A Safety Controlling System Framework for Implementing Artificial Intelligence in Healthcare

Abstract

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to solve the AI black-box mystery in the healthcare industry. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first-level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

Artificial Intelligence Application in Agile Transport Project Management

“…It is necessary to point out that agile management of transport projects with artificial intelligence application achieves higher efficiency [20,21].…”

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

“…We assessed agreement among the AI experts by calculating Kendall's W (Kendall's coefficient of concordance) [43,44]. This non-parametric statistic ranges in value between 0 and 1, such that 1 indicates more substantial agreement [45].…”