Work

Ethics of trustworthy AI in healthcare: Challenges, principles, and practical pathways

Year: 2025

Type: article

Abstract: Artificial Intelligence (AI) is transforming healthcare by enhancing diagnostics, personalizing treatment planning, and streamlining patient care. Yet, its adoption is hindered by persistent ethical challenges, including algorithmic bias, lack of transparency, privacy risks, and unclear accountability. Existing international frameworks articulate high-level principles but seldom provide operational guidance for clinical deployment. We bridge this gap by synthesizing trust dimensions for healthcare, with measurable metrics for fairness, explainability, privacy, accountability, and robustness, and proposing the Healthcare AI Trustworthiness Index (HAITI), a composite, context-aware readiness score with explicit normalization, weighting, and uncertainty reporting. We outline a development–deployment–governance blueprint and present two case studies (diagnostic bias mitigation; privacy-preserving federated learning). Together, these contributions translate ethical principles into measurable practices that can foster trust, improve equity, and accelerate responsible AI integration in clinical settings.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131942

Authors: Pegah Ahadian, Wei Xu, Dongfang Liu, Qiang Guan

Institutions: Kent State University, Brookhaven National Laboratory, Rochester Institute of Technology

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,670