AI-Guided Pharmacogenomics for Personalised Antidepressant Therapy
| dc.contributor.author | Kumar, Soorya Kanath Sudee | |
| dc.date.accessioned | 2025-11-14T13:01:35Z | |
| dc.date.available | 2025-11-14T13:01:35Z | |
| dc.date.issued | 2025-11 | |
| dc.description.abstract | This poster explores the transformative role of artificial intelligence (AI) in enhancing pharmacogenomic techniques to personalise antidepressant prescriptions, focusing on implementations and perceptions in India and Europe. Using a mixed-methods design combining quantitative surveys and qualitative interviews, the research assesses AI's potential to reduce trial-and-error prescribing, improve treatment accuracy, and address regional ethical, regulatory, and infrastructure challenges. Findings show strong agreement on AI's effectiveness in improving prescription accuracy and personalising therapy, with Europe demonstrating higher awareness and regulatory structure compared to India. Key barriers include data privacy concerns, training deficits, and ethical uncertainties. The study concludes that AI-driven pharmacogenomics can significantly optimize antidepressant therapy but requires targeted clinician training, robust ethical and data protection frameworks, and investment in genetic testing infrastructure. Cross-regional collaboration is essential to harmonize regulations and maximise AI benefits in personalised mental health care. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14136/265 | |
| dc.title | AI-Guided Pharmacogenomics for Personalised Antidepressant Therapy | |
| dc.type | Other |