Towards Human-AI Collaboration in Healthcare: Guided Deferral Systems with Large Language Models

Abstract

Large language models (LLMs) present a valuable technology for various applications in healthcare, but their tendency to hallucinate introduces unacceptable uncertainty in critical decision-making situations. Human-AI collaboration (HAIC) can mitigate this uncertainty by combining human and AI strengths for better outcomes. This paper presents a novel *guided deferral* system that provides intelligent guidance when AI defers cases to human decision-makers. We leverage LLMs' verbalisation capabilities and internal states to create this system, demonstrating that fine-tuning small-scale LLMs with data from large-scale LLMs greatly enhances performance while maintaining computational efficiency and data privacy. A pilot study showcases the effectiveness of our proposed deferral system.

Cite

Text

Strong et al. "Towards Human-AI Collaboration in Healthcare: Guided Deferral Systems with Large Language Models." ICML 2024 Workshops: LLMs_and_Cognition, 2024.

Markdown

[Strong et al. "Towards Human-AI Collaboration in Healthcare: Guided Deferral Systems with Large Language Models." ICML 2024 Workshops: LLMs_and_Cognition, 2024.](https://mlanthology.org/icmlw/2024/strong2024icmlw-humanai/)

BibTeX

@inproceedings{strong2024icmlw-humanai,
  title     = {{Towards Human-AI Collaboration in Healthcare: Guided Deferral Systems with Large Language Models}},
  author    = {Strong, Joshua and Men, Qianhui and Noble, Alison},
  booktitle = {ICML 2024 Workshops: LLMs_and_Cognition},
  year      = {2024},
  url       = {https://mlanthology.org/icmlw/2024/strong2024icmlw-humanai/}
}