ReviewERJ open research2017
Tuberculosis control, and the where and why of artificial intelligence.
Review in ERJ open research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled 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.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drug-associated adverse events in the treatment of multidrug-resistant tuberculosis: an individual patient data meta-analysis.The Lancet. Respiratory medicine · 2020Pooled it
- Latent Tuberculosis and Associated Factors Among Adults Living With HIV in Kinshasa, Democratic Republic of the Congo: A Cross-Sectional Study.Public health challenges · 2026Article
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis.Diagnostics (Basel, Switzerland) · 2023Article
- A qualitative exploration of tuberculosis patients who were lost to follow-up in Malaysia.PloS one · 2023Article
- A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases.Tropical medicine and infectious disease · 2022Review
- Effect Evaluation of Artificial Intelligence-Based Electronic Health PDCA Nursing Model in the Treatment of Mycoplasma Pneumonia in Children.Journal of healthcare engineering · 2022Article
- Scaling up artificial intelligence to curb infectious diseases in Africa.Frontiers in digital health · 2022Article
- Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.Journal of public health research · 2021Article
- Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study.BMC medical education · 2021Article
- Article
- A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- The Tuberculosis-Depression Syndemic and Evolution of Pharmaceutical Therapeutics: From Ancient Times to the Future.Frontiers in psychiatry · 2021Review
- Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review.Journal of medical Internet research · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.
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.