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Authors’ Reply: Citation Accuracy Challenges Posed by Large Language Models
We also propose another solution aimed at fundamentally reducing citation errors: the development of “Reference-Accurate” academic LLM by major global publishers. Leading journals could develop their own specialized LLM, trained exclusively on rigorously verified academic literature from robust databases. This targeted training would ensure that every generated reference is accurate and directly traceable to published work.
JMIR Med Educ 2025;11:e73698
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Citation Accuracy Challenges Posed by Large Language Models
Developers must continuously improve the LLM technology and algorithms, users must increase their awareness and critical evaluation skills while using LLMs, and academic institutions must strengthen the management and education in academic practices. Only through these efforts can we ensure that LLMs play a positive role in academic research and promote the dissemination and progress of knowledge.
JMIR Med Educ 2025;11:e72998
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Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review
The emergence of several recent pilot studies of LLM-enabled data extraction prompts the need for a scoping review to map the current landscape, including definitions, frameworks, and future directions for this novel tool in clinical data extraction. This review seeks to address this gap in the literature by characterizing primary research articles that evaluated an LLM tool applied to data extraction from unstructured clinical text into structured data.
JMIR Cancer 2025;11:e65984
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The AI Reviewer: Evaluating AI’s Role in Citation Screening for Streamlined Systematic Reviews
As we only ran each citation through a given LLM once, multiple runs or “prompt engineering” strategies could yield more consistent or refined outcomes when evaluating LLMs. Nonetheless, our study offers a novel approach by directly comparing the performance of multiple LLMs, thus providing insight into how different architectures perform on the same dataset.
JMIR Form Res 2025;9:e58366
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Our LLM-based model inherently provides ease of use in other LLM-based solutions for the medical domain enabling clinical automation.
Our generative pretrained transformer (GPT)-based LLM beats the BERT-based Che Xbert model on many pathologies and with a far bigger context length can handle long reports as compared with Che Xbert. Our model outperforms the previous labelers [8] for many pathologies on an external dataset, MIMIC-CXR [9].
JMIR Med Inform 2025;13:e68618
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Therefore, to leverage the benefits of LLMs in mental health care while avoiding the numerous risks, it is crucial to develop robust systems for restricting the scope of LLM-powered chatbots to the supplementary roles in which they excel and ensuring that they do not drift into taking on superficially similar roles.
Prompting is a technique often used to direct chatbots toward producing more accurate and relevant responses without having to collect new training data and retrain the LLM [10].
JMIR AI 2025;4:e69820
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This task poses a different challenge to an LLM than medical board examinations in the English language [12,13], as the performance of such models in other languages and in combination with more recent GPT versions and available plugins has not been explored. In the medical field, where mistakes can have severe consequences, assessing the amount of uncertainty is of paramount importance [14].
JMIR Med Educ 2025;11:e58375
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Using AI to Translate and Simplify Spanish Orthopedic Medical Text: Instrument Validation Study
llm
JMIR AI 2025;4:e70222
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We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by Chat GPT-4, a common LLM, as well as Menta LLa MA, an LLM fine-tuned for the mental health domain, exemplified by their application in the context of ED symptomatology and health-related quality of life (HRQo L) of patients with AN or BN.
JMIR Ment Health 2025;12:e57986
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