AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Overall, AI can support proactive, person-centred kidney care only if clinicians help design, govern, evaluate, and critically supervise these emerging technologies within robust, learning healthcare systems.
Artificial intelligence (AI) is moving rapidly from experimental prediction models toward workflow-integrated tools that can support clinical care, education, research, and operational efficiency. Kidney care is particularly well suited to AI because it is data-rich, longitudinal, and multidisciplinary, incorporating l...
Introduction: The digital transformation of healthcare services has expanded the use of clinical decision support systems as tools capable of integrating clinical information and assisting healthcare professionals in evidence-based decision-making. In nephrology, the complexity of managing patients with chronic kidney...
Abstract Artificial intelligence (AI) is reshaping kidney care by integrating electronic health records (EHRs), imaging, digital pathology, genomics, wearable biosensors, and longitudinal physiologic data into dynamic risk-prediction systems. Traditional nephrology risk stratification has relied on static variables and...
Digital health and artificial intelligence (AI) are increasingly part of paediatric respiratory clinics, from home-based symptom monitoring and remote consultations to AI-supported test interpretation and large language model (LLM)-supported communication. These tools promise to capture real-world disease and treatment...
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