Mapping Municipal Regulation of Digital Health: a Proposal Using Artificial Intelligence in Legal Research

Abstract

This article proposes an empirical-legislative methodology based on web scraping and artificial intelligence techniques. Its specific objectives are to describe the proposed method, apply it to the analysis of municipal regulations related to digital health governance in Brazil, and evaluate its performance in light of potential biases, gaps, and limitations. The research involved an exploratory analysis of five Brazilian capitals, selected for their normative repositories’ accessibility and standardization. Through automated data scraping and classification of legal text using a language model, relevant legislations were identified and categorized into five main areas: governance, privacy and data protection, telehealth, research, and medical devices. The results show that digital healthrelated regulations still represent a small fraction of the total municipal regulations, mostly in the form of executive degrees. However, their number has grown significantly in the past few years, particularly after the enactment of the General Data Protection Law and during the Covid-19 pandemic. Despite limitations such as the lack of standardization of repositories and the exclusion of certain secondary regulations, the methodology proved to be efficient and scalable, enabling deeper analyses of regulatory patterns and gaps. The study concludes that automated tools have the potential to enhance analytical capacity in the legal field, promoting the empirical knowledge that is an essential foundation for shaping public policies in digital health, a strategic topic for future of regulation in Brazil.