A generalist vision–language foundation model for diverse biomedical tasks
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Optimizing large language models (LLMs) for downstream use cases often involves the
customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama …
customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama …
Pre-training in medical data: A survey
Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …
part of a clinical trial program. There are many categories of such data, such as clinical …
Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks
Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …
real-world deployment and maintenance for biomedicine. At the same time, the growing …
[HTML][HTML] Integrating domain knowledge for biomedical text analysis into deep learning: A survey
The past decade has witnessed an explosion of textual information in the biomedical field.
Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision …
Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision …
BioInstruct: instruction tuning of large language models for biomedical natural language processing
Objectives To enhance the performance of large language models (LLMs) in biomedical
natural language processing (BioNLP) by introducing a domain-specific instruction dataset …
natural language processing (BioNLP) by introducing a domain-specific instruction dataset …
Parameter-efficient fine-tuning of llama for the clinical domain
Adapting pretrained language models to novel domains, such as clinical applications,
traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine …
traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine …
Clinical prompt learning with frozen language models
When the first transformer-based language models were published in the late 2010s,
pretraining with general text and then fine-tuning the model on a task-specific dataset often …
pretraining with general text and then fine-tuning the model on a task-specific dataset often …
[HTML][HTML] Learning to make rare and complex diagnoses with generative AI assistance: qualitative study of popular large language models
Background: Patients with rare and complex diseases often experience delayed diagnoses
and misdiagnoses because comprehensive knowledge about these diseases is limited to …
and misdiagnoses because comprehensive knowledge about these diseases is limited to …
Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes
and efficient utilization of resources. Accessibility of electronic health records (EHR) has …
and efficient utilization of resources. Accessibility of electronic health records (EHR) has …