e.g. mhealth
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Skip search results from other journals and go to results- 12 Journal of Medical Internet Research
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Clinical decision support systems (CDSS) have been implemented to aid practitioners in this duty [1-3]. Clinical practice guidelines (CPG) serve as the prototype for CDSS. They are published and updated at varying frequencies by scientific societies and policy makers, covering virtually every medical field or disorder. Over time, the number and complexity of CPG have increased, resulting in more detailed and robust recommendations.
JMIR Med Educ 2025;11:e55709
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Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review
AI in medicine can take a number of forms and fulfill a number of tasks, ranging from risk prediction or diagnosis and screening to AI-powered clinical decision support systems (CDSS) [1]. AI systems have also been introduced across a range of medical specialties, including oncology, pulmonology, and radiology [2].
Physician autonomy has been found to play a role in physician acceptance and adoption of medical technologies [3], and in particular, AI [1].
JMIR AI 2025;4:e59295
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The pilot version of the AI-based CDSS was evaluated in a 2-stage semistructured qualitative process [32]. With regard to feasibility, physicians were each shown 5 exemplary sepsis patient cases in a desktop version of the CDSS. Cases were selected randomly from the evaluation dataset and were identical for each interviewee. In addition to basic patient information, this contained relevant vital parameters, and the treatment recommendation determined by the AI-based CDSS (Figure 1).
JMIR Hum Factors 2025;12:e66699
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The primary objective of CDSS is to optimize clinical decisions by integrating clinical observations with knowledge bases, ultimately improving patient outcomes [14]. In recent years, CDSS has been preliminarily applied in some specific medical scenarios, such as chronic obstructive pulmonary disease patient management, antibiotic management, and venous thromboembolism management [15-17].
JMIR Med Inform 2025;13:e63186
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The introduction of an automated clinical decision support system (CDSS) that guides physicians to order the most appropriate test(s) for their patients while simultaneously providing both medical necessity requirements and applicable diagnostic codes will be a vital tool to improve test ordering and reimbursement efficiency.
Interact J Med Res 2025;14:e46007
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Clinical decision support systems (CDSS) are tools that use medical knowledge and health information to aid clinicians’ decision-making to provide enhanced patient care [1,2]. CDSS can be classified into 2 types: knowledge-based and non–knowledge-based [2]. In knowledge-based CDSS, relevant information is evaluated by a set of IF-THEN rules, and recommendations are generated.
J Med Internet Res 2025;27:e59946
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AI-based CDSS could be particularly useful in sepsis care due to the high heterogeneity and complexity of the disease [10]. Non–knowledge-based respectively data-based CDSS are subject to a trade-off between model complexity and interpretability. As sepsis is an extremely complex condition, a majority of machine learning–based CDSS for this disease can be considered “black box” systems.
JMIR Res Protoc 2025;14:e62704
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Main categories (1-3), primary codes, and subcodes emerging from inductive analysis of the interviews.
a The total sum of quotes belonging to the subcodes.
b AI-CDSS: artificial intelligence–supported clinical decision support system.
c In the following section, wishes for and chances of an AI-CDSS are presented together due to their overlapping subcodes.
d HIS: hospital information system.
JMIR Aging 2024;7:e57899
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Health Care Professionals’ Experience of Using AI: Systematic Review With Narrative Synthesis
Examples of nonspecific codes include problems with internet connection, because this will cause a problem with any digital system requiring internet connection (eg, video calls); challenges with using digital systems, because it can surface where insufficient training has been given [16]; alert fatigue, because it is a recognized problem for any CDSS [17]; and the importance of ensuring that systems do not adversely affect workflows, because it is a recognized issue for the design of digital interventions [
J Med Internet Res 2024;26:e55766
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Overall, the axes integrate the organization or environment in which the CDSS is implemented, the individual behaviors related to the prescriber or patient, and the CDSS itself and its functionalities. Within these broad axes, there are numerous factors, such as practitioner acceptance of new technology, integration of the CDSS into workflows, and access to technical support [21-23]. These factors guide the analysis of potential barriers to CDSS implementation.
J Med Internet Res 2024;26:e45122
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