Sep 2026· Cambridge University Press eBooks· 24 references
Artificial Intelligence in Healthcare and Education
Abstract
With artificial intelligence (AI) becoming increasingly integrated into healthcare systems, questions have emerged regarding how patients’ and clinicians’ views of AI in these high-stakes environments are shaped by its perceived moral competence and adherence to ethical principles, including respect for personal autonomy. To examine this question, this chapter focuses on how people evaluate medical decisions made by AI versus humans and the role that ascriptions of various moral and compassionate traits play in these judgments. We discuss current applications of AI in healthcare settings, existing empirical evidence on public perceptions of AI-assisted versus human decision-making, and offer speculative explanations for the widely documented asymmetrical preference for human over AI decision-makers in patient medication, triage, and life support decisions. As AI evolves, understanding its impact on ethical choices in healthcare is vital for balancing technological advancement with compassionate care.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
