AI and the future of healthcare in Peru: exploring legal pathways and ethical dilemmas
Maritza Reátegui Valdiviezo
Rodrigo, Elías & Medrano, Lima-Perú
Daniela Supo Calderón
Rodrigo, Elías & Medrano, Lima-Perú
Artificial intelligence (AI) is reshaping industries globally, with healthcare standing out as one of its most transformative applications. In Peru, AI holds the potential to revolutionise healthcare by enhancing diagnostics, improving treatment efficiency and advancing patient outcomes. However, the integration of AI also introduces significant legal and ethical challenges that require urgent attention. This article examines AI’s role in Peruvian healthcare, the regulatory frameworks needed for its implementation, and the ethical considerations at play.
AI in Peruvian healthcare: opportunities and challenges
AI technologies are beginning to redefine global healthcare: Peru is no exception. AI applications can facilitate early diagnosis, predictive analytics, drug discovery and tailored treatment plans. For instance, AI-driven tools can analyse medical imaging to detect conditions like cancer or cardiovascular disease at earlier, more treatable stages, potentially saving lives.
Despite its transformative potential, the adoption of AI in Peru faces notable challenges. Limited access to advanced technology in rural areas, gaps in digital infrastructure and a fragmented healthcare system hinder the widespread application of AI solutions. Additionally, many healthcare professionals lack the training necessary to effectively use and trust AI-driven tools, particularly in underfunded health facilities. Addressing these barriers is essential to ensuring the equitable distribution of AI benefits.
The successful integration of AI into healthcare hinges on a robust legal framework that strikes a careful balance between fostering innovation and safeguarding patient rights. In Peru, key priorities include ensuring data privacy, establishing clear liability frameworks and implementing rigorous certification processes for AI-based medical tools.
AI systems depend heavily on data, often involving sensitive health information. Adherence to the Personal Data Protection Law (Law No 29733) is critical for protecting patient privacy, especially as the effectiveness of data anonymisation continues to present legal and technical challenges. Additionally, collaborations with international AI developers often require cross-border data transfers, underscoring the need for robust legal safeguards to harmonise privacy protection with the demands of innovation.
Ethical dilemmas in AI-driven healthcare
Ethical dilemmas in AI-driven healthcare extend beyond legal considerations, raising complex questions about fairness, autonomy and decision-making. While AI has the potential to significantly improve healthcare access and outcomes, its benefits could disproportionately favour urban areas with better resources, leaving rural regions further behind. This disparity highlights the need for ethical frameworks that prioritise equitable access. Strategies such as government subsidies or partnerships could help extend AI-driven healthcare solutions to underserved areas, ensuring that innovation benefits all populations.
Another critical issue is informed consent and patient autonomy. AI tools used in diagnosis or treatment recommendations, while valuable, could inadvertently pressure patients into accepting decisions without fully understanding their basis. For instance, patients might feel obligated to follow AI-generated suggestions, even when they have reservations. Transparent communication about how AI systems operate, including their limitations, is essential to maintaining trust and preserving the ability of patients and providers to make informed decisions.
The predictive capabilities of AI also raise ethical concerns about its role in diagnostics, particularly in preventive care. There are questions about whether an AI system should inform a patient of a predisposition to an incurable disease. While early detection has clear benefits, it can also lead to unnecessary psychological distress or unwarranted medical interventions. Developing clear guidelines is crucial to balance the advantages of predictive diagnostics with the potential risks of causing harm or anxiety to patients.
Building public trust is essential for the successful adoption of AI in healthcare. Transparency plays a vital role, as patients and healthcare providers must clearly understand how AI systems make decisions. This can be achieved through explainable AI models and transparent reporting mechanisms that demystify the technology. Stakeholder engagement is equally important: policymakers should include diverse perspectives (such as those of patients, healthcare workers, technologists and ethicists) in shaping AI regulations. Public consultations and educational campaigns can address misconceptions, encourage informed discussions and foster confidence in AI-driven healthcare solutions.
Conclusion
Ethical dilemmas in AI-driven healthcare revolve around fairness, autonomy and the responsible integration of technology in decision-making. While AI offers significant potential to enhance healthcare, its benefits risk disproportionately favouring urban areas, leaving rural populations underserved. Addressing these disparities requires ethical frameworks that prioritise equitable access, supported by initiatives such as government subsidies or partnerships designed to extend AI-driven solutions to underserved areas. Transparency about how AI systems operate and their limitations is equally critical to safeguarding patient autonomy and maintaining trust in both the technology and healthcare providers.
Building public trust is essential for successful AI adoption in healthcare. This can be achieved through explainable AI models and meaningful stakeholder engagement – ensuring that patients, healthcare professionals and policymakers have a voice in shaping regulations. By fostering transparency, fairness and inclusivity, Peru can position AI as a transformative tool to enhance healthcare outcomes, bridge inequalities and complement the expertise of healthcare professionals.