Latest AI and machine learning research in surveillance for healthcare professionals.
Background Outcome after stroke varies according to stroke subtype by location, but healthcare systems data studies do not include subtyping information. We linked natural language processing (NLP) of brain imaging reports to routinely collected data to estimate risk of death and other outcomes after stroke subtypes in a nationwide dataset. Methods We applied a previously validated NLP algorithm t...
Background Randomized controlled trials (RCTs) often have incomplete methods reporting despite widespread adoption of the CONSORT guideline. The editorial process is supposed to detect these shortcomings and request clarifications from authors, which is time-consuming. We developed an LLM-based CONSORT Rohe Nordberg Report that highlights which CONSORT items appear fully or partially reported and ...
The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, heal...
This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International ...
Physical adversarial attacks are increasingly studied in settings that resemble deployed surveillance systems rather than isolated image benchmarks. I...
We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response...
Extracting vehicle information from surveillance images is essential for intelligent transportation systems, enabling applications such as traffic mon...
During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although ...
Background: Secure text messages (TMs) exchanged among interdisciplinary care teams in nursing homes (NHs) contain clinical information that aligns wi...
Background: The integration of artificial intelligence (AI) into clinical practice holds transformative potential for healthcare in West Africa, but s...
Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the...
The rapid advancement of AI research automation systems--including AI Scientist, data-to-paper, and Agent Laboratory--has demonstrated the potential f...
Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammograph...
Background: The FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance; however, its utility is constrain...
Image restoration, the recovery of clean images from degraded measurements, has applications in various domains like surveillance, defense, and medica...
Digital health technologies, including machine learning (ML), are transforming infectious disease management, however ML models for HIV care have been...
Background Chronic subdural hematoma (cSDH) recurrence requiring reoperation occurs in 5-33% of cases, representing a substantial clinical and economi...
Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartm...
Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incid...
Despite decades of work, surveillance still struggles to find specific targets across long, multi-camera video. Prior methods -- tracking pipelines, C...