Artificial intelligence as an emerging diagnostic approach in paediatric pulmonology

Ferrante, Giuliana; Licari, Amelia; Marseglia, Gian Luigi; Grutta, Stefania La

doi:10.1111/resp.13842

Cited by 7 publications

(3 citation statements)

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“…AI has established a widespread presence within medicine [316,317]. For instance, radiologists have harnessed AI to assist with interpretation of medical images [16,318,319], cardiologists use AI to diagnose and monitor patients with heart disease [320][321][322], gastroenterologists leverage AI to enhance the effectiveness of their interventions [323][324][325] and pulmonologists apply AI algorithms to optimize their diagnoses [326][327][328]. The AI Silecosystem has demonstrated tremendous value in oncology.…”

Section: The Academic Origins and Catalysis Of The Ai Silecosystemmentioning

confidence: 99%

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell

2023

JCM

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Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

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Section: The Academic Origins and Catalysis Of The Ai Silecosystemmentioning

confidence: 99%

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell

2023

JCM

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“…Physicians will likely work closely alongside AI‐guided computers in the near future. By discovering patterns in data obtained from different diagnostic tests, AI will assist clinicians in the work‐up of an individual patient, improving clinical management in a revolutionary fashion 48 . In this scenario, the contribution of healthcare professionals working in multidisciplinary teams will be fundamental.…”

Section: Perspectives and Conclusionmentioning

confidence: 99%

“…By discovering patterns in data obtained from different diagnostic tests, AI will assist clinicians in the work-up of an individual patient, improving clinical management in a revolutionary fashion. 48 In this scenario, the contribution of healthcare professionals working in multidisciplinary teams will be fundamental. Indeed, the application of AI algorithms involves the collection and the analysis of large amounts of data, which have to be adequately interpreted within a specific clinical context.…”

Section: Co N Fli C T O F I Nte R E S Tmentioning

confidence: 99%

Artificial intelligence in the diagnosis of pediatric allergic diseases

Ferrante

Licari

Fasola

et al. 2020

Pediatric Allergy Immunology

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Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.

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