Artificial Intelligence for Patient Support: Assessing Retrieval-Augmented Generation for Answering Postoperative Rhinoplasty Questions.

Background: While artificial intelligence (AI) is revolutionizing healthcare, inaccurate or incomplete information from pre-trained large language models (LLMs) like ChatGPT poses significant risks to patient safety. Retrieval-Augmented Generation (RAG) offers a promising solution by leveraging curated knowledge bases to enhance accuracy and reliability, especially in high-demand specialties like plastic surgery.

Objectives: This study evaluates the performance of RAG-enabled AI models in addressing postoperative rhinoplasty questions, aiming to assess their safety and identify necessary improvements for effective implementation into clinical care.

From Promise to Practice: Harnessing AI's Power to Transform Medicine.

Artificial intelligence (AI) is not merely a tool for the future of clinical medicine; it is already reshaping the landscape, challenging traditional paradigms, and expanding the horizons of what is achievable in healthcare [...

Optimal treatment time with systemic antimicrobial therapy in odontogenic infections affecting the jaws: a systematic review.

Objectives: This systematic review aimed to analyze the existing literature to determine the most effective and safe duration of antimicrobial treatment in odontogenic infections of the mandible, addressing a critical gap in clinical guidelines regarding optimal treatment duration.

Materials And Methods: A systematic review protocol was registered in PROSPERO (CRD42024551258), and a comprehensive search was conducted in databases including PubMed, Web of Science, Scopus, ScienceDirect, Embase, and Google Scholar for articles published up to June 16, 2024. Randomized clinical trials (RCTs) evaluating different durations of antimicrobial treatment were prioritized.

Effects of preparation design on the marginal and internal fit of CAD-CAM overlay restorations: A µCT evaluation.

Statement Of Problem: The effects of different finish line designs on the seating accuracy of partial indirect restorations are unclear.

Purpose: The purpose of this in vitro study was to evaluate the influence of different preparation designs on the marginal and internal discrepancy of lithium disilicate computer-aided design and computer-aided manufacture (CAD-CAM) partial indirect restorations before and after thermomechanical aging by using 3-dimensional (3D) microcomputed tomography (μCT).

Material And Methods: Forty human molars were divided according to the preparation design and their location relative to the tooth survey line: SO: rounded shoulder occlusal to the survey line; CO: chamfer occlusal to the survey line; SA: rounded shoulder apical to the survey line; CA: chamfer apical to the survey line (n=10).

Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition.

Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)-ChatGPT-4, ChatGPT-4o, and Gemini-and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.