Latest AI and machine learning research in health policy for healthcare professionals.
Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable amount of data, facilitating proper knowledge extraction and generalization. Access to such extensive resources may be hindered due to the ...
Using the MIMIC-IV database (1,500-7,000 patients), we assess the robustness of healthcare machine learning models under controlled data quality (DQ) degradations applied to training or test data across five dimensions. Model performance declined with increasing degradation, with the largest losses observed when degradations occurred at inference time. Completeness, coherence, and validity were th...
Machine learning (ML) has great potential in healthcare, especially with large structured data. Routine health insurance claims (HIC) data are a valua...
This paper investigates healthcare policymakers' and professionals' perceptions of Artificial Intelligence (AI) Machine Learning (ML) Decision Tree Mo...
Generative artificial intelligence (GAI) offers new opportunities to improve healthcare efficiency and quality, yet its adoption raises ethical and so...
The integration of diverse healthcare data into unified data lake infrastructures is a promising way to support research and clinical decision-making....
Mapping local clinical concepts to standardized terminologies such as SNOMED CT is essential for semantic interoperability and large-scale research, b...
The integration of Generative Artificial Intelligence (GenAI) into eHealth represents a transformative shift in the design, development, and deploymen...
The scarcity of high-quality clinical datasets and strict privacy regulations remain major barriers to developing robust predictive models in healthca...
Federated Learning enables collaborative AI development in healthcare without sharing patient data, addressing privacy and regulatory constraints like...
Artificial intelligence (AI) is considered to have great potential to support healthcare by improving diagnostic accuracy, optimizing workflows, and p...
As the United States approaches its 250th anniversary, this perspective examines the intertwined evolution of medicine and democracy as mutually reinf...
BACKGROUND: Artificial intelligence (AI) and clinical informatics (CI) are strategic priorities for UK ophthalmology, with the Royal College of Ophtha...
BACKGROUND: Cancer is a leading cause of death in both the USA and India. During Prime Minister Narendra Modi's State Visit to Washington, DC in June ...
Growing misuse of, and unequal access to, AI tools requires universities to rethink how they evaluate learning.
BACKGROUND: Data quality is the degree to which data are fit for their intended purpose and is described using quality dimensions. The increased use o...
BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The complexity of guideline-concordant care and un...
BACKGROUND: De-escalation after transoral surgery (TOS) for HPV-related oropharyngeal squamous cell carcinoma (OPSCC) requires accurate risk stratific...
Healthcare systems in the United States are now mandated to provide patients with immediate access to medical results. Access to complex results prior...
Over the past two decades, major advances in observational techniques and modeling capabilities have enabled the development of air pollution data fus...