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Benchmarking Large Language Models for Self-Diagnosis

Benchmarking Large Language Models for Self-Diagnosis

Project Contents

Full project title: Benchmarking Large Language Models for Self-Diagnosis

Overview

Our work investigates applications of large language models (LLMs) in healthcare settings, with a particular focus on interactions between LLMs and human users. It is a multi-phase project led by Dr Adam Mahdi (Principal Investigator), along with a team of AI researchers and clinical experts.

The Project focuses on LLMs for medical self-diagnosis and identifies factors leading to collaboration failure. We are recruiting 1,000 individuals to better understand these human-machine interactions. This is one of the first large scale projects to study how members of the general public attempt to use LLMs for information-seeking purposes, and adds an important lens in focusing on the risks arising from the interactions.

Key Information

Funders:
  • Prolific
  • Clarendon Fund
  • Contact:
    Adam Mahdi