Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations

TL;DR

The paper investigates the ethical, societal, legal, and technical challenges of deploying Generative AI in healthcare, focusing on medical imaging and text analysis. It analyzes privacy, consent, bias, transparency, accountability, IP, and regulatory considerations, along with data availability, hallucinations, model training costs, and workflow integration. It proposes a roadmap for responsible innovation that includes robust governance, audit trails, human-in-the-loop approaches, and regulatory alignment to ensure safety, fairness, and trust. The work highlights practical implications for clinical practice and policy, aiming to balance AI-enabled diagnostic gains with patient wellbeing and equity.

Abstract

Generative AI is rapidly transforming medical imaging and text analysis, offering immense potential for enhanced diagnosis and personalized care. However, this transformative technology raises crucial ethical, societal, and legal questions. This paper delves into these complexities, examining issues of accuracy, informed consent, data privacy, and algorithmic limitations in the context of generative AI's application to medical imaging and text. We explore the legal landscape surrounding liability and accountability, emphasizing the need for robust regulatory frameworks. Furthermore, we dissect the algorithmic challenges, including data biases, model limitations, and workflow integration. By critically analyzing these challenges and proposing responsible solutions, we aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare, ensuring its transformative potential serves humanity with utmost care and precision.