ReviewCurrent allergy and asthma reports2023
Computer-Assisted Clinical Diagnosis and Treatment.
Review in Current allergy and asthma reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Multidimensional feature fusion in longitudinal physical examination data: a machine learning framework for classification of type 2 diabetes.Frontiers in public health · 2026Article
- A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images.Diagnostics (Basel, Switzerland) · 2024Article
- Reviewing the Potential Role of Artificial Intelligence in Delivering Personalized and Interactive Pain Medicine Education for Chronic Pain Patients.Journal of pain research · 2024Review
- Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study.Frontiers in digital health · 2024Article
- Deep learning-based multi-class classification of thyroid disorders on Tc-99m scintigraphy using modified DenseNet-201.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewComputer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes. RECENT
findingsCAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.
Indexed as
Identifiers
37351722What 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.