Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial intelligence (AI)-assisted diagnostic… Continue reading Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study
Tag: artificial-intelligence
Digital Therapeutics for Postoperative Rehabilitation after Arthroscopic Rotator Cuff Repair: A Single-Center, Assessor-Blinded, Randomized Pilot Study
Background: Home-based rehabilitation after arthroscopic rotator cuff repair (ARCR) often lacks objective monitoring. To address this limitation, we developed the ANAPA digital rehabilitation (DR) system, which includes a patient-facing mobile application for clinical efficacy evaluation (ANAPA ME) and a physician-facing measurement application for technical assessment (ANAPA PS). This study compared the safety and preliminary efficacy… Continue reading Digital Therapeutics for Postoperative Rehabilitation after Arthroscopic Rotator Cuff Repair: A Single-Center, Assessor-Blinded, Randomized Pilot Study
Translating artificial intelligence into clinical practice for gastrointestinal endoscopy: current applications and future perspectives
Artificial intelligence (AI) has emerged as a transformative tool in gastrointestinal (GI) endoscopy, addressing challenges in detection, diagnosis, and decision-making. In upper GI endoscopy, AI supports blind spot monitoring, Helicobacter pylori diagnosis, and the identification of premalignant and malignant lesions, with high accuracy and reduced miss rates. In lower GI endoscopy, computer-aided detection improves adenoma… Continue reading Translating artificial intelligence into clinical practice for gastrointestinal endoscopy: current applications and future perspectives
Voluntary use of LLM-powered virtual standardized patients and medical students’ interview performance: An observational study
Background: Large language model (#LLM )-powered Virtual Standardized Patients (VSPs) offer scalable practice opportunities for clinical interviewing, but their added value within established human Standardized Patient (SP) curricula remains unclear. This study examined how self-directed VSP engagement relates to interview performance on traditional SP assessments through the lens of self-regulated learning theory.Methods: We analyzed VSP… Continue reading Voluntary use of LLM-powered virtual standardized patients and medical students’ interview performance: An observational study
AI-based burn image assessment: Reliability and clinical error patterns of multimodal large language models in a repeated-inference study
Accurate assessment of #burn depth and total body surface area (TBSA) is critical for clinical decision-making; however, it remains subjective and prone to interobserver variability. Multimodal large language models (MLLMs) are increasingly encountered in clinical contexts, but whether these systems can reliably assess burn images remains unclear. We evaluated four MLLMs (GPT-5.4 Pro, Grok 4.1,… Continue reading AI-based burn image assessment: Reliability and clinical error patterns of multimodal large language models in a repeated-inference study
Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Background: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in #emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope… Continue reading Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Using randomization to compare #AI and expert-generated formative assessment questions in medical education
Background: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI… Continue reading Using randomization to compare #AI and expert-generated formative assessment questions in medical education
From Advice to Action — Real-World Behavior of Patients Using an Integrated Diagnostic Decision Support System for Navigating the Health Care System
Artificial intelligence (#AI )–powered digital front door tools are increasingly being used to guide patients to appropriate care and alleviate health care system pressure. However, most evaluations offer limited insight into stated care intent, real-world behavior, or care appropriateness.MethodsThe E-Health Self–Symptom Assessment as a Front Door and Facilitator of Care (ESSENCE) study was a prospective… Continue reading From Advice to Action — Real-World Behavior of Patients Using an Integrated Diagnostic Decision Support System for Navigating the Health Care System
Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice
Importance Large language models (#LLMs ) are increasingly integrated into health care applications; however, their vulnerability to prompt-injection attacks (ie, maliciously crafted inputs that manipulate an LLM’s behavior) capable of altering medical recommendations has not been systematically evaluated.Objective To evaluate the susceptibility of commercial LLMs to prompt-injection attacks that may induce unsafe clinical advice and… Continue reading Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice
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