e.g. mhealth
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Skip search results from other journals and go to results- 58 Journal of Medical Internet Research
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Large Language Models in Biochemistry Education: Comparative Evaluation of Performance
nlp
JMIR Med Educ 2025;11:e67244
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Advances in natural language processing (NLP) techniques have sought to address challenges in the efficiency and accuracy of data abstraction. Efforts have included applying NLP methods to extract pain scores in patients with cancer undergoing radiation [5], classify metastatic phenotypes from radiology reports of patients with colorectal cancer [6], and identify recurrence status in patients with hepatocellular carcinoma [7].
JMIR Form Res 2025;9:e64544
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To analyze information shared and consumed on social media, natural language processing (NLP) is used due to its capacity to analyze nonstructured, textual data.
For example, Pleasants et al [28] used NLP to study posts related to birth control on the US platform Reddit and found that “Side Effects!?” is the most common flair, a tag that users can attach to their post to categorize the content. Furthermore, “Experience” and “Side Effects?!”
JMIR AI 2025;4:e68809
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Natural language processing (NLP) of clinic text notes is one promising solution to extracting care quality data from unstructured text [9], with previous studies focusing on pain care quality in primary care and chiropractic care settings [10,11]. Use cases for NLP across other clinical domains highlight its potential utility in information extraction and analysis tasks.
JMIR Med Inform 2025;13:e66466
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Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review
To address this task of extracting key attributes from unstructured clinical text, natural language processing (NLP) methods have classically applied rule-based and machine-learning methods to identify important entities in text and categorize them based on categories of interest [2].
JMIR Cancer 2025;11:e65984
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The NLP algorithm flagged typographical errors in dictated notes that human reviewers overlooked.
To ensure consistency in the gold standard corpus used for training the algorithm, all discrepancies among reviewers were resolved through senior adjudication. When ambiguities were identified in a case, they were escalated to the senior author, who conducted a thorough review of the full case file.
JMIR Med Inform 2025;13:e64705
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