Latest AI and machine learning research in risk management for healthcare professionals.
Unlearning an identity from a face-conditioned generator by redirecting its conditioning embedding can silently fail if the redirected output is still verified as the original person. We show that this failure depends on a controllable choice of how far the redirection target lies from the forget identity in recognition space, and that the most intuitive target, the nearest neighbour, is the one m...
Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent analysis in medical and scientific imaging. Corresponding image points are often observed with non-overlapping spectral sensitivities, leading to wavelength-dependent contrast changes, intensity inversions, and appearance shifts for which dense gro...
Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce ...
Medical vision-language models (VLMs) can appear reliable in-domain while failing when acquisition domain, paired supervision, or evaluation protocol ...
Systematic reviews, scoping reviews, mapping studies, and related evidence syntheses are increasingly difficult to conduct with fully manual workflows...
Offline reinforcement learning (RL) provides a promising framework for learning and evaluating treatment policies from logged clinical data, particula...
Anti-amyloid therapies and blood-based biomarkers are changing Alzheimer disease workups into a two-stage measurement workflow: screen broadly with ch...
Clinical records contain rich evidence about patient state, but converting that evidence into reliable, structured knowledge graphs remains difficult ...
Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to retain, an attribute to modify, a...
Person names are widely used as prompt variables in LLM evaluations of factuality, privacy leakage, bias and abstention, but when a name's evidential ...
Diagnostic errors, including misdiagnoses and delayed clinical diagnoses, could affect outcomes of a significant patient population, particularly indi...
The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling th...
Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires both eviden...
Selecting a small, diverse subset from a large candidate pool often means balancing several incompatible notions of diversity. In trademark curation, ...
Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is d...
Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else. We present a content-based video narration syst...
Frozen image embeddings from models such as CLIP are increasingly used to classify paintings by art-historical style, with high reported accuracy. We ...
Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with ...
Objective. A deep learning (DL) model was used to convert smartphone videos of a complete arch implant cast into 3D scans. The aim of current study wa...
Existing 3D facial-landmark methods localize points on visible skin, but whether CT-defined internal skeletal landmarks can be inferred from external ...