State Required CME

Post-quantum cognitive zero trust architecture for healthcare IoT devices.

Healthcare IoT systems increasingly rely on interconnected, resource-constrained devices that are vulnerable to both classical and emerging quantum-enabled cyber threats, but introduced heightened cybersecurity risks, particularly from emerging quantum computing threats that can break conventional encryption such as RSA and ECC. This study addresses the urgent need to secure resource-constrained h...

Enhancing healthcare information security through VAE-driven anomaly detection in EHR access patterns.

HIPAA breaches and unauthorized access to Electronic Health Records (EHRs) have been growing more likely due to the sudden digitalization of the healthcare sector. High endurance, privacy-based security practices have never been more in demand as hospitals and other medical facilities of this type have clung to the electronic system. The given study considers this problem by suggesting an anomaly ...

Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.

Federated Learning enables collaborative AI development in healthcare without sharing patient data, addressing privacy and regulatory constraints like...

Teaching Applied Artificial Intelligence in Health Schools: An Example from Rouen and Nice Universities in France.

This SaNuRN initiative developed a teaching module on applied AI for health residents, fellows, and CME professionals, responding to the French Minist...

Reduction of Electronic Medical Record Documentation Time and Burden Using an Artificial Intelligence Scribe Tool in an Orthopaedic Clinic.

INTRODUCTION: With an aging population in the United States, the demand for joint arthroplasty procedures continues to rise. As patient volumes increa...

HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.

The process of migration of IoMT systems in healthcare into post-quantum cryptographic systems is expected to be a gradual one. Here, existing ECC-bas...

Multi-modal personalized federated learning with adaptive differential privacy for medical image classification and a privacy-preserving approach.

Deep learning on medical images classification intervention needs to use large data on multi-institutional datasets but privacy laws inhibit sharing o...

Using large language models to identify geriatric assessment domains in patients with advanced cancer: A feasibility study.

INTRODUCTION: In older adults with cancer, geriatric assessment (GA) can improve care quality. In-person assessment may not be feasible for all patien...

Using Large Language Models to Identify Patient-Oncologist Communication Domains: A Feasibility Study.

BACKGROUND: The American Society of Clinical Oncology (ASCO) convened a multidisciplinary panel in 2017, resulting in patient-oncologist communication...

Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.

BACKGROUND: Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-w...

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

BACKGROUND: Poor cardiac MR image quality can prompt repeat examinations and hinder clinical decision-making. PURPOSE: To evaluate whether pre-imaging...

Legal and Ethical Considerations for Translating Federated Learning into Cross-Border Healthcare Innovation.

Federated Learning (FL) offers a privacy-enhancing architecture for training artificial intelligence on decentralized healthcare data, yet the prevail...

Feasibility of a Large Language Model Chatbot to Support Parental Understanding in the PICU.

OBJECTIVES: To evaluate the feasibility of a large language model (LLM)-based chatbot for answering parental questions in the PICU and inform design o...

A Pilot Study to Evaluate Artificial Intelligence-Driven Early Retrieval of Medical Histories in the Emergency Department.

STUDY OBJECTIVE: To assess the feasibility and acceptability of using ChatGPT to obtain histories of present illnesses directly from patients or careg...

AI-Powered Documentation for Mental Health Providers: Retrospective Observational Mixed Methods Study.

BACKGROUND: Mental health providers (MHPs) face a significant administrative burden from documentation, which can contribute to burnout and reduce tim...

Using Data and Artificial Intelligence to Improve Care Pathways of Older People Experiencing Falls and Frailty: Opportunities, Challenges, and Practical Considerations for Clinicians.

Older people living with falls and frailty are common in emergency attendances, admissions and functional decline. Artificial intelligence (AI) and ma...

Artificial intelligence in neuropsychological practice-tool selection, privacy, and billing guidance.

Objective: AI is rapidly transforming healthcare, yet its integration into clinical neuropsychology remains limited and uneven. This paper explores th...

A privacy-preserving multi-user retrieval system for multimodal artificial intelligence.

Large language models (LLMs) are integral to cloud-based AI applications, offering robust capabilities for multimodal data processing and retrieval. H...

Defining Physician-AI Collaboration in Pulmonary and Critical Care Medicine: Concepts and Illustrative Examples for Clinical Reasoning.

The exponential growth of medical data and complexity in Pulmonary and Critical Care Medicine (PCCM) necessitates a paradigm shift in clinical reasoni...

Observer: creation of a novel multimodal dataset for outpatient care research.

OBJECTIVE: To support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health...