Latest AI and machine learning research in medicolegal for healthcare professionals.
OBJECTIVE: Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introduces a novel method for automatically detecting such language to improve healthcare documentation practices. MATERIALS AND METHODS: We developed a multi-stage transfer learning framework integrating semantic, syntactic, and task adaptation using three...
OBJECTIVES: To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center, as well as how ambient transcription reshapes clinicians' perceptions of their work. MATERIALS AND METHODS: We conducted semistructured interviews with clinicians who participated in an ambient scribe pilot (n = 8) and the initial enterprise rollou...
BACKGROUND: Clinical documentation is a major contributor to physician burnout, and artificial intelligence (AI) scribes are increasingly being adopte...
BACKGROUND: The incidence of cancer continues to increase, and cancer patients still suffer from a range of burdens, leading to decreased quality of l...
INTRODUCTION: While current blood-based biomarkers for Alzheimer's disease (AD) are effective for determining amyloid beta (Aβ) pathology positivity/n...
Respiratory diseases remain a major global health burden, motivating the need for improved experimental lung models that capture both anatomical geome...
Atmospheric wet deposition represents a key pathway linking atmospheric pollution to terrestrial ecosystems, with its chemical composition and deposit...
This article analyzes how large language models (LLMs) and generative artificial intelligence (GenAI) are reshaping dental education and practice. In ...
Atherosclerosis (AS), a chronic inflammatory process driven largely by macrophage-mediated plaque formation, remains poorly understood in mitochondria...
INTRODUCTION: Antimicrobial resistance (AMR) remains one of the greatest threats to global health, requiring innovative approaches to antibiotic disco...
Access to information about fouling in the membrane modules can provide critical insights to advance fundamental understanding and develop effective s...
BACKGROUND: The use and standardization of innovative artificial intelligence (AI) tools continue to grow and have the potential to enhance the field ...
OBJECTIVE: Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the...
Innovation in biomaterials has brought both breakthroughs and challenges in medicine, as implant materials have become increasingly multifunctional an...
Artificial intelligence (AI) is increasingly integrated into plastic and reconstructive surgery. It supports preoperative prediction and imaging analy...
Nose-to-brain drug delivery via nasal sprays is severely limited by low olfactory deposition. In this study, we integrate machine learning with high-t...
BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease s...
BACKGROUND: Medical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulator-approved ...
Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction w...
Artificial intelligence (AI) is reshaping the paradigm of clinical surgical field at an unprecedented pace, demonstrating immense potential in areas s...