Algor-ethics: charting the ethical path for AI in critical care

Abstract

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

Keywords: Algorethics; Artificial intelligence; Data engineering; Ethics; Machine learning.

Fig. 1

Graphical depiction illustrating the progression of technological innovation in relation to human adaptability. The evident divergence emphasizes the accelerated rate of technological advancements compared to human capacity for adaptation, leading to potential areas of disparity and unpredictability. Such observations necessitate a considered approach to technological developments to ensure alignment with human evolutionary pathways

Fig. 2

A Mind Map of AI Application in Healthcare. The looping structure illustrates the various steps in the development and deployment of AI models, starting with data acquisition from hospitals or institutions (data guardians), to the storage of this information in electronic health records (EHRs), to the prototyping and validation phase of AI algorithms within a clinical AI department (CAID). The process culminates in the implementation of clinical decision support systems (CDSSs) that can alter clinical practice. This cycle is built upon the three pillars of digital transformation: data quality and quantity, a sound technological infrastructure, and a nurturing digital culture. Algorithmic stewardship guides and oversees the various domains: learning with humans, curriculum learning, explainable AI, and Beyond learning – Useful and Usable AI. This entire process aligns with the principles of algor-ethics, putting humans at the center of the process and adhering to the principles of traceability, customization, and adequation. This underscores the human-centered approach of algor-ethics, with AI designed to augment human decision-making and adapt to the unique needs and interests of each individual. It serves as an ethical layer that embraces and interacts with all aspects of the AI development and deployment process