Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction

• Published in: Mayo Clinic Proceedings. Digital health 2024-12, Vol.2 (4), p.611-619
• Main Authors: Kim, Won Tae, Shin, Jaegwang, Yoo, In-Sang, Lee, Jae-Woo, Jeon, Hyun Jeong, Yoo, Hyo-Sun, Kim, Yongwhan, Jo, Jeong-Min, Hwang, ShinJi, Lee, Woo-Jeong, Park, Seung, Kim, Yong-June
• Format: Article
• Language: English
• ISSN: 2949-7612; 2949-7612
• DOI: 10.1016/j.mcpdig.2024.09.001

To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously. In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024. This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably. By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.