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339 stories · page 13 of 23

Evidence-backed gain

Multispecialty Dental EMRs from Chairside Audio: An Exploratory Study

The objective of this study was to develop and internally evaluate a modular large language model (LLM) system for generating standardized electronic medical records (EMRs) from dental chairside consultations under conditions of acoustic interference and specialty-specific heterogeneity. We built a controllable pipeline integrating multistage audio enhancement and local automatic speech recognition with a cascaded LLM generator. A baseline end-to-end system (system 1) was compared with an evidence-enhanced syste…

Journal of Dental Research · Health

Multispecialty Dental EMRs from Chairside Audio: An Exploratory Study

Evidence-backed gain

Epigenomics-Guided Multi-Omics Integration Uncovers a Lipid-Metabolic Signature with Translational Utility in Bladder Cancer

Background: Bladder cancer (BLCA) exhibits marked heterogeneity, and current classifiers provide limited guidance for prognosis or treatment. Because epigenetic reprogramming and metabolic rewiring jointly shape BLCA biology, we sought to identify epigenomically informed biomarkers with functional relevance. Methods: Epigenome (genome-wide promoter DNA methylation) and matched transcriptome (RNA sequencing) profiles from tumor and adjacent normal samples were integrated to identify genes with concordant differen…

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Both readings

Ultra-widefield color fundus images and artificial intelligence for diagnosis of diabetic retinopathy: A systematic review and meta-analysis

Ultra-widefield (UWF) fundus cameras capture a larger retinal area without pupil dilation. We summarized evidence and diagnostic performance of artificial intelligence (AI)-driven diabetic retinopathy (DR) assessments using UWF images (UWFIs). We searched PubMed, Scopus, the Cochrane Library, and Web of Science to February 9, 2025, for studies evaluating DR using UWFIs and AI analyses. We followed the PRISMA guidelines and assessed study quality using the Joanna Briggs Institute Critical Appraisal Checklist for…

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Evidence-backed gain

Clinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study

Distinguishing vitiligo from postinflammatory hypopigmentation (PIH) is clinically challenging because both conditions may present with similar depigmented lesions. Although deep learning has shown strong potential for dermatologic image classification, limited interpretability remains a barrier to clinical adoption. This study aimed to develop an interpretable deep learning framework for accurate differentiation between vitiligo and PIH using a lightweight convolutional neural network and an ensemble of explain…

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Evidence-backed gain

Persona-Driven Data Augmentation for Disease Name Recognition Across Rare and General Disease Corpora: Comparative Evaluation Study

Medical information extraction requires automatically identifying disease names and related terms in text. This task, known as named entity recognition (NER), relies on expert-annotated data that are costly to produce and often available only in limited quantities. Data augmentation (DA) aims to expand available training data; however, standard techniques such as synonym replacement and back-translation may introduce inappropriate substitutions or fail to preserve entity-label alignment, which is critical for se…

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Evidence-backed gain

The single-cell atlas of programmed cell death signature: A machine learning-based prognostic framework in breast cancer

Breast cancer remains a leading cause of cancer-related mortality in women, and current prognostic models are suboptimal. The transcriptomic role of programmed cell death (PCD) in breast cancer progression is not fully understood. Here, we integrated single-cell RNA sequencing data from breast tumors with nine bulk transcriptomic cohorts to systematically analyze 19 PCD modalities. Using a machine learning framework incorporating 14 algorithms, we constructed a prognostic signature, with a ridge regression-based…

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Evidence-backed gain

Prognostic Significance of Cell-Free DNA Derived 5-Hydroxymethylcytosine Signatures in Newly Diagnosed Multiple Myeloma

While survival outcomes in multiple myeloma (MM) have improved with contemporary combination therapies, predicting disease trajectories for individual patients at diagnosis remains a significant challenge. We investigate the prognostic value of a noninvasive biomarker-cell-free DNA (cfDNA)‑derived 5-hydroxymethylcytosine (5hmC) signature-in newly diagnosed MM, aiming to improve risk stratification at diagnosis. In this prospective cohort study, 321 patients with newly diagnosed MM were enrolled between 2010 and…

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks…

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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency

Background and Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote m…

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FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and…

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AI image generation technology in ophthalmology: Use, misuse and future applications

BACKGROUND: AI-powered image generation technology holds the potential to reshape medical practice, yet it remains an unfamiliar technology for both medical researchers and clinicians alike. Given the adoption of this technology relies on clinician understanding and acceptance, we sought to demystify its use in ophthalmology. To this end, we present a literature review on image generation technology in ophthalmology, examining both its theoretical applications and future role in clinical practice. METHODS: First…

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AI image generation technology in ophthalmology: Use, misuse and future applications

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

The Prediction model Risk Of Bias ASsessment Tool (PROBAST) is used to assess the quality, risk of bias, and applicability of prediction models or algorithms and of prediction model/algorithm studies. Since PROBAST’s introduction in 2019, much progress has been made in the methodology for prediction modelling and in the use of artificial intelligence, including machine learning, techniques. An update to PROBAST-2019 is thus needed. This article describes the development of PROBAST+AI. PROBAST+AI consists of two…

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PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Machine learning in point-of-care testing: innovations, challenges, and opportunities

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. Thi…

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Machine learning in point-of-care testing: innovations, challenges, and opportunities

Towards conversational diagnostic artificial intelligence

Abstract At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust 1,2 . Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self…

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Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military

Conducted in challenging environments such as disaster or conflict areas, operational medicine presents unique challenges for the delivery of efficient and quality healthcare. It exposes first responders and medical personnel to many unexpected health risks and dangerous situations. To tackle these issues, artificial intelligence (AI) has been progressively incorporated into operational medicine, both on the front lines and also more recently in support roles. The ability of AI to rapidly analyze high-dimensiona…

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Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military