Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses

TL;DR

This study evaluates the diagnostic capabilities of large language models like GPT-4, Gemini, and GPT-3.5 in recognizing common illnesses from symptoms, highlighting their potential to improve accuracy and efficiency in digital healthcare diagnostics.

Contribution

It provides a comparative analysis of LLMs' diagnostic accuracy and discusses their potential applications and ethical considerations in medical practice.

Findings

GPT-4 shows highest diagnostic accuracy.

Gemini excels in disease triage.

GPT-3.5 is effective but slightly less accurate.

Abstract

The recent swift development of LLMs like GPT-4, Gemini, and GPT-3.5 offers a transformative opportunity in medicine and healthcare, especially in digital diagnostics. This study evaluates each model diagnostic abilities by interpreting a user symptoms and determining diagnoses that fit well with common illnesses, and it demonstrates how each of these models could significantly increase diagnostic accuracy and efficiency. Through a series of diagnostic prompts based on symptoms from medical databases, GPT-4 demonstrates higher diagnostic accuracy from its deep and complete history of training on medical data. Meanwhile, Gemini performs with high precision as a critical tool in disease triage, demonstrating its potential to be a reliable model when physicians are trying to make high-risk diagnoses. GPT-3.5, though slightly less advanced, is a good tool for medical diagnostics. This study highlights the need to study LLMs for healthcare and clinical practices with more care and attention, ensuring that any system utilizing LLMs promotes patient privacy and complies with health information privacy laws such as HIPAA compliance, as well as the social consequences that affect the varied individuals in complex healthcare contexts. This study marks the start of a larger future effort to study the various ways in which assigning ethical concerns to LLMs task of learning from human biases could unearth new ways to apply AI in complex medical settings.