This repository contains the data, reproducible code, and figures included in the 2024 manuscript, Evaluating Generative Artificial Intelligence's Limitations in Health Policy Identification and Evaluation. The following items are found in the folders on this repo:

Data - Contains all of the data utilized in this analysis in .csv files

Code - Contains the reproducible code for these analyses and for figure rendering in .R files

Figures - Contains .tiff files of the figures included in the manuscript

Archived - Contains code and figures that were not included in the manuscript or were duplicates of files in other folders

Other - .gitignore

Read the manuscript at: https://doi.org/10.1371/journal.pone.0312078

Abstract: Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools powered by large language models have led to suggestions that such technologies be incorporated into our project and similar research efforts to decrease the human resources required. To test the accuracy and precision of GAI in identifying and interpreting health policies, we designed a study to systematically assess the responses produced by a GAI tool versus those produced by a SME. We used two validated policy datasets, on emergency and childhood vaccination policy and quarantine and isolation policy in each United Nations Member State. We found that the SME and GAI tool were concordant 78.09% and 67.01% of the time respectively. It also significantly hastened the data collection processes. However, our analysis of non-concordant results revealed systematic inaccuracies and imprecision across different World Health Organization regions. Regarding vaccination, over 50% of countries in the African, Southeast Asian, and Eastern Mediterranean regions were inaccurately represented in GAI responses. This trend was similar for quarantine and isolation, with the African and Eastern Mediterranean regions least concordant. Furthermore, GAI responses only provided laws or information missed by the SME 2.14% and 2.48% of the time for the vaccination dataset and for the quarantine and isolation dataset, respectively. Notably, the GAI was least concordant with the SME when tasked with policy interpretation. These results suggest that GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information. However, we found that GAI is a useful tool for quality assurance and quality control processes in health policy identification.