The shaky foundations of large language models and foundation models for electronic health records
The success of foundation models such as ChatGPT and AlphaFold has spurred significant
interest in building similar models for electronic medical records (EMRs) to improve patient …
interest in building similar models for electronic medical records (EMRs) to improve patient …
Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …
the automation of physician tasks as well as enhancements in clinical capabilities and …
A guide to machine learning for biologists
The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …
use of machine learning in biology to build informative and predictive models of the …
[HTML][HTML] Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …
resulted in algorithms being adopted for resolving a variety of problems. However, this …
Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver
impressive performance in many clinical tasks. Large training cohorts, however, are often …
impressive performance in many clinical tasks. Large training cohorts, however, are often …
[HTML][HTML] Artificial intelligence in health care: bibliometric analysis
Background As a critical driving power to promote health care, the health care–related
artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis …
artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis …
A guide to deep learning in healthcare
Here we present deep-learning techniques for healthcare, centering our discussion on deep
learning in computer vision, natural language processing, reinforcement learning, and …
learning in computer vision, natural language processing, reinforcement learning, and …
BEHRT: transformer for electronic health records
Today, despite decades of developments in medicine and the growing interest in precision
healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs …
healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs …