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Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis

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Guxue Shan, Xiaonan Chen, Chen Wang, Li Liu, Yuanjing Gu, Huiping Jiang, Tingqi Shi

JMIR Med Inform 2025;13:e64963

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Guideline-Incorporated Large Language Model-Driven Evaluation of Medical Records Using MedCheckLLM

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Marc Cicero Schubert, Stella Soyka, Wolfgang Wick, Varun Venkataramani

JMIR Form Res 2025;9:e53335

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Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Both the raw audio data and the transcribed text content were processed to extract acoustic and NLP-based features from speech outputs. NLP and acoustic signal models were embedded in the backend part of the mobile app. Consistent with recent evidence, we assumed speech as verbal behavior, the spoken output of the mental system underlying the language [39]. Through speech recognition, acoustic and linguistic features were extracted.

Cristina Crocamo, Riccardo Matteo Cioni, Aurelia Canestro, Christian Nasti, Dario Palpella, Susanna Piacenti, Alessandra Bartoccetti, Martina Re, Valentina Simonetti, Chiara Barattieri di San Pietro, Maria Bulgheroni, Francesco Bartoli, Giuseppe Carrà

JMIR Form Res 2025;9:e65555

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Large Language Models in Biochemistry Education: Comparative Evaluation of Performance

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Olena Bolgova, Inna Shypilova, Volodymyr Mavrych

JMIR Med Educ 2025;11:e67244

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Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study

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].

Denise Lee, Akhil Vaid, Kartikeya M Menon, Robert Freeman, David S Matteson, Michael L Marin, Girish N Nadkarni

JMIR Form Res 2025;9:e64544

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Insights on the Side Effects of Female Contraceptive Products From Online Drug Reviews: Natural Language Processing–Based Content Analysis

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?!”

Nicole Groene, Audrey Nickel, Amanda E Rohn

JMIR AI 2025;4:e68809

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Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis

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.

Brian C Coleman, Kelsey L Corcoran, Cynthia A Brandt, Joseph L Goulet, Stephen L Luther, Anthony J Lisi

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].

David Chen, Saif Addeen Alnassar, Kate Elizabeth Avison, Ryan S Huang, Srinivas Raman

JMIR Cancer 2025;11:e65984

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