Patient Trust, Protection, and Ethical Care Focus

Document Type

Conference Proceeding

Publication Date

6-2026

Abstract

Research Objective: As artificial intelligence becomes increasingly embedded in healthcare, questions of trust, dignity, and ethical protection become inseparable from questions of innovation. This study explored how professionals engaged in cybersecurity, data systems, and digital health understand the moral responsibilities tied to AI in healthcare, with particular attention to patient trust, vulnerability, and the ethical obligations of those who shape AI-enabled care.

Study Design: This qualitative study used semi-structured interviews to examine how individuals with intimate knowledge of AI infrastructures reflect on the promise and risk of AI in healthcare. Interviews were transcribed and analyzed using reflexive thematic analysis, allowing careful attention to the relational, ethical, and moral dimensions of trust, protection, and accountability in AI-supported care environments.

Population Studied: Participants included twenty professionals working in cybersecurity, AI development, digital health infrastructures, and healthcare technology. The sample was predominantly Black/African American, with Asian, White, and other racial identities also represented. Most participants were between 25 and 44 years old, the majority held master’s or professional degrees, and nearly all lived in urban or suburban contexts. These were highly educated experts with direct experience in health-related data security, AI decision-making environments, and digital health implementation realities, bringing both technical knowledge and ethical concern to their reflections.

Principal Findings: Participants described AI as genuinely capable of strengthening healthcare by supporting decision-making, enhancing monitoring, and potentially improving safety and efficiency. Yet they also spoke with deep awareness of the vulnerability inherent in healthcare contexts and the fragile nature of trust when technology becomes intertwined with human care. Trust was described not as something automatic but as something earned through visible integrity, strong protections, transparency, fairness, and ethical commitment. Concerns emerged regarding insecurity, data misuse, weak governance, poorly monitored tools, and implementation environments that move faster than ethical readiness. Throughout the conversations, participants emphasized the indispensable role of human judgment, meaningful oversight, and a care ethic that prioritizes patient dignity, protection, and moral accountability.

Conclusions: This study suggests that trust in health AI is never automatic. It is relational, ethical, and deeply human. For AI to genuinely serve healthcare rather than complicate it, systems must embody protection, accountability, transparency, fairness, and compassion. Ethical readiness, rather than technological enthusiasm alone, ultimately determines whether AI strengthens or weakens trust in care.

Implications for Policy or Practice: These findings call for governance approaches that explicitly center patients as people, not merely data subjects. Strong cybersecurity protections, clear ethical guidance, transparent communication, meaningful human oversight, and accountability structures that honor patient dignity are essential. Responsible AI in healthcare must not only advance innovation but preserve trust, protect vulnerability, and sustain the moral foundations of healing environments.

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AcademyHealth Annual Research Meeting, May 30-June 2, 2026, Seattle, WA

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