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McClean, M. ; Panciu, T.C.* ; Lange, C.* ; Duarte, R.G.* ; Theis, F.J.

Artificial intelligence in tuberculosis: A new ally in disease control.

Breathe 20:240056 (2024)
Verlagsversion DOI PMC
Free journal
Creative Commons Lizenzvertrag
The challenges to effective tuberculosis (TB) disease control are considerable, and the current global targets for reductions in disease burden seem unattainable. The combination of complex pathophysiology and technical limitations results in difficulties in achieving consistent, reliable diagnoses, and long treatment regimens imply serious physiological and socioeconomic consequences for patients. Artificial intelligence (AI) applications in healthcare have significantly improved patient care regarding diagnostics, treatment and basic research. However, their success relies on infrastructures prioritising comprehensive data generation and collaborative research environments to foster stakeholder engagement. This viewpoint article briefly outlines the current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Diagnosis; Impact
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1810-6838
e-ISSN 2073-4735
Zeitschrift Breathe
Quellenangaben Band: 20, Heft: 3, Seiten: , Artikelnummer: 240056 Supplement: ,
Verlag Maney Publishing
Verlagsort Leeds
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen
DZIF
German Federal Ministry of Education and Research
Ludwig-Maximilians-Universitaet Muenchen (LMU)
Deutsches Zentrum fuer Infektionsforschung e. V. (DZIF)
EFPIA
European Union
Innovative Medicines Initiative 2 Joint Undertaking (JU)
DOI 10.1183/20734735.0056-2024
WOS ID 001410617500004
Scopus ID 85213064474
PubMed ID 39660086
Erfassungsdatum 2024-12-12
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