Latest AI and machine learning research in health policy for healthcare professionals.
Recent advancements in reinforcement learning (RL) have achieved great success in fine-tuning diffusion-based generative models. However, fine-tuning continuous flow-based generative models to align with arbitrary user-defined reward functions remains challenging, particularly due to issues such as policy collapse from overoptimization and the prohibitively high computational cost of likelihoods...
Access to diverse, high-quality datasets is crucial for machine learning model performance, yet data sharing remains limited by privacy concerns and competitive interests, particularly in regulated domains like healthcare. This dynamic especially disadvantages smaller organizations that lack resources to purchase data or negotiate favorable sharing agreements. We present SecureKL, a privacy-pres...
The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living a...
Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from user...
Digital healthcare infrastructure is crucial for global medical service delivery. Egypt faces EHR adoption barriers: only 314 hospitals had such sys...
Consistency models (CMs) have shown promise in the efficient generation of both image and text. This raises the natural question of whether we can l...
Guidance in image generation steers models towards higher-quality or more targeted outputs, typically achieved in Diffusion Models (DMs) via Classif...
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, ...
The COVID-19 pandemic has accelerated the adoption of telemedicine and patient messaging through electronic medical portals (patient medical advice ...
Following recent advancements in computer-aided detection and diagnosis systems for colonoscopy, the automated reporting of colonoscopy procedures i...
This paper reports on the results from a pilot study investigating the impact of automatic speech recognition (ASR) technology on interpreting quali...
Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication ...
Artificial Intelligence (AI) advancement is heavily dependent on access to large-scale, high-quality training data. However, in specialized domains ...
It is widely believed that outsourcing cognitive work to AI boosts immediate productivity at the expense of long-term human capital development. An ...
Traditionally, query optimizers rely on cost models to choose the best execution plan from several candidates, making precise cost estimates critica...
The emergence of foundational models has greatly improved performance across various downstream tasks, with fine-tuning often yielding even better r...
Independent algorithm audits hold the promise of bringing accountability to automated decision-making. However, third-party audits are often hindere...
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sec...
Inter-organisational data exchange is regulated by norms originating from sources ranging from (inter)national laws, to processing agreements, and i...
Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete ...