⁠Advancing ethical AI in healthcare through interpretability

Yilin Ning; Mingxuan Liu; Nan Liu

doi:10.1016/j.patter.2025.101290

⁠Advancing ethical AI in healthcare through interpretability

Patterns (N Y). 2025 Jun 13;6(6):101290. doi: 10.1016/j.patter.2025.101290.

Authors

Yilin Ning^{1

2}, Mingxuan Liu^{1

2}, Nan Liu^{1

2

3

4

5}

Affiliations

¹ Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
² Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore.
³ Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
⁴ NUS Artificial Intelligence Institute, National University of Singapore, Singapore, Singapore.
⁵ Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

Abstract

Interpretability is essential for building trust in health artificial intelligence (AI), but ensuring trustworthiness requires addressing broader ethical concerns, such as fairness, privacy, and reliability. This opinion article discusses the multilayered role of interpretability and transparency in addressing these concerns by highlighting their fundamental contribution to the responsible adoption and regulation of health AI.

Publication types

News