Summary of Trustworthy and Practical Ai For Healthcare: a Guided Deferral System with Large Language Models, by Joshua Strong et al.
Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models
by Joshua Strong, Qianhui Men, Alison Noble
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel Human-AI Collaboration (HAIC) guided deferral system that combines the strengths of humans and AI to improve trust in critical decision-making environments like healthcare. The system can parse medical reports for disorder classification while deferring uncertain predictions to human guidance. To achieve this, the authors develop open-source large language models (LLMs) and conduct a pilot study demonstrating their effectiveness in practice. Additionally, the paper highlights the limitations of standard calibration metrics in imbalanced data scenarios common in healthcare and suggests a simple solution: the Imbalanced Expected Calibration Error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to help doctors make better decisions by looking at medical reports. It makes machines that can understand what’s written in these reports, but also knows when it’s not sure and needs human help. This can be useful because doctors trust their own judgment more than a machine’s. The authors made a special system that works well in this area and tested it to see how good it is. |
Keywords
» Artificial intelligence » Classification
