In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM).

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Machine learning (ML) has the potential to transform oncology and, more broadly, medicine. 1 The introduction of ML in health care has been enabled by the digitization of patient data, including the ...

Data integration is a prerequisite for building analytics and AI applications. In healthcare, it seems that NLP is quite important, too.

Artificial intelligence and machine learning now have an opportunity to provide positive value and impact in the life sciences industry.

Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality ...

Controlling machine learning in a finance environment requires stakeholders' commitment to creating a strong ethical foundation.

The goal of implementing artificial intelligence and machine learning in clinical research is not to replace humans with digital tools but to increase their productivity.

Machine learning is a tremendous resource for researchers. Yet not understanding the limitations of data and algorithms can lead to erroneous conclusions.