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

Won Tae Kim1,2, Jaegwang Shin3, In-Sang Yoo3

  • 1Department of Urology, Chungbuk National University Hospital, Cheongju, Republic of Korea.

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Summary

The Medication Extraction and Drug Interaction Chatbot (MEDIC) uses AI to identify potentially harmful drug interactions from medication images. This tool enhances medication safety for patients with complex health conditions.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Pharmacology
  • Medical Informatics

Background:

  • Polypharmacy increases the risk of adverse drug events, especially in patients with cancer or comorbidities.
  • Accurate identification of drug-drug interactions is crucial for patient safety and effective treatment.

Purpose of the Study:

  • To develop an AI-powered system for rapid identification of contraindicated medications.
  • To assist patients and healthcare providers in managing complex medication regimens.

Main Methods:

  • Introduction of the Medication Extraction and Drug Interaction Chatbot (MEDIC).
  • Integration of optical character recognition (OCR) and Chat generative pretrained transformer (GPT) via the Langchain framework.
  • Extraction of drug names from medication packaging images using OCR and text similarity, followed by contraindication analysis via Chat GPT.

Main Results:

  • The MEDIC system demonstrated high accuracy in identifying drug-drug interactions.
  • Validation using real-world data confirmed the system's effectiveness.
  • The streamlined process enhances the accuracy of drug-drug interaction detection.

Conclusions:

  • MEDIC enables identification of contraindicated medications solely from packaging images.
  • The system alerts users to potential adverse drug effects, improving patient care.
  • This technology offers significant potential for advancing medication safety and management practices.