AIMC Topic: Natural Language Processing

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Use of Artificial Intelligence in Manuscript Preparation: AI as a Coauthor.

The International journal of periodontics & restorative dentistry
The use of artificial intelligence (AI) is rapidly expanding. While it comes with some drawbacks, it also offers numerous advantages. One significant application of AI is chatbots, which utilize natural language processing and machine learning to pro...

Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models.

Studies in health technology and informatics
BACKGROUND: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-prone.

Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification.

Studies in health technology and informatics
BACKGROUND: Large Language Models (LLMs) offer promising applications in oncology pathology report classification, improving efficiency, accuracy, and automation. However, the use of real patient data is restricted due to legal and ethical concerns, ...

Exploring the Potential of Non-Proprietary Language Models for Analysing Patient-Reported Experiences.

Studies in health technology and informatics
Large language models (LLMs) are increasingly being explored for various applications in medical language processing. Due to data privacy issues, it is recommended to apply non-proprietary models that can be run locally. Therefore, this study aims to...

Leveraging LLMs to Understand Narratives in MAUDE Reports.

Studies in health technology and informatics
Interest in using the MAUDE database to investigate adverse events linked to medical devices has been growing. Yet, the narrative sections of these reports remain largely unexplored, leaving valuable insights unutilized and creating an incomplete und...

GPT-4 in Clinical Practice: Assessing Its Capability for Symptom Extraction from Cancer Patient Notes.

Studies in health technology and informatics
Accurate extraction of patient symptoms and signs from clinical notes is essential for effective diagnosis, treatment planning, and research. In this study, we evaluate the capability of GPT-4, specifically GPT-4o, in extracting symptoms and signs fr...

Evaluation of the Performance of a Large Language Model to Extract Signs and Symptoms from Clinical Notes.

Studies in health technology and informatics
Large language models (LLMs) have increasingly been used to extract critical information from unstructured clinical notes, which often include important details not captured in the structured sections of electronic health records (EHRs). This study a...

Leveraging Large Language Models for Sentiment Analysis in Educational Contexts.

Studies in health technology and informatics
This short communication presents preliminary findings on the application of Large Language Models (LLMs) for sentiment analysis in educational settings. By analyzing qualitative descriptions derived from student reports, we aimed to assess students'...

Comparing Emotional Valence from Human Quantitative Ratings and Qualitative Narrative Data on Using Artificial Intelligence to Reduce Caregiving Disparity.

Studies in health technology and informatics
We compared emotional valence scores as determined via machine vs human ratings from a survey conducted from April to May 2024 on perceived attitudes on the use of artificial intelligence (AI) for African American family caregivers of persons with Al...

Automated identification of fall-related injuries in unstructured clinical notes.

American journal of epidemiology
Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language process...