Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations
It addresses critical issues for healthcare stakeholders, but is incremental as it reviews existing concerns without new empirical results.
The paper examines the ethical, legal, and societal challenges of applying generative AI in healthcare, such as accuracy and data privacy, and proposes solutions for responsible implementation.
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.