Trust, Security, and Regulatory Compliance in AI: Literature and Practical Experience, and the Way Forward for AI in Healthcare

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities to enhance patient care and healthcare systems. With AI technologies, including machine learning (ML) and natural language processing (NLP), healthcare organizations can optimize workflows, improve decision making, and deliver more personalized care. However, to fully realize AI’s potential, it is essential to integrate human factors (HF) principles and adhere to stringent regulatory frameworks that ensure both safety and trust. This paper describes examples of healthcare AI from the authors’ experience and from literature review, including electronic health record (EHR) applications and neuromorphics technologies, and then addresses relevant issues relating to automation trust theory and practice. Trust issues covered include users’ positive/negative attitudes stemming from knowing AI is the source of the information, as well as cybersecurity concerns related to the privacy and security of data generated or processed by AI, and ethical considerations affecting user trust. We summarize key solutions that support compliance with regulatory standards, and suggest future work.

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