UNAIR NEWS The Faculty of Health, Medicine, and Natural Sciences (FIKKIA) (UNAIR) hosted a scientific seminar titled Integrating Artificial Intelligence in Health Care: A Data-Driven Approach to Enhance and Promote Personalized Interventions in Neuropsychiatric Disorders on Thursday (March 6, 2025). Held in a hybrid format, the event took place at the Main Hall of FIKKIA UNAIR Banyuwangi and was also streamed online via Zoom. The keynote speaker, Dr. Benjamin Wade, PhD, from Harvard Medical School, was joined by Dr. Kurnia Alisaputri, SpPD, from FIKKIA UNAIR, who moderated the discussion.
Dr. Wade explored the integration of machine learning in Precision Interventional Psychiatry, highlighting AI檚 critical role in diagnosing and treating mental disorders with greater personalization. 淒epression is one of the most prevalent health conditions worldwide, affecting approximately 280 million individuals, he stated.
Currently, the diagnosis and treatment of neuropsychiatric disorders primarily rely on conventional clinical assessments, neurological observations, genetic data, and patient medical histories. However, nearly half of patients prescribed Selective Serotonin Reuptake Inhibitors (SSRIs) fail to achieve optimal results from initial treatment. 淥nly about 50 percent of patients show improvement following their first SSRI therapy, he explained.
To address this challenge, Precision Interventional Psychiatry develops machine learning-based statistical models to predict individual responses to specific treatments. These models analyze cognitive patterns, SSRI responsiveness, genetic expressions, demographic factors, and medical histories. 淥ur goal is to accelerate treatment effectiveness while reducing reliance on trial-and-error approaches, he added.
Advancements in Interventional Psychiatry
Dr. Wade outlined three key approaches within Interventional Psychiatry: neuromodulation therapy, pharmacological therapy, and electroconvulsive therapy (ECT). ECT involves administering controlled electrical impulses to the brain to treat severe, treatment-resistant depression. Non-invasive alternatives such as Magnetic Seizure Therapy (MST) and Transcranial Magnetic Stimulation (TMS) utilize magnetic fields to regulate brain activity. Additionally, Ketamine and S-Ketamine, NMDA receptor antagonists with rapid antidepressant effects, have been FDA-approved in intranasal form since 2019.
He also discussed machine learning applications in Precision Psychiatry, particularly the use of supervised learning, where labelled-patient data trains predictive models. 淭his methodology enables us to detect patterns within datasets to forecast treatment outcomes for new patients, he explained.
To ensure model reliability, cross-validation techniques are employed, dividing datasets into multiple subsets to validate predictions. One widely used technique, random forests, utilizes multiple decision trees working collectively to improve predictive accuracy. 淭his approach enhances model resilience and enables it to identify complex patterns in patient data, Dr. Wade noted.
The integration of AI into interventional psychiatry holds the potential to significantly enhance treatment precision and efficiency. 淣ot only can this approach expedite diagnosis and therapy, but it also improves patient outcomes by reducing the risks associated with ineffective treatments, he emphasized.
The seminar garnered substantial interest, particularly from students and healthcare professionals keen on exploring AI-driven innovations in medicine. As advancements in AI and mental health research continue to evolve, data-driven treatment strategies promise a future of more personalized and evidence-based care solutions.
Author: Ameyliarti Bunga Lestari
Editor: Edwin Fatahuddin
