Latest AI and machine learning research in medicolegal for healthcare professionals.
Ambient artificial intelligence scribes are being increasingly used in healthcare to improve efficiency and reduce provider clinical documentation burden, yet their performance across linguistically diverse patient populations is not well characterized. We conducted a retrospective analysis of 54,160 outpatient encounters within a U.S. safety net health system to evaluate the performance of an art...
Objectives Safety claims for ambient artificial intelligence (AI) scribes rest on automated judges that detect documentation errors and grade clinical risk. Expert reviewers are under-sensitive and disagree with one another, so no gold standard exists and validation cannot mean accuracy. We tested whether such judges are a defensible instrument: reproducible, within the envelope of expert disagree...
Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve...
Stigmatizing language in clinical documentation, which conveys negative stereotypes, attitudes, or judgments toward patients, is a recognized source o...
Thinking with images allows a multimodal model to compensate for limited perception by invoking visual tools through code. Yet the prevailing SFT-then...
Objective: To evaluate decision concordance between commercially available multimodal large language models (LLMs), resident doctors, and senior-surge...
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions lock...
Enterprise investment in generative artificial intelligence (AI) tripled in a single year to roughly US$37 billion, yet independent field research fin...
ICU trauma patients are clinically heterogeneous, and early mortality risk stratification may support monitoring and resource allocation. We developed...
Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. Howe...
Stigmatizing language in medical documentation may reflect and perpetuate bias, but its prevalence in obstetrics has not been systematically quantifie...
Background manipulation is a practical but under-specified image-forensics setting: the manipulated evidence can sit outside the salient foreground ob...
Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of...
Background. Clinical quality measurement often relies on manual abstraction of medical records, an approach that is costly, burdensome, and often infe...
Introduction: Cerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with...
Background. Early diagnosis and intervention are crucial in multiple sclerosis (MS), yet diagnostic delays are common. Large language models (LLMs) su...
ICU-to-ward transfers are high-risk transitions marked by information loss and burdensome handoff preparation. We developed PAUSE-Agents, a clinician-...
Background: Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on docu...
The African clawed frog Xenopus laevis is a widely utilized model organism in biomedical research; however, significant challenges in experimental rep...
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, of...