Aug 2026· Current Surgery Reports· Vol 14· 0 citations· 11 references
TL;DR
Large Language Models show potential in their diagnostic accuracy and consequent ability to reduce clinician burden, and may provide the greatest benefit when used to optimise referral quality at source, improving both clinician and potentially LLM triage downstream.
LLMs appear to have potential as supplementary information resources for clinicians in restorative dentistry; however, their clinical integration, impact on patient outcomes, and real-world usability remain to be established in future research.
Ebru İrem Teke, A. Borsöken· BMC Oral Health· 0 citations
OrL-HNS patients are largely familiar with LLMs and frequently use them, but their trust and confidence regarding health information provided by LLMs alone is limited, suggesting that clinician-led LLM use may be acceptable to many patients.
C. Buhr, A. Blaikie, Harry Smith et al.· Frontiers in Digital Health· 0 citations
This exploratory scenario-based study demonstrates model-specific, rather than universal, language effects on LLM performance in dental trauma management.
Although Grok 4 showed the highest diagnostic concordance, current LLMs should support, not replace, clinician judgment, and LLM performance varied across ICOP-based scenarios.
M. S. Şimşek, Enis Esen, M. Koparal· Cranio· 0 citations
The LLM showed offline potential for identifying cost-communication cues and generating clinician-facing preparation checklists for TKA, but content omissions, quality variation, safety risks, and substantive cross-run variability remained.
Zebing Ma, Liping Xue, Gong-Hui Jian et al.· Frontiers in Medicine· 0 citations
The finding that LLMs can distinguish urgent from nonurgent upper extremity conditions suggests that artificial intelligence tools could help reduce unnecessary use of high-cost emergency services, allowing those resources to be reserved for patients who require timely care.
Jefferson Hunter, David C. Ring, Prakash Jayakumar· Inquiry : a journal of medic...· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations 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.
