How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?
It addresses the problem of unreliable AI for disease prediction in healthcare, highlighting risks for patients and clinicians, and is incremental as it compares existing models without proposing new methods.
This study evaluated the reliability of AI chatbots (GPT 4.0, Claude 3 Opus, Gemini Ultra 1.0) and BERT for predicting diseases from patient complaints in emergency departments, finding that GPT 4.0 achieved high accuracy with more data, Gemini performed well with fewer examples, and BERT underperformed due to limited labeled data, but none were reliable enough for critical medical decisions.
Artificial Intelligence (AI) chatbots leveraging Large Language Models (LLMs) are gaining traction in healthcare for their potential to automate patient interactions and aid clinical decision-making. This study examines the reliability of AI chatbots, specifically GPT 4.0, Claude 3 Opus, and Gemini Ultra 1.0, in predicting diseases from patient complaints in the emergency department. The methodology includes few-shot learning techniques to evaluate the chatbots' effectiveness in disease prediction. We also fine-tune the transformer-based model BERT and compare its performance with the AI chatbots. Results suggest that GPT 4.0 achieves high accuracy with increased few-shot data, while Gemini Ultra 1.0 performs well with fewer examples, and Claude 3 Opus maintains consistent performance. BERT's performance, however, is lower than all the chatbots, indicating limitations due to limited labeled data. Despite the chatbots' varying accuracy, none of them are sufficiently reliable for critical medical decision-making, underscoring the need for rigorous validation and human oversight. This study reflects that while AI chatbots have potential in healthcare, they should complement, not replace, human expertise to ensure patient safety. Further refinement and research are needed to improve AI-based healthcare applications' reliability for disease prediction.