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This study integrates large language models with specialized AI diagnostic tools, creating a smart chatbot capable of analyzing medical images and predicting diseases. The modular design allows for easy expansion, enhancing user experience in bioinformatics.

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Area of Science:

  • Artificial Intelligence
  • Medical Diagnostics
  • Bioinformatics

Background:

  • Conversational AI, particularly generative AI, is a leading trend, often deployed as chatbots.
  • OpenAI advancements allow integrating external models for specialized chatbot functions beyond general language processing.

Purpose of the Study:

  • Develop a smart chatbot integrating OpenAI's large language models with specialized medical diagnostic models.
  • Demonstrate combining LLMs with external models for multimodal, task-oriented conversational agents.

Main Methods:

  • Developed image-based classifiers using transfer learning (Google's Teachable Machine).
  • Integrated models for chest X-ray pneumonia detection and optical coherence tomography (OCT) analysis.
  • Incorporated a TensorFlow.js diabetes prediction model using physiological data.
  • Designed a modular architecture for easy addition of new diagnostic models.

Main Results:

  • Successfully integrated chatbot with diagnostic models, showing minor deviations from expected behavior.
  • Implemented a function for scheduling medical appointments based on condition severity.
  • Tested integration with OCT and X-ray models for appointment scheduling.

Conclusions:

  • Demonstrated feasibility of developing advanced AI systems (image diagnostics, chatbot integration) using AI as a service.
  • Highlighted AI's potential to improve user experience and accessibility in bioinformatics.
  • Emphasized the chatbot's modularity for future integration of additional diagnostic models.