The Quiet Replacement of Care

AI-supported feature uses BioBaseline™ physiological trend modeling to support personalized relaxation and sleep preparation experiences

AI technology helps find Cancer and make the solution simpler rather than complex. The models of AI regarding screening helps find cancer.

Should AI be used in hospitals? If so how? First of all, AI can be used for patient check ins and out and name calling and turns. Basically, micromanaging each patient stay at the hospital.

The GRADES Institute from Paris-Saclay University and the Health Law Research Centre of Lund University within the project Euglohria and AIcare invite you to a seminar on the topic:   The European Hea...

The GRADES Institute from Paris-Saclay University and the Health Law Research Centre of Lund University within the project Euglohria and AIcare invite you to a seminar on the topic:   The European Hea...

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Motorola has rolled out a nationwide monthly service camp offering free labor, doorstep pickup & drop for premium devices and AI-driven support tools.

The Data-Driven Hospital and the Inevitability of AI. Healthcare industry has been sluggish in adopting this rapidly advancing technology.

for AI-based Epilepsy Management Platform SK Biopharmaceuticals forms a joint venture, Mentis Care, to develop an integrated epilepsy care ecosystem through AI-driven platform commercialization. SEOUL, South Korea, Oct. 21, 2025 /PRNewswire/ — SK Biopharmaceuticals, Co., Ltd., a biotech company focusing on research, development, and commercialization of treatments for disorders of the central nervous system (CNS) […] The post SK Biopharmaceuticals Launches Joint Venture with Eurofarma first appeared on AsiaNewsToday.

aiCare is an intelligent health management APP that helps users monitor heart rate, blood oxygen, body temperature and other health data in real time through wearable devices, provides personalized…

Background: Clinicians are the interface between artificial intelligence (#AI) (AI) applications and patient care. To maximize benefits and minimize risks of AI, clinicians must be “AI-ready”—that is, willing and able to understand, evaluate, and appropriately use AI tools in practice. Prior literature suggests that clinicians lack fundamental competencies in the use of AI. These gaps could be especially problematic in primary care, given its broad reach into patient care. We surveyed primary care providers (PCPs) in the United States’ largest integrated health care system, relatively early in its widespread implementation of clinical AI for frontline use, in order to identify readiness gaps that may warrant particular attention as part of a comprehensive AI implementation strategy. Objective: The aim of this study was to characterize PCPs’ use, knowledge, attitudes, and training priorities related to AI in order to inform health system AI implementation efforts. Methods: We conducted a national cross-sectional survey of United States Veterans Health Administration (VA) PCPs in October 2025, assessing AI use, self-reported knowledge, attitudes, and training experience. Descriptive analyses summarized responses with exploratory bivariate comparisons across clinician subgroups. Conventional content analysis with inductive coding was used to characterize open-ended responses providing a definition of AI. Results: Among 170 respondents (170/989, 17.2% response rate), 66.5% (113/170) reported current AI use, most commonly generative AI and decision support tools. Overall attitudes toward AI were positive, with 70.6% (120/170) mostly enthusiastic or more enthusiastic than apprehensive. Confidence in understanding sources of AI bias (62/170, 36.5%) and ethical issues (81/170, 47.6%) was limited. When asked to define AI, very few respondents provided an accurate technical definition. Key concerns about use of AI included accountability, accuracy, and transparency. Though 88.2% (150/170) identified AI training as a priority, only 26.5% (45/170) had any training. Training experiences ranged widely in source, focus, and structure. Conclusions: PCPs are eager to harness AI’s practical advantages but lack foundational competencies to do so in ways that maximize benefit and minimize risk. Our findings highlight a need for targeted education that prioritizes critical appraisal, workflow integration, and risk mitigation, supported by governance that addresses clinicians’ concerns and validated measures to evaluate progress toward an AI-ready workforce. These steps can empower PCPs to leverage AI safely and effectively and strengthen the quality and safety of primary care delivery at scale.