Latest AI and machine learning research in health policy for healthcare professionals.
When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare biases. Although many definitions of fairness exist, we focus on path-specific causal fairness, which allows us to better consider the social and medical contexts in which biases occur (e.g., direct discrimination by a c...
The development of machine learning (ML) methods has made quantum chemistry (QC) calculations more accessible by reducing the computational cost incurred in conventional QC methods. This has since been translated into the overhead cost of generating training data. Increased work in reducing the cost of generating training data resulted in the development of Δ-ML and multifidelity machine learning ...
Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Alt...
The critical period for visual function and ocular structure development occurs from 0 to 6 years of age, making standardized eye care and vision scre...
Advances in low-communication training algorithms are enabling a shift from centralised model training to compute setups that are either distributed...
Search engines have become the gateway to information, products, and services, including those concerning healthcare. Access to reproductive health ...
Wallets are access points for the digital economys value creation. Wallets for blockchains store the end-users cryptographic keys for administrating...
Cost models in healthcare research must balance interpretability, accuracy, and parameter consistency. However, interpretable models often struggle ...
Vaccination plays a vital role in global public health, yet healthcare professionals often struggle to access immunization guidelines quickly and ef...
Property graphs are widely used in domains such as healthcare, finance, and social networks, but they often contain errors due to inconsistencies, m...
Domain adaptation has become a widely adopted approach in machine learning due to the high costs associated with labeling data. It is typically appl...
Developed nations are undergoing a profound demographic transformation, characterized by rapidly aging populations and declining birth rates. This d...
Background: Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on adminis...
Software systems must comply with legal regulations, which is a resource-intensive task, particularly for small organizations and startups lacking d...
With the widespread adoption of large language models (LLMs) in practical applications, selecting an appropriate model requires balancing not only p...
Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control face...
The complexity of mental healthcare billing enables anomalies, including fraud. While machine learning methods have been applied to anomaly detectio...
Emerging low-altitude economy networks (LAENets) require agile and privacy-preserving resource control under dynamic agent mobility and limited infr...
With the booming development of generative artificial intelligence (GAI), semantic communication (SemCom) has emerged as a new paradigm for reliable...
Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic ul...