Sep 2026· AI and Ethics· Vol 6· 0 citations· 35 references
TL;DR
This paper distinguishes technical reliability from trust in AI-supported clinical decision-making, arguing that the two belong to distinct registers that current debates tend to elide and proposes replacing the expression "trustworthy AI" with "technically reliable AI," a distinction that relocates the conditions of trust from the design of the system to the clinical settings in which it is used.
Artificial intelligence is increasingly used in medical imaging, clinical decision support, and digital triage. However, technical performance alone does not determine whether such tools will be accepted in practice. Their implementation also depends on whether patients perceive them as understandable, safe, fair, and...
Oliwer Műller, Jagoda Pałubska, Zuzanna Rafałowska et al.· International Journal of Inn...· 0 citations
It is concluded that a rights-sensitive use of AI requires procedural safeguards embedded in institutional design: functional transparency, documented dialogue, renewable consent, protected deviation from automated outputs, and accessible routes of contestation.
Martyna· Discourse of Law and Adminis...· 0 citations
Background Large language models (LLMs) have demonstrated considerable potential in cardiovascular diagnostic assistance and clinical decision support. A critical safety-related question, however, remains poorly characterised: how patients subjectively perceive the consistency between artificial intelligence (AI) and p...
Yun Yan, Zhi-Ping Wang, Xue-Ting Wang et al.· Frontiers in Digital Health· 0 citations
This study integrates two experimental investigations to examine how AI-CDSS design features jointly influence trust, autonomy-related perceptions, and acceptance across patients and physicians, and demonstrates that perceived usefulness is the strongest predictor of intention to use among both patients, potential futu...
Sophia Ackerhans, Carsten Schultz· Current Directions in Biomed...· 0 citations
AI in clinical decision-making challenges liability frameworks grounded in subject–object dichotomies, linear causation, and atomised attribution. Drawing on relational responsibility, this article argues that the human–machine association, rather than the AI system or clinician alone, should be treated as the fundam...
Xi-Yi Chen· Asian journal of Law and Soc...· 0 citations
The growing integration of artificial intelligence (AI) in healthcare has transformed clinical decision-making. Nevertheless, doctors’ engagement with AI-based tools remains hindered, particularly due to trust-related concerns. The purpose of this study is to examine the factors influencing doctors’ engagement with AI-...
A. Seow, Shanthi Isparan, Jing-Jing Chang et al.· International Journal of Man...· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
