Neural Network Assisted Pathology Case Identification

Cheng, Jerome

doi:10.1016/j.jpi.2022.100008

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Information extraction from clinical notes and free-text reports is challenging using traditional methods; regular expressions [27] or multiple text matching rules have to be written, and these rules can be error-prone, especially when unstructured free text is involved. Determining the presence or absence of cancer within a report is prone to error, as looking for cases with "carcinoma" could retrieve cases of "negative for carcinoma" being phrased in different ways [28]. This is one task LLMs have proven to be good at, since they are trained to learn the meaning of words, in addition to looking for the presence or absence of a particular word.…”

Section: Information Extractionmentioning

confidence: 99%

Applications of Large Language Models in Pathology

Cheng

2024

Bioengineering

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Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology.

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Section: Information Extractionmentioning

confidence: 99%

Applications of Large Language Models in Pathology

Cheng

2024

Bioengineering

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Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology

Zhang

Weng

et al. 2023

Diagnostics

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In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.

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Neural Network Assisted Pathology Case Identification

Cited by 2 publications

References 8 publications

Applications of Large Language Models in Pathology

Applications of Large Language Models in Pathology

Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology

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