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Reassessing AI in Medicine: Exploring the Capabilities of AI in Academic Abstract Synthesis
extract
J Med Internet Res 2024;26:e55920
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There is a growing volume of literature adopting NLP techniques to extract and analyze social media data for PHS including monitoring public sentiments and health behaviors, predicting a pandemic, and detecting misinformation [1,14-18]. However, there could be potential bias from using social media data due to selected data sets that could overlook underrepresented population groups (generalizability) or contain misinformation (validity) [19-21].
JMIR AI 2024;3:e55059
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Fundamental to the feasibility of both of these applications is the availability of clinical information, which, despite the plethora of raw data available in the EHR, can be nontrivial to extract in a computationally accessible format. This is particularly the case for information only accessible within unstructured data, such as clinical narratives, due to the intrinsic nature of human language.
JMIR Med Inform 2024;12:e49997
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Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study
Prominent examples of such applications include Robot Reviewer [15], Trial Streamer [16], Research Screener [7], Distiller SR [17], and Abstrackr [18], which are artificial intelligence models developed to extract information from scientific articles or abstracts to judge study quality and infer treatment effects. More specifically, Robot Reviewer (2016) was shown to have similar capabilities to assess the risk of bias assessment as a human reviewer, only differing by around 7% in accuracy [19].
J Med Internet Res 2024;26:e48996
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Reference 2: A study of machine-learning-based approaches to extract clinical entities and their assertionsextract
JMIR Med Inform 2023;11:e48933
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To extract clinical information from radiology reports, the Medical Language Extraction and Encoding system [11] and Radiology Analysis tool [12] have been developed. To detect clinical terms, these systems mainly use predefined dictionaries such as the Unified Medical Language System [13] and their customized dictionaries and apply some grammatical rules to present them in a structured format.
The major issues of these systems include the lack of coverage and scalability [14].
JMIR Med Inform 2023;11:e49041
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