ArticleCureus2024
The Role of Artificial Intelligence in Combatting Respiratory Tract Infections.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Diagnostic tools in respiratory medicine (Review).Biomedical reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory tract infections (RTIs) such as pneumonia, bronchitis, and COVID-19 are significant global health concerns due to their high morbidity and mortality rates. The advent of artificial intelligence (AI) offers innovative solutions across various aspects of RTI management, including diagnosis, prediction, treatment, and prevention. AI algorithms enhance diagnostic accuracy by analyzing extensive data from electronic health records and imaging studies, often surpassing human radiologists in identifying diseases such as pneumonia. For instance, AI-based image recognition tools have demonstrated remarkable precision in detecting pneumonia from chest X-rays. Additionally, AI models can predict disease outbreaks and optimize public health responses, as exemplified during the COVID-19 pandemic where AI predicted infection hotspots and evaluated the effectiveness of containment measures. In personalized medicine, AI tailors treatments based on individual patient profiles, thereby improving therapeutic outcomes and accelerating drug discovery. Wearable AI devices facilitate early detection and prevention of RTIs through continuous health monitoring. Despite its transformative potential, AI implementation in healthcare faces challenges, including data privacy, algorithm transparency, and ethical concerns. Addressing these issues necessitates collaboration among technologists, healthcare providers, and policymakers to ensure responsible and equitable integration of AI technologies. This editorial underscores the transformative potential of AI in managing RTIs and calls for robust frameworks to harness AI's benefits while safeguarding patient rights.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.