Abstract
Background: The integration of Artificial Intelligence (AI) into precision health holds transformative potential, promising enhanced diagnostics, personalized treatments, and improved patient outcomes. However, this rapid advancement introduces a complex array of ethical challenges that necessitate rigorous examination to ensure responsible and equitable deployment. This article addresses the critical need for a structured understanding of these ethical considerations within the context of precision health. Methods: This study employs a systematic conceptual analysis and critical review of existing literature, synthesizing prevalent ethical concerns associated with AI in precision health interventions. Drawing exclusively from a pre-filtered corpus of scholarly works published on or before February 2024, the methodology identifies, categorizes, and analyzes recurring ethical themes such as data privacy, algorithmic bias, transparency, accountability, and patient autonomy. Results: The analysis reveals a multifaceted ethical landscape characterized by significant concerns across various domains. Key findings highlight the pervasive risks of algorithmic bias leading to health disparities, challenges in establishing accountability for AI-driven errors, and the imperative for greater transparency in 'black-box' AI models. Furthermore, issues of data security, patient informed consent, and the potential impact on human-AI professional roles are consistently identified as critical. Invented tables illustrate the prevalence of these concerns and comparative approaches to ethical governance. Conclusion: Proactive integration of ethical safeguards is paramount for the responsible and equitable deployment of AI in precision health. Addressing these challenges requires multidisciplinary collaboration, robust regulatory frameworks, and a sustained commitment to human-centered AI design to maximize benefits while mitigating risks.
Keywords
Artificial Intelligence, Precision Health, Medical Ethics, Algorithmic Bias, Data Privacy, Accountability, Transparency, Digital Health