In modern life, the application of artificial intelligence (AI) has promoted the implementation of data-driven algorithms in high-stakes domains, such as healthcare. However, it is becoming increasingly challenging for humans to understand the working and reasoning of these complex and opaque algorithms. For AI to support essential decisions in these domains, specific ethical issues need to be addressed to prevent the misinterpretation of AI, which may have severe consequences for humans. However, little research has been published on guidelines that systematically addresses ethical issues when AI techniques are applied in healthcare. In this systematic literature review, we aimed to provide an overview of ethical concerns and related strategies that are currently identified when applying AI in healthcare. The review, which followed the PRISMA guidelines, revealed 12 main ethical issues: justice and fairness, freedom and autonomy, privacy, transparency, patient safety and cyber security, trust, beneficence, responsibility, solidarity, sustainability, dignity, and conflicts. In addition to these 12 main ethical issues, we derived 19 ethical sub-issues and associated strategies from the literature.

Pancreatic cancer remains one of the deadliest malignancies, with outcomes dependent on timely diagnosis and accurate assessment of surgical resectability. Centralized care models aim to improve diagnostic quality by concentrating specialized expertise. While centralization improves access to expertise, it introduces new challenges in coordinating diagnostic work across institutions, aligning heterogeneous data and technologies, and sustaining reliable collective decision-making under time pressure. How diagnostic reasoning is practically accomplished within these distributed care networks remains insufficiently understood. We conducted a qualitative case study of a regional centralized pancreatic cancer care network in the Netherlands. Drawing on socio-technical systems theory, we analyzed diagnostic workflows including imaging assessments, multidisciplinary team meetings, and surgical procedures through contextual observations. Our findings show that diagnostic reliability emerges from the coordinated interaction of distributed expertise, clinical data, and technological infrastructures. Multidisciplinary meetings, shared imaging systems, and iterative data interpretation play a central role, while tensions arise around efficiency, standardization, authority, and uncertainty. This study contributes (1) empirical insight into the socio-technical dynamics of centralized pancreatic cancer care, (2) a qualitative approach for studying complex, distributed healthcare systems, and (3) practice-grounded implications for integrating AI in ways that support existing clinical coordination and expertise.