Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
With the development of Multimodal Large Language Models (MLLMs), numerous outstanding accomplishments have emerged within the open-source community. Due to the complexity of creating and training multimodal data pairs, it is still a computational and time-consuming process to build powerful MLLMs. In this work, we introduce Capybara-OMNI, an MLLM that trains in a lightweight and efficient manne...
Privacy Masking is a critical concept under data privacy involving anonymization and de-anonymization of personally identifiable information (PII). Privacy masking techniques rely on Named Entity Recognition (NER) approaches under NLP support in identifying and classifying named entities in each text. NER approaches, however, have several limitations including (a) content sensitivity including a...
This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of cl...
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that fail...
This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance wit...
We argue that there is a need for Accessibility to be represented in several important domains: - Capitalize on the new capabilities AI provides -...
Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by decomposing complex problems into step-by-step solutions, improving perfor...
Massively multiplayer online games (MMOGs) can foster social interaction and relationship formation, but they pose specific privacy and safety chall...
The generation of incorrect images, such as depictions of people of color in Nazi-era uniforms by Gemini, frustrated users and harmed Google's reput...
In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate inf...
Despite progresses in data engineering, there are areas with limited consistencies across data validation and documentation procedures causing confu...
Problem-solving therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues by guiding the...
Several clinical prediction models have been developed using electronic health records data to help inform decisions about HIV pre-exposure prophylaxi...
Randomized controlled trials (RCTs) can produce valid estimates of the benefits and harms of therapeutic interventions. However, incomplete reporting ...
Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients wi...
The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engage...
Programming deep brain stimulation (DBS) of the subthalamic nucleus for optimal symptom control in Parkinson’s Disease (PD) requires time and trained ...
Advancements in artificial intelligence (AI) are revolutionising the healthcare sector, but challenges exist in AI adoption and its long-term use. Thi...
Artificial intelligence (AI) technologies are increasingly being integrated into mental health interventions, but their impact on user experience and ...
Target Trial Emulation (TTE) has emerged as a rigorous framework for causal inference using observational data, but its application in hypertension re...