Diagnostic tests which can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been therefore paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. Here, we follow other recent proposals to further refine this concept, by using machine learning to assess the situational risk under which a diagnostic test is applied to augment its interpretation . We develop this to predict the occurrence of breakdowns of cattle herds due to bovine tuberculosis, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected by the skin test, improves by over 16 percentage points. While many risk factors are associated with increased risk of becoming infected, of note are several factors which suggest that, in some herds there is a higher risk of infection going undetected, including effects that are correlated to the veterinary practice conducting the test, and number of livestock moved off the herd.

Through AI platforms, computers can learn how to mimic human thought processes and cognitive functions, but with increased speed and learning capacity. Artificial intelligence has several subdisciplines, including machine learning, unsupervised learning, and deep learning. The versatility of AI makes it useful in a variety of medical disciplines. In human medicine, AI has already found applications in areas such as drug design, anesthesiology, cardiology, radiology, oncology, and infectious disease management. In general veterinary practice, protocols for vaccination, parasite prevention, and many aspects of wellness care are well-established.