PT  - JOURNAL ARTICLE
AU  - Riddhi Doshi
AU  - Dennis Falzon
AU  - Bruce V. Thomas
AU  - Zelalem Temesgen
AU  - Lal Sadasivan
AU  - Giovanni Battista Migliori
AU  - Mario Raviglione
TI  - Tuberculosis control, and the where and why of artificial intelligence
AID  - 10.1183/23120541.00056-2017
DP  - 2017 Apr 01
TA  - ERJ Open Research
PG  - 00056-2017
VI  - 3
IP  - 2
4099  - http://openres.ersjournals.com/content/3/2/00056-2017.short
4100  - http://openres.ersjournals.com/content/3/2/00056-2017.full
SO  - erjor2017 Apr 01; 3
AB  - 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.Tuberculosis control, and the where and why of artificial intelligence http://ow.ly/GwuY30bCXkJ