Health

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HealthEvidence-backed gain

Cervical cancer survival analysis by integrating multi-omics data through an adaptive weighted deep neural network

Accurately analyzing cervical cancer prognosis holds significant importance for enhancing clinical outcomes within the framework of precision medicine. Since single-omics data only capture a limited perspective of cervical cancer patients, integrating multiple types of omics data is essential to provide more comprehensive insights. However, capturing the key information in multi-omics data fusion remains challenging owing to the presence of a large number of redundant variables and a relatively small sample size…

Computational Biology and Chemistry

Evidence-backed problem

Beyond Autonomy: Ethical Considerations of AI in Substitute Decision-Making for Incapacitated Patients

Both readings

Clinical performance and practical limitations of large language models in respiratory medicine

Both readings

Reliable Detection, Unreliable Clinical Decision-making: Artificial Intelligence Models in Burn Assessment

Evidence-backed gain

Validation of generative artificial intelligence techniques for synthetic data generation in multiple sclerosis research: A comparison with real-world evidence from the Italian multiple sclerosis registry

Background: The use of generative artificial intelligence to create synthetic data is an emerging strategy to enhance real-world evidence. Objective: To assess the validity of artificial intelligence-generated synthetic data in replicating data collected in the Italian Multiple Sclerosis and Related Disorders Register for the evaluation of progression independent of relapse activity risk under early intensive treatment and escalation treatment strategies. Methods: Artificial intelligence generative models were t…

Evidence-backed problem

As US medical debt grows, AI will be used more to collect it

Medical debt is expected to grow in coming years as Republican-led health cuts come into force this winter. As it does, a changing world of medical billing and debt collection awaits patients: one where artificial intelligence increasingly listens, codes, bills and collects. Artificial intelligence takes notes in exam rooms, codes visits, sends out billing messages and powers AI agents who text patients in billing portals – all changing the debt-collection ecosystem. Medical debt will grow among “both the insure…

Evidence-backed gain

Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity

Background Retinopathy of prematurity (ROP) is one of the leading causes of childhood blindness globally, but particularly in middle-income countries, where neonatal survival has improved faster than access to specialist ophthalmic care. Although telemedicine and artificial intelligence (AI)-assisted screening show promise for improving access to ROP screening, their economic value within decentralised health systems remains uncertain. To explore this issue, we evaluated the cost-effectiveness of community-based…

Evidence-backed problem

Evaluation and Comparison of Large Language Models in Answering Frequently Asked Patient Questions on Glaucoma

Prcis: ChatGPT-o1 and DeepSeek-R1 demonstrated similar performance in answering frequently asked patient questions. However, limitations in reliability and high reading levels underscore the need for greater oversight and refinement to improve their role in patient education. Purpose: This study evaluated and compared the reliability and readability of responses generated by ChatGPT-o1 and DeepSeek-R1 to frequently asked patient questions (FAQs) on glaucoma. The findings aim to elucidate the utility of each mode…

Health

A Machine Learning Model to Predict Complications Following Head and Neck Microvascular Surgery

Objectives: Microvascular free tissue transfer is the primary reconstructive method in patients with complex defects of the head and neck. Defect site complications vary; incidence ranges from 15% to 70%. The use of machine learning via mutual information analysis has recently been described in the context of head and neck cancer, but this does not distinguish between causal relationships. The current study explores the first causal machine learning model to predict surgical outcomes in patients undergoing head…

Health

Voice-Enabled Virtual Patients for Interactive Training in Standardized Clinical Assessment: Mixed Methods Pilot Study

Background Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities. Traditional methods often fail to prepare trainees for the variability and complexity of real-world patient interactions, potentially impacting data quality in clinical trials. This paper introduces a novel approach to address this training gap using large language model (LLM)-based interview simulations. Objective This study aimed to develop and va…

Health

Diagnos Inc recalls an AI-enabled medical device

CARA (Computer Assisted Retinal Analysis), Product ID: +B543CARA0/$$+7V4.7.2/16D202111179 Recall reason: Retinal analysis system software has some features, based on artificial intelligence, that potentially lack appropriate market approval, so it is recommended to use a specific prior software version.

Health

Cost-Effectiveness of AI-Powered Decentralized Production for Advanced Therapies in the Hospital-Generated Chimeric Antigen Receptor T-Cell Therapy for Multiple Myeloma From a German Hospital Perspective: Early Health Technology Assessment Study

Background: Although effective, current chimeric antigen receptor T-cell (CAR-T) production methods-centralized, manual, and complex-are cost-intensive, time-consuming, and prone to variability. AIDPATH (AI-Powered Decentralized Production for Advanced Therapies in the Hospital) proposes a decentralized, automated alternative that integrates patient-specific data; optimizes resource use; and potentially improves cell viability, manufacturing efficiency, and patient outcomes. Objective: The aim of this study was…

Health

Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations

Background Periodontitis is one of the most prevalent yet preventable oral diseases, as indicated by multiple clinical and radiographic factors. As these factors are recorded in electronic health records (EHRs), their reuse offers opportunities for personalized risk assessment and targeted prevention. Predictive AI and traditional machine learning models support fragmented detection tasks but lack the integration of textual and imaging predictors. Emerging multimodal large language models (M-LLMs) show promise i…

Health

Algorithmic Guardrails for Echocardiography AI: Deep Feature Representations for Error Slicing and Selective LVEF Estimation

Background: Automated echocardiography analysis using artificial intelligence (AI) currently lacks effective guardrails or oversight mechanisms to ensure its safe and reliable deployment. Objectives: This study investigated whether deep feature representations can identify echocardiograms at risk of misinterpretation by echocardiography AI-based left ventricular ejection fraction (LVEF) estimation models. Methods: We retrospectively validated 2 state-of-the-art models for LVEF estimation, EchoNet (single-task mo…

Health

Utility of Augmented Reality Glasses With Waveguide Optics and Facial Recognition Technology for Patient Verification in a Simulated Outpatient Setting: Physician Questionnaire Survey Study

Background Augmented reality (AR) glasses with waveguide optics and facial recognition may enhance patient identification and clinical workflows. However, their usability among physicians has not yet been evaluated. Objective The objective of this study was to assess the usability and acceptability of prototype AR glasses with facial recognition in a simulated outpatient setting. Methods In May 2025, 14 urologists from a single institution tested prototype AR glasses developed by Cellid Inc. The device incorpora…