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

TL;DR

This paper reviews the transformative potential of generative AI in healthcare, focusing on medical imaging and text analysis, while critically examining ethical, legal, and algorithmic challenges to promote responsible implementation.

Contribution

It provides a comprehensive analysis of ethical, legal, and algorithmic issues in applying generative AI to healthcare and proposes responsible solutions for its ethical deployment.

Findings

Generative AI enhances diagnosis and personalized care in healthcare.

Legal and ethical frameworks are crucial for responsible AI deployment.

Addressing biases and model limitations is essential for safe AI integration.

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.