e.g. mhealth
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The Right to Explanation in AI: In a Lonely Place
This has brought about a variety of methods used to explain automation, resulting in further ambiguities in what constitutes an explanation [34].
Surprisingly, the elements of a good explanation are not well defined. Instead, many guidelines exist outlining what an explanation should contain. As a result of this varying guidance, there are different ways to categorize explanations.
J Med Internet Res 2025;27:e64482
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Iterative adaptations to m Paris in response to stochastic environmental and technical implementation barriers resulted in the formation of a robust m Health system that is locally hosted in a low-resource setting, but readily extensible to other geographic and topical contexts, including high-resource settings.
m Paris’ open-source nature, auditability, and ability to autonomously execute algorithms in the face of diverse challenges suggest favorable prospects for the automation of critical health communication
JMIR Form Res 2025;9:e65150
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Automation may offer benefits in standardization, efficiency, effectiveness, cost, confidentiality, and access. Social desirability response bias is associated with higher levels of treatment nonadherence [25] and reduces the accuracy of clinical history taking [26] in human-human interactions. In educational settings, certain interactions, such as quizzing, enhance information retention but may be more socially appropriate from a “digital clinician” than from a health care professional.
JMIR Diabetes 2025;10:e63503
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AI commonly refers to the interdisciplinary study and development of models engineered to perform varied levels of automation that would typically require human intelligence [16,17]. Machine learning is a subset of AI that involves algorithms that autonomously learn patterns from the data they are trained on without being explicitly programmed [16].
J Med Internet Res 2025;27:e69700
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Artificial intelligence (AI) automation in low- to mid-level tasks like patient education and initial therapy screening emerges as a strategic response to mitigate this shortage, reallocating medical staff to higher-priority tasks [2,3].
The advent of advanced multimodal large language models (LLMs) such as GPT-4 introduces a paradigm shift, promising scalable, cost-effective chatbot solutions, which are particularly helpful for tasks that require the provider to interact with the patient [4].
JMIR Nursing 2025;8:e63058
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Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation
In this study, we present a model for the automation of repetitive follow-up of patients with amiodarone treatment. First, we constructed an automated solution including the whole surveillance process, using a robotic process automation (RPA) software tool. We then compared the RPA to standard manual amiodarone follow-up as presently practiced.
J Med Internet Res 2025;27:e65473
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Trust in automation, defined as “the attitude that an agent will help achieve an individual’s goals in situations characterized by uncertainty and vulnerability” [26], is one of the most crucial factors determining the use of automation [27,28]. There is a growing body of research examining people’s trust in autonomous and robotic technologies in various domains, including transportation [29-31], health care [32,33], education [34], and defense [35,36].
JMIR Hum Factors 2025;12:e60273
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