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AD/ADRD: Alzheimer disease/Alzheimer disease and related dementias; AI: artificial intelligence; BCI: brain-computer interface; ML: machine learning;
Our research approach centered on a comprehensive evaluation of the literature exploring the integration of AI—particularly its subset, ML—within BCI closed-loop systems in health care. The goal was to synthesize current knowledge on the methodologies, algorithms, outcomes, limitations, and emerging directions that define this interdisciplinary field.
JMIR Biomed Eng 2025;10:e72218
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AI in Health Care Service Quality: Systematic Review
Therefore, unbiased data are necessary for training AI systems; addressing these concerns can ensure a more informed and prepared approach to integrating AI in health care [7]. Second, AI can pose a security risk if not designed and implemented securely. Malicious actors can exploit AI systems, leading to unauthorized data access or other harmful consequences.
JMIR AI 2025;4:e69209
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Therefore, using artificial intelligence (AI), specifically training a large language model (LLM), has emerged as a valuable tool to automate this analysis, making it comprehensive, scalable, and efficient.
In this study, we aimed to use natural language processing (NLP) and an LLM to quantify the available online patient-level information regarding guideline-recommended KTR selection criteria reported by US kidney transplant centers.
JMIR AI 2025;4:e74066
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“It’s Not Only Attention We Need”: Systematic Review of Large Language Models in Mental Health Care
In response, researchers have explored countermeasures, such as techniques to flag potential hallucinations [41] or explainable AI (XAI) methods [39,42-44] to portray a realistic picture of what AI can and cannot do. Additionally, beyond technical solutions, fostering AI literacy among users is equally important. A higher level of AI literacy enhances comprehension of LLM behavior and promotes more effective human-AI collaboration [45,46].
JMIR Ment Health 2025;12:e78410
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As m Health continues to advance, artificial intelligence (AI) innovations are being increasingly integrated into the health care sector to enhance the quality of care and address various health areas [53,54]. AI has been used in diagnostic support, as well as to enhance access to virtual health services in remote areas and address knowledge and skill gaps [55].
JMIR Mhealth Uhealth 2025;13:e65599
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Therefore, the following research question (RQ) is proposed:
RQ1: How is communication competence operationalized in artificial intelligence (AI) CAs for health?
When examining the impacts of CAs within the health care context, user experience, psychological outcomes, and health outcomes are primary evaluation dimensions [5,6,10].
J Med Internet Res 2025;27:e76296
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To address these health issues, there is a growing interest in leveraging artificial intelligence (AI) and digital health technologies (eg, mobile apps and smartwatches) to facilitate home-based hypertension management. Such technologies offer long-term blood pressure monitoring, send reminders for the medication intake, provide educational content to support lifestyle changes, and provide personalized health advice.
JMIR Mhealth Uhealth 2025;13:e68361
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This will likely become an even greater concern with the rise of “black box” products, such as those incorporating artificial intelligence (AI).
Latent failures are flaws that remain dormant until specific conditions trigger them, potentially leading to catastrophic harm that can scale exponentially [29].
J Med Internet Res 2025;27:e80076
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Each method has distinct advantages, rendering them especially suitable for our research objectives, as elaborated upon in the following sections.
wav2vec 2.0: It is developed by Facebook AI Research, uses a multilayer convolutional neural network (CNN) for audio encoding, and is supplemented by latent representation masking and contextualization through a Transformer network trained with contrastive learning methods [49].
JMIR Med Inform 2025;13:e66907
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