences, and the presence of a nurse was crucial for support. Further research is needed to explore this combined treatment.
Study at a glance
| Design | case report |
|---|---|
| Sample size | 1 |
| Population | one 37-year-old patient with treatment-resistant depression |
| Key finding | The patient achieved remission from depression with a 50% decline in MADRS scores after combining intranasal Esketamine treatment with generative artificial intelligence. |
Abstract
Intranasal Esketamine is an effective rapid-acting antidepressant currently used to treat treatment-resistant depression. Artificial intelligence is another emerging tool in medicine, but little is known about the effectiveness of combining these innovations in psychiatry. This case report presents the outcome of a 37-year-old patient who received intranasal Esketamine treatment (84 mg) and utilized artificial intelligence (ChatGPT-4) to generate images and interpretations of his experiences with dissociation. This process was conducted in the presence of a nurse who assessed and supported the patient. The Montgomery-Åsberg Depression Rating Scale (MADRS) was used to measure the severity of depression at the beginning of each session. The patient achieved remission from depression, with MADRS scores declining by 50% in the third session, and the scores indicated mild depression or euthymia in the eight subsequent sessions. The patient reported that incorporating artificial intelligence-generated images and interpretations helped him create a timeline of his experiences at the end of each session. This case report highlights the potential effectiveness of combining intranasal Esketamine treatment with generative artificial intelligence images and interpretations as part of an integration process. It also emphasizes the importance of having a nurse present to support the process. Further research is needed to determine which patients may benefit most from this combined treatment approach.