{
  "abstract": "Lung function evaluation is rapidly evolving beyond laboratory testing. While conventional tests remain the clinical reference standard, digital health technologies now enable respiratory assessment at home and in real-world settings through home-based systems, portable devices, and wearables. These tools can support repeated or continuous monitoring of traditional and digital biomarkers of respiratory health and disease. However, they differ in physiological detail, usability, degree of patient involvement and clinical maturity. In parallel, artificial intelligence (AI) is becoming an important complement to these technologies. Machine learning and deep learning can support signal processing, quality control and interpretation of pulmonary function tests, while also extracting clinically relevant information from multimodal respiratory data collected outside controlled environments. Generative AI has a more emerging role, mainly in patient communication and educational support. For pulmonary function test interpretation, current evidence supports clinician-AI collaboration as the most realistic near-term model, rather than fully autonomous decision-making. This review summarises the main technologies currently available for lung function evaluation in adults and discusses the current and future role of AI in respiratory monitoring, interpretation and personalised care.",
  "abstract_shown": true,
  "affiliations": [
    "Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Alessandra Angelucci",
      "orcid": "0000-0001-8266-9346"
    },
    {
      "affs": [
        0
      ],
      "name": "Andrea Aliverti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c4cft0f2z90n2.bib",
  "cited_by": 0,
  "doi": "10.1183/20734735.0047-2026",
  "fulltext_url": "https://europepmc.org/articles/PMC13750359",
  "id": "c4cft0f2z90n2",
  "issns": [
    "1810-6838",
    "2073-4735"
  ],
  "landing_url": "https://europepmc.org/article/MED/43011908",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2740|Chronic Obstructive Pulmonary Disease (COPD) Research"
    ]
  ],
  "openalex": "W7225725809",
  "pdf_url": "https://europepmc.org/articles/PMC13750359?pdf=render",
  "pmcid": "PMC13750359",
  "pmid": "43011908",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c4cft0f2z90n2.ris",
  "sources": [
    {
      "first_seen": "2026-11-12T15:25:13Z",
      "native_id": "MED:43011908",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/43011908"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2740|Chronic Obstructive Pulmonary Disease (COPD) Research",
      "subfield": "general"
    }
  ],
  "title": "The digital health ecosystem in respiratory medicine: from traditional testing to real-world, artificial intelligence-powered monitoring and personalised care.",
  "title_text": "The digital health ecosystem in respiratory medicine: from traditional testing to real-world, artificial intelligence-powered monitoring and personalised care.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-13T03:21:10Z",
  "url": "https://research.draconitic.com/paper/c4cft0f2z90n2",
  "venue": "Breathe (Sheffield, England)",
  "versions": []
}