AIMC Topic: Natural Language Processing

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Integrating ChatGPT in Orthopedic Education for Medical Undergraduates: Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: ChatGPT is a natural language processing model developed by OpenAI, which can be iteratively updated and optimized to accommodate the changing and complex requirements of human verbal communication.

Machine learning and natural language processing in clinical trial eligibility criteria parsing: a scoping review.

Drug discovery today
Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent years have witnessed an explosion of powerful machine learning (ML) and natural language processing (NLP...

Natural language processing in dermatology: A systematic literature review and state of the art.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language.

Investigation of bias in the automated assessment of school violence.

Journal of biomedical informatics
OBJECTIVES: Natural language processing and machine learning have the potential to lead to biased predictions. We designed a novel Automated RIsk Assessment (ARIA) machine learning algorithm that assesses risk of violence and aggression in adolescent...

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

PloS one
An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due t...

Practical Evaluation of ChatGPT Performance for Radiology Report Generation.

Academic radiology
RATIONALE AND OBJECTIVES: The process of generating radiology reports is often time-consuming and labor-intensive, prone to incompleteness, heterogeneity, and errors. By employing natural language processing (NLP)-based techniques, this study explore...

FLAT: Fusing layer representations for more efficient transfer learning in NLP.

Neural networks : the official journal of the International Neural Network Society
Parameter efficient transfer learning (PETL) methods provide an efficient alternative for fine-tuning. However, typical PETL methods inject the same structures to all Pre-trained Language Model (PLM) layers and only use the final hidden states for do...

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing.

Journal of biomechanics
Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulat...

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.

Journal of biomedical semantics
BACKGROUND: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, ...

Role of Natural Language Processing in Automatic Detection of Unexpected Findings in Radiology Reports: A Comparative Study of RoBERTa, CNN, and ChatGPT.

Academic radiology
RATIONALE AND OBJECTIVES: Large Language Models can capture the context of radiological reports, offering high accuracy in detecting unexpected findings. We aim to fine-tune a Robustly Optimized BERTĀ PretrainingĀ Approach (RoBERTa) model for the autom...