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Related Experiment Video

Updated: Aug 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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AI chatbots not yet ready for clinical use.

Joshua Au Yeung1,2, Zeljko Kraljevic3, Akish Luintel1

  • 1Department of Neuroscience, Kings College Hospital, London, United Kingdom.

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|May 1, 2023
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Summary

This study compares ChatGPT and Foresight, two transformer-based large language models (LLMs), for medical diagnosis forecasting. Foresight shows potential for clinical applications, though limitations exist for healthcare chatbots.

Keywords:
AI safetychatbotdigital healthlarge language modelsnatural language processing (computer science)transformer

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Medical Informatics

Background:

  • Large language models (LLMs) and conversational AI are advancing rapidly.
  • Healthcare-specific LLMs like GatorTron focus on medical knowledge.
  • Transformer-based models show near human-level performance on medical benchmarks.

Purpose of the Study:

  • Compare ChatGPT (general LLM) and Foresight (healthcare-focused GPT model).
  • Evaluate their performance in forecasting diagnoses from clinical vignettes.
  • Discuss the potential and limitations of transformer-based chatbots in clinical settings.

Main Methods:

  • Comparative analysis of ChatGPT and Foresight.
  • Task: Forecasting relevant diagnoses based on clinical vignettes.
  • Evaluation of performance metrics for diagnostic accuracy.

Main Results:

  • Foresight, a GPT-based model, demonstrates potential in modeling patients and disorders.
  • Performance comparison on diagnostic forecasting task highlights differences between general and specialized LLMs.
  • Identified key considerations and limitations for clinical implementation.

Conclusions:

  • Transformer-based chatbots show promise for healthcare applications.
  • Specialized models like Foresight may offer advantages over general models like ChatGPT for clinical tasks.
  • Further research is needed to address limitations before widespread clinical adoption.