ETHICAL CONSIDERATIONS AND REGULATORY COMPLIANCE IN IMPLEMENTING AI SOLUTIONS FOR HEALTHCARE APPLICATIONS

Abstract

The introduction of regulatory guidance and governance efforts in this rapidly changing space requires early and engaged participation among a diverse group of stakeholders. Rapid iteration on regulation and governance will accommodate the diversity of medical AI, distinguishing non-technical from technical variations, and encourage beneficial uses through developing and sharing best practices. The iterative approach will also empower responsible and safe innovation by encouraging an environment of experimentation and value-focused iteration, and by more proactively engaging the needs of multiple stakeholders at once rather than pitting them against each other [1]. Medicine and Information Systems researchers and developers are constantly working to figure out when it is appropriate for physicians, nurses, and patients to trust computer-aided information-based intelligence as a decision helper. Researchers are focused on what, when, and how explanations should be introduced to build any kind of artificial intelligence system. The lack of confidence in the black-box decision-making process of artificial intelligence-driven solutions is still an issue today. Recent research has shown that AI recommendation explanation methods were not effective without first improving the task model and the design of the explanation method. Commercial companies and responsible researchers have been increasingly working to inform AI assistants to provide clear, targeted explanations for their predictions. Computer-aided intelligent methods have shown excellent results in classification, prediction, and treatment recommendations in various domains of healthcare including telemedicine, bioinformatics, and medical imaging [2]. The quality and effectiveness of these advanced algorithms depend on how meaningful and valuable the contribution of domain experts toward the definition of models and problem solving within the established practices is. Physicians usually are not aware of the techniques and methodologies used by computer-aided intelligent methods applications and assume the decisions are the result from algorithm computations without any possible influence. This article identifies challenges, research gaps, and proposes improvements to increase the inclusion of the physicians' knowledge based on how AI solutions recommendation was obtained and encourage further cooperation with the responsible predictors in healthcare. The aim is to discuss and address ethical foundations associated with Artificial Intelligence (AI) intelligent-based solutions in healthcare [3].