This extensive literature review investigates the integration of Machine Learning (ML) into
the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The …

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural
language processing (NLP) and machine learning (ML), such as the development of models …

Simple Summary Obtaining and structuring information about the characteristics, treatments,
and outcomes of people living with cancer for research purposes is difficult and resource …

Objectives As current health technology assessment (HTA) frameworks do not provide
specific guidance on the assessment of medical artificial intelligence (AI), this study aimed to …

PURPOSE Electronic health record (EHR)–based real-world data (RWD) are integral to
oncology research, and understanding fitness for use is critical for data users. Complexity of …

Abstract This ISPOR Good Practices report provides a framework for assessing the suitability
of electronic health records data for use in health technology assessments (HTAs). Although …

Adverse drug events (ADEs) are common in clinical practice and can cause significant harm
to patients and increase resource use. Natural language processing (NLP) has been …

Objective To assess the current real-world applications of machine learning (ML) and
artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that …

PURPOSE Real-world data (RWD) derived from electronic health records (EHRs) are often
used to understand population-level relationships between patient characteristics and …

The transformative integration of Machine Learning (ML) for Artificial General Intelligence
(AGI)-enhanced clinical imaging diagnostics, is itself in development. In brain tumor …