Evidence map›Paper›PMID 39598056›Full record

ReviewJournal of clinical medicine2024

Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.

Osvaldo Graña-Castro, Elena Izquierdo, Antonio Piñas-Mesa, Ernestina Menasalvas, Tomás Chivato-Pérez

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Osvaldo Graña-CastroDepartamento de Ciencias Médicas Básicas, Instituto de Medicina Molecular Aplicada (IMMA-Nemesio Díez), Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, 28925 Alcorcón, Spain.ORCID 0000-0003-1615-4242
Elena IzquierdoDepartamento de Ciencias Médicas Básicas, Instituto de Medicina Molecular Aplicada (IMMA-Nemesio Díez), Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, 28925 Alcorcón, Spain.
Antonio Piñas-MesaDepartamento de Humanidades-Sección de Pensamiento Facultad de Humanidades y Ciencias de la Comunicación, Universidad San Pablo-CEU, CEU Universities, 28003 Madrid, Spain.ORCID 0000-0002-3641-2651
Ernestina MenasalvasETSI Informáticos, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo, Spain.ORCID 0000-0002-5615-6798
Tomás Chivato-PérezDepartamento de Ciencias Médicas Básicas, Instituto de Medicina Molecular Aplicada (IMMA-Nemesio Díez), Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, 28925 Alcorcón, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic respiratory diseases (CRDs), including asthma and chronic obstructive pulmonary disease (COPD), represent significant global health challenges, contributing to substantial morbidity and mortality. As the prevalence of CRDs continues to rise, particularly in low-income countries, there is a pressing need for more efficient and personalized approaches to diagnosis and treatment. This article explores the impact of emerging technologies, particularly artificial intelligence (AI), on the management of CRDs. AI applications, including machine learning (ML), deep learning (DL), and large language models (LLMs), are transforming the landscape of CRD care, enabling earlier diagnosis, personalized treatment, and enhanced remote patient monitoring. The integration of AI with telehealth and wearable technologies further supports proactive interventions and improved patient outcomes. However, challenges remain, including issues related to data quality, algorithmic bias, and ethical concerns such as patient privacy and AI transparency. This paper evaluates the effectiveness, accessibility, and ethical implications of AI-driven tools in CRD management, offering insights into their potential to shape the future of respiratory healthcare. The integration of AI and advanced technologies in managing CRDs like COPD and asthma holds substantial potential for enhancing early diagnosis, personalized treatment, and remote monitoring, though challenges remain regarding data quality, ethical considerations, and regulatory oversight.

Indexed as

artificial intelligence (AI)asthmachronic obstructive pulmonary disease (COPD)chronic respiratory diseases (CRDs)deep learning (DL)ethical considerations in AIlarge language models (LLMs)machine learning (ML)remote patient monitoring (RPM)telehealth

Identifiers

PMID39598056
PMCPMC11594345

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.