AI in Health, Profiling and Data Protection

tensions between technological innovation, biolaw and fundamental rights

Abstract

Context: The advancement of artificial intelligence (AI) in healthcare brings promises of more accurate diagnoses and personalized treatments, but also generates significant legal and bioethical tensions. The risks arising from these technologies demand constant revisions of the regulatory framework surrounding them. This article examines one of these risks by investigating the potential insufficiency of the Brazilian General Data Protection Law (LGPD) to address challenges associated with the use of sensitive data and profiling techniques, which pose threats to privacy protection, equality, and the free development of personality. Objective: The aim of this study is to critically analyze the alleged algorithmic neutrality, identify weaknesses in the current regulation, and propose a consequentialist interpretation of the concept of personal data, integrating it into the principles of Biolaw, particularly the precautionary principle. Methods: The methodology adopted was deductive, based on documentary analysis and literature review, both national and international, as well as comparative examination of foreign regulatory instruments such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act of the European Union. Results: The findings show that the LGPD presents significant gaps, notably the exclusion of anonymized data from its scope, which undermines personality protection, as well as the absence of clear restrictions on automated decisions with significant effects. Risks also emerge from the technical and economic opacity of AI systems, which enable subtle forms of discrimination and hinder accountability. Conclusions: It is concluded that Brazilian law, by privileging a reactive control logic, does not provide sufficient safeguards to address the challenges of the digital age. It is therefore essential to align the LGPD with a preventive and consequentialist approach, to strengthen its integration with Biolaw principles, and to ensure robust mechanisms for transparency, auditability, and human intervention in healthcare AI systems.