Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Abstract

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

How paediatric nursing can leverage the age of artificial intelligence to improve health outcomes and quality of care?

“…KATE) as these are some AI tools that aid clinical data engine providing real-time analytics to deliver nursing services to patients. 2,3 Every patient is different and presents with different symptoms; however, a variety of factors, including the knowledge and experience with psychological and emotional states of all healthcare providers, including nurses, can inevitably result in such errors. One such machine-learning technique is the multilayer perceptron method which can handle complex properties of various data types.…”

“…But AI has been integrated into some parts of the healthcare industry including—AI‐assisted auscultation, Cardiac Auscultation Record Card, Automated image‐based bilirubin and a patient triage system for the emergency room (e.g. KATE) as these are some AI tools that aid clinical data engine providing real‐time analytics to deliver nursing services to patients 2,3 …”

Development and Validation of a Deep Learning System for the Diagnosis of Pediatric Diseases: A Large-Scale Real-World Data Study in Shanghai

“…2 As we learn more about pediatric diseases, we learn more about the power of artificial intelligence (AI) tools that can be used in unprecedented ways. AI-assisted diagnosis is considered to be an important means to summarize complex diagnostic mechanisms, improve the efficiency and accuracy of pediatric clinical diagnostics, 3 and provide a solution to the imbalance between the supply and demand of pediatricians and patients sequentially. The deep learning method, as a subgroup of AI, is a particularly promising method that automatically learns entity representations from natural language and has been shown to match and even outperform human performance in task-specific applications.…”