{
  "abstract": "Artificial intelligence (AI) presents an opportunity to transform neonatal care. By rapidly assimilating large volumes of complex, multi-dimensional data to produce real-time predictions, machine learning (ML) models have the potential to enable personalised monitoring and treatment and earlier intervention. This narrative review aims to provide clinicians with a foundational overview of AI and its potential role in the care of preterm infants, with a particular focus on respiratory pathologies. We introduce different types of AI tools and ways to assess predictive model performance. We summarise existing literature using AI and conventional predictive models to predict respiratory conditions, including extubation readiness, bronchopulmonary dysplasia, and apnoea occurrence and risk. Finally, we discuss current limitations of AI in neonatology, barriers to clinical translation and the next steps needed to overcome them. These may include multicentre collaboration and data sharing, using dynamic inputs such as vital signs to reflect evolving physiology, and improved transparency, accessibility and training for clinicians, as well as clear guidelines for clinical application, accountability and ongoing monitoring.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Paediatrics, University of Oxford, Oxford, United Kingdom."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Vithushanan Ketheeswaranathan"
    },
    {
      "affs": [
        0
      ],
      "name": "Surina Fordington"
    },
    {
      "affs": [
        0
      ],
      "name": "Caroline Hartley"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5xm45eb5fjdyx.bib",
  "cited_by": 0,
  "doi": "10.3389/fped.2026.1885589",
  "fulltext_url": "https://europepmc.org/articles/PMC13711111",
  "id": "5xm45eb5fjdyx",
  "issns": [
    "2296-2360"
  ],
  "landing_url": "https://europepmc.org/article/MED/42948079",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Review"
    ],
    [
      "openalex",
      "2740|Neonatal Respiratory Health Research"
    ]
  ],
  "openalex": "W7221688120",
  "pdf_url": "https://europepmc.org/articles/PMC13711111?pdf=render",
  "pmcid": "PMC13711111",
  "pmid": "42948079",
  "published": "2026-10-13",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/5xm45eb5fjdyx.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:34Z",
      "native_id": "MED:42948079",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42948079"
    },
    {
      "first_seen": "2026-10-31T12:21:56Z",
      "native_id": "PMC13711111",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fped.2026.1885589"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2740|Neonatal Respiratory Health Research",
      "subfield": "general"
    }
  ],
  "title": "How could artificial intelligence aid clinicians caring for preterm neonates with respiratory pathology?",
  "title_text": "How could artificial intelligence aid clinicians caring for preterm neonates with respiratory pathology?",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:56Z",
  "url": "https://research.draconitic.com/paper/5xm45eb5fjdyx",
  "venue": "Frontiers in pediatrics",
  "versions": []
}