Prompt-Based Inoculation Reduces AI Bias in Epilepsy Care

Source: medRxiv : the preprint server for health sciences

Summary

This study looked at how well large language models (LLMs) can provide accurate medical recommendations for epilepsy while avoiding biases based on socioeconomic status. Researchers tested six different LLMs using two fictional cases of epilepsy that were identical except for the socioeconomic details. They wanted to see if a simple prompt could help the models ignore irrelevant information and improve their accuracy in diagnosing and treating epilepsy.

The results showed that the models initially had low accuracy rates—36% for diagnosis and 51% for treatment. There was also a significant bias, with high socioeconomic cases getting much better responses than low socioeconomic ones. After adding the prompt to instruct the models to disregard socioeconomic details, accuracy improved to 55% for diagnosis and 63% for treatment, while the bias gap decreased significantly. However, the effectiveness of the prompt varied across different models, with some completely eliminating bias and others worsening the situation.

This research is important because it suggests that simple changes in how we interact with AI can help reduce bias and improve the quality of medical recommendations. However, the varying results among different models highlight that this approach is not a one-size-fits-all solution. Ongoing monitoring and additional strategies will be necessary to ensure that AI in healthcare remains fair and effective for everyone.

Original source