Tuberculosis control, and the where and why of artificial intelligence.
Riddhi P DoshiDennis FalzonBruce V ThomasZelalem TemesgenLal SadasivanGiovanni Battista MiglioriMario RaviglionePublished in: ERJ open research (2017)
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.
Keyphrases
- artificial intelligence
- healthcare
- machine learning
- mycobacterium tuberculosis
- big data
- deep learning
- public health
- working memory
- mental health
- pulmonary tuberculosis
- palliative care
- physical activity
- adverse drug
- hiv aids
- primary care
- health information
- case report
- emergency department
- risk factors
- data analysis
- study protocol
- climate change
- human immunodeficiency virus