A research team at Chung – ang University Hospital plans to develop a novel artificial intelligence (AI) algorithm. The aim is to predict the likelihood of weight gain and the effectiveness of obesity medications in patients taking psychiatric drugs.
Psychiatric medications such as antipsychotics, antidepressants, and mood stabilizers can cause side effects like significant weight gain and metabolic alterations. However, individual susceptibility to weight gain from the same drug varies greatly. Until now, it has been nearly impossible to predict which patients are more likely to gain weight and which anti – obesity drugs might be effective.
To address this, the team, led by Professors Kim Sun – mi from the Department of Psychiatry and Lee Hye – jun from the Department of Family Medicine, evaluated weight – related side effects after 24 weeks of administering psychiatric medications to patients with depression, bipolar disorder, or schizophrenia. Among overweight or obese patients, an additional 24 – week course of anti – obesity drugs was introduced while maintaining psychiatric treatment. The researchers monitored changes in body measurements, body composition, lifestyle habits, blood markers, and psychological states.
The team will collaborate with Professor Kim Young – bin from the Department of Artificial Intelligence at Chung – ang University. They will conduct a joint study using deep learning techniques. The AI model will be trained to recognize patterns in how specific psychiatric and anti – obesity drugs, both individually and in combination, affect weight, obesity, and metabolic parameters.
The ultimate goal is to develop a personalized AI algorithm that can predict weight gain risk and treatment efficacy. This will enable more precise and patient – specific therapeutic strategies. “This algorithm will provide crucial support in medical decision – making. It helps physicians select the most appropriate anti – obesity therapy early in the treatment process,” said Professor Lee. “It could also prevent obesity – related complications, including metabolic syndrome, cardiovascular disease, and cancer.”
Through interdisciplinary collaboration between medicine and engineering, the team hopes this AI algorithm will allow clinicians to tailor psychiatric treatment plans more safely and effectively from the outset. The results were published in PLOS One.
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